Atanas Kiryakov's, Ontotext’s CEO, presentation at the first edition of Graphorum (http://graphorum2017.dataversity.net/) – a new forum that taps into the growing interest in Graph Databases and Technologies. Graphorum is co-located with the Smart Data Conference, organized by the digital publishing platform Dataversity.
The presentation demonstrates the capabilities of Ontotext’s own approach to contributing to the discipline of more intelligent information gathering and analysis by:
- graphically explorinh the connectivity patterns in big datasets;
- building new links between identical entities residing in different data silos;
- getting insights of what type of queries can be run against various linked data sets;
- reliably filtering information based on relationships, e.g., between people and organizations, in the news;
- demonstrating the conversion of tabular data into RDF.
Learn more at http://ontotext.com/.
The Bounties of Semantic Data Integration for the Enterprise Ontotext
Semantic data integration allows enterprises to connect heterogeneous data sources through a common language. This creates a unified 360-degree view of enterprise data and facilitates knowledge management and use. Semantic integration aims to enrich existing data with external knowledge and provide a single access point for enterprise assets. It addresses challenges of accessing and storing data from various internal resources by building a well-structured integrated whole to enhance business processes.
Transforming Your Data with GraphDB: GraphDB Fundamentals, Jan 2018Ontotext
These are slides from a live webinar taken place January 2018.
GraphDB™ Fundamentals builds the basis for working with graph databases that utilize the W3C standards, and particularly GraphDB™. In this webinar, we demonstrated how to install and set-up GraphDB™ 8.4 and how you can generate your first RDF dataset. We also showed how to quickly integrate complex and highly interconnected data using RDF and SPARQL and much more.
With the help of GraphDB™, you can start smartly managing your data assets, visually represent your data model and get insights from them.
[Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry ...Ontotext
This webinar continues series are demonstrating how linked open data and semantic tagging of news can be used for comprehensive media monitoring, market and business intelligence. The platform for the demonstrations is FactForge: a hub for news and data about people, organizations, and locations (POL). FactForge embodies a big knowledge graph (BKG) of more than 1 billion facts that allows various analytical queries, including tracing suspicious patterns of company control; media monitoring of people, including companies owned by them, their subsidiaries, etc.
First Steps in Semantic Data Modelling and Search & Analytics in the CloudOntotext
This webinar will break the roadblocks that prevent many from reaping the benefits of heavyweight Semantic Technology in small scale projects. We will show you how to build Semantic Search & Analytics proof of concepts by using managed services in the Cloud.
Knowledge graphs - it’s what all businesses now are on the lookout for. But what exactly is a knowledge graph and, more importantly, how do you get one? Do you get it as an out-of-the-box solution or do you have to build it (or have someone else build it for you)? With the help of our knowledge graph technology experts, we have created a step-by-step list of how to build a knowledge graph. It will properly expose and enforce the semantics of the semantic data model via inference, consistency checking and validation and thus offer organizations many more opportunities to transform and interlink data into coherent knowledge.
Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data LinkingOntotext
A presentation of Ontotext’s CEO Atanas Kiryakov, given during Semantics 2018 - an annual conference that brings together researchers and professionals from all over the world to share knowledge and expertise on semantic computing.
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
This document discusses graph databases and the graph database Neo4j. It provides an introduction to NoSQL databases and graph theory, including graph algorithms. It outlines some common uses of graph databases such as social networking, recommendations, and identity and access management. It also provides examples of Cypher queries that can be used with Neo4j to find and create nodes and relationships.
The Bounties of Semantic Data Integration for the Enterprise Ontotext
Semantic data integration allows enterprises to connect heterogeneous data sources through a common language. This creates a unified 360-degree view of enterprise data and facilitates knowledge management and use. Semantic integration aims to enrich existing data with external knowledge and provide a single access point for enterprise assets. It addresses challenges of accessing and storing data from various internal resources by building a well-structured integrated whole to enhance business processes.
Transforming Your Data with GraphDB: GraphDB Fundamentals, Jan 2018Ontotext
These are slides from a live webinar taken place January 2018.
GraphDB™ Fundamentals builds the basis for working with graph databases that utilize the W3C standards, and particularly GraphDB™. In this webinar, we demonstrated how to install and set-up GraphDB™ 8.4 and how you can generate your first RDF dataset. We also showed how to quickly integrate complex and highly interconnected data using RDF and SPARQL and much more.
With the help of GraphDB™, you can start smartly managing your data assets, visually represent your data model and get insights from them.
[Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry ...Ontotext
This webinar continues series are demonstrating how linked open data and semantic tagging of news can be used for comprehensive media monitoring, market and business intelligence. The platform for the demonstrations is FactForge: a hub for news and data about people, organizations, and locations (POL). FactForge embodies a big knowledge graph (BKG) of more than 1 billion facts that allows various analytical queries, including tracing suspicious patterns of company control; media monitoring of people, including companies owned by them, their subsidiaries, etc.
First Steps in Semantic Data Modelling and Search & Analytics in the CloudOntotext
This webinar will break the roadblocks that prevent many from reaping the benefits of heavyweight Semantic Technology in small scale projects. We will show you how to build Semantic Search & Analytics proof of concepts by using managed services in the Cloud.
Knowledge graphs - it’s what all businesses now are on the lookout for. But what exactly is a knowledge graph and, more importantly, how do you get one? Do you get it as an out-of-the-box solution or do you have to build it (or have someone else build it for you)? With the help of our knowledge graph technology experts, we have created a step-by-step list of how to build a knowledge graph. It will properly expose and enforce the semantics of the semantic data model via inference, consistency checking and validation and thus offer organizations many more opportunities to transform and interlink data into coherent knowledge.
Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data LinkingOntotext
A presentation of Ontotext’s CEO Atanas Kiryakov, given during Semantics 2018 - an annual conference that brings together researchers and professionals from all over the world to share knowledge and expertise on semantic computing.
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
This document discusses graph databases and the graph database Neo4j. It provides an introduction to NoSQL databases and graph theory, including graph algorithms. It outlines some common uses of graph databases such as social networking, recommendations, and identity and access management. It also provides examples of Cypher queries that can be used with Neo4j to find and create nodes and relationships.
Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven RecipesOntotext
This presentation will provide a brief introduction to logical reasoning and overview of the most popular semantic schema and ontology languages: RDFS and the profiles of OWL 2.
While automatic reasoning has always inspired the imagination, numerous projects have failed to deliver to the promises. The typical pitfalls related to ontologies and symbolic reasoning fall into two categories:
- Over-engineered ontologies. The selected ontology language and modeling patterns can be too expressive. This can make the results of inference hard to understand and verify, which in its turn makes KG hard to evolve and maintain. It can also impose performance penalties far greater than the benefits.
- Inappropriate reasoning support. There are many inference algorithms and implementation approaches, which work well with taxonomies and conceptual models of few thousands of concepts, but cannot cope with KG of millions of entities.
- Inappropriate data layer architecture. One such example is reasoning with virtual KG, which is often infeasible.
Linked Data Experiences at Springer NatureMichele Pasin
An overview of how we're using semantic technologies at Springer Nature, and an introduction to our latest product: www.scigraph.com
(Keynote given at http://2016.semantics.cc/, Leipzig, Sept 2016)
Diving in Panama Papers and Open Data to Discover Emerging NewsOntotext
Get guidance through the gigantic sea of freely released data from Panama Papers as well as Linked Open Data could. You will learn how it can empower you understanding of today’s news or any other information source.
Gain Super Powers in Data Science: Relationship Discovery Across Public DataOntotext
The document summarizes a webinar on relationship discovery across public data. It outlines the webinar agenda which includes use cases of relation discovery and media monitoring. It also describes examples of relationship discovery from datasets like the Panama Papers and media monitoring examples. It discusses linking news to knowledge graphs and semantic media monitoring. Finally, it covers mapping additional datasets to DBPedia to facilitate relationship discovery.
Build Narratives, Connect Artifacts: Linked Open Data for Cultural HeritageOntotext
Many issues are faced by scholars, book researchers, museum directors who try to find the underlying connection between resources. Scholars in particular continuously emphasizes the role of digital humanities and the value of linked data in cultural heritage information systems.
Enterprise knowledge graphs use semantic technologies like RDF, RDF Schema, and OWL to represent knowledge as a graph consisting of concepts, classes, properties, relationships, and entity descriptions. They address the "variety" aspect of big data by facilitating integration of heterogeneous data sources using a common data model. Key benefits include providing background knowledge for various applications and enabling intra-organizational data sharing through semantic integration. Challenges include ensuring data quality, coherence, and managing updates across the knowledge graph.
Choosing the Right Graph Database to Succeed in Your ProjectOntotext
The document discusses choosing the right graph database for projects. It describes Ontotext, a provider of graph database and semantic technology products. It outlines use cases for graph databases in areas like knowledge graphs, content management, and recommendations. The document then examines Ontotext's GraphDB semantic graph database product and how it can address key use cases. It provides guidance on choosing a GraphDB option based on project stage from learning to production.
Efficient Practices for Large Scale Text Mining ProcessOntotext
Text mining is a need when managing large scale textual collections. It facilitates access to, otherwise, hard to organise unstructured and heterogeneous documents, allows for extraction of hidden knowledge and opens new dimensions in data exploration.
In this webinar, Ivelina Nikolova, PhD, shares best practices and text analysis examples from successful text mining process in domains like news, financial and scientific publishing, pharma industry and cultural heritage.
[Conference] Cognitive Graph Analytics on Company Data and NewsOntotext
Ontotext introduced their cognitive analytics platform that performs cognitive graph analytics on company data and news. The platform builds large knowledge graphs by integrating data from multiple sources and uses text mining to link news articles to entities in the knowledge graph. It provides functionality for node ranking, similarity analysis and data cleaning to consolidate and reconcile company records across datasets. The platform was demonstrated through a knowledge graph containing over 2 billion facts built by integrating datasets like DBpedia, Geonames, and news article metadata.
How to Reveal Hidden Relationships in Data and Risk AnalyticsOntotext
Imagine risk analysis manager or compliance officer who can discover easily relationships like this: Big Bucks Café out of Seattle controls My Local Café in NYC through an offshore company. Such discovery can be a game changer if My Local Café pretends to be an independent small enterprise, while recently Big Bucks experiences financial difficulties.
The document discusses big data and linked data. It presents the three V's of big data - volume, velocity, and variety. It shows the semantic web layer cake and how linked data provides a lingua franca for data integration. It provides examples of using linked data for sensor data, supply chain data, and as a bridge between online and offline systems. Finally, it discusses adding a linked data layer to the existing internet architecture and engaging more stakeholders with the technology.
Linking Open, Big Data Using Semantic Web Technologies - An IntroductionRonald Ashri
The Physics Department of the University of Cagliari and the Linkalab Group invited me to talk about the Semantic Web and Linked Data - this is simply an introduction to the technologies involved.
Using the Semantic Web Stack to Make Big Data SmarterMatheus Mota
The document discusses using semantic web technologies to make big data smarter. It provides an overview of key concepts in semantic web, including linked data and ontologies. It describes how semantic web can add structure and meaning to unstructured data through modeling data as graphs and defining relationships and properties. The goal is to publish and query interconnected data at scale to enable new types of queries and inferences over big data.
1) The document compares different methods for representing statement-level metadata in RDF, including RDF reification, singleton properties, and RDF*.
2) It benchmarks the storage size and query execution time of representing biomedical data using each method in the Stardog triplestore.
3) The results show that RDF* requires fewer triples but the database size is larger, and it outperforms the other methods for complex queries.
A Semantic Data Model for Web ApplicationsArmin Haller
This presentation gives a short overview of the Semantic Web, RDFa and Linked Data. The second part briefly discusses ActiveRaUL, our model and system for developing form-based Web applications using Semantic Web technologies.
Linked data experience at Macmillan: Building discovery services for scientif...Michele Pasin
Macmillan is developing a linked data platform and semantic data model to power discovery services for scientific content. They have created an RDF-based data model and ontology to organize over 270 million triples of metadata. They are focusing on internal use cases and have implemented a hybrid architecture using MarkLogic and a triplestore to optimize query performance and deliver content in under 200ms. Going forward, they aim to expand the ontology, enable more advanced querying, and establish the semantic data model as a core enterprise asset.
This document discusses creating a knowledge graph for Irish history as part of the Beyond 2022 project. It will include digitized records from core partners documenting seven centuries of Irish history. Entities like people, places, and organizations will be extracted from source documents and related in a knowledge graph using semantic web technologies. An ontology was created to provide historical context and meaning to the relationships between entities in Irish history. Tools will be developed to explore and search the knowledge graph to advance historical research.
ROI in Linking Content to CRM by Applying the Linked Data StackMartin Voigt
Today, decision makers in enterprises have to rely more and more on a variety of data sets that are internally but also externally available in heterogeneous formats. Therefore, intelligent processes are required to build an integrated knowledge-base. Unfortunately, the adoption of the Linked Data lifecycle within enterprises, which targets the extraction, interlinking, publishing and analytics of distributed data, lags behind the public domain due to missing frameworks that are efficiently to deploy and ease to use. In this paper, we present our adoption of the lifecycle through our generic, enterprise-ready Linked Data workbench. To judge its benefits, we describe its application within a real-world Customer Relationship Management scenario. It shows (1) that sales employee could significantly reduce their workload and (2) that the integration of sophisticated Linked Data tools come with an obvious positive Return on Investment.
EC-WEB: Validator and Preview for the JobPosting Data Model of Schema.orgJindřich Mynarz
The presentation describes a tool for validating and previewing instances of Schema.org JobPosting described in structured data markup embedded in web pages. The validator and preview was developed to assist users of Schema.org to produce data of better quality. In this way, it tries to enhance usability of a part of Schema.org covering the domain of job postings. The paper discusses implementation of the tool and design of its validation rules based on SPARQL 1.1. Results of experimental validation of a job posting corpus harvested from the Web are presented. Among other findings, the results indicate that publishers of Schema.org JobPosting data often misunderstand precedence rules employed by markup parsers and that they ignore case-sensitivity of vocabulary names.
This document discusses various use cases for linked data and semantic web technology, including linked data for cross-domain knowledge bases like DBpedia and Freebase, linked geographic data like GeoNames and LinkedGeoData, linked government data from data portals like Data.gov and data.gov.uk, linked media data from projects like MusicBrainz, BBC, and LinkedMDB, linked data for user generated content from projects like flickr wrappr and Revyu.com, and linked life science data. It provides an overview of the concepts of linked data, RDF, URIs and describes several popular linked open datasets.
How is the Semantic Web vision unfolding and what does it take for the Web to fully reach its potential and evolve from a Web of Documents to a Web of Data through universal data representation standards.
Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven RecipesOntotext
This presentation will provide a brief introduction to logical reasoning and overview of the most popular semantic schema and ontology languages: RDFS and the profiles of OWL 2.
While automatic reasoning has always inspired the imagination, numerous projects have failed to deliver to the promises. The typical pitfalls related to ontologies and symbolic reasoning fall into two categories:
- Over-engineered ontologies. The selected ontology language and modeling patterns can be too expressive. This can make the results of inference hard to understand and verify, which in its turn makes KG hard to evolve and maintain. It can also impose performance penalties far greater than the benefits.
- Inappropriate reasoning support. There are many inference algorithms and implementation approaches, which work well with taxonomies and conceptual models of few thousands of concepts, but cannot cope with KG of millions of entities.
- Inappropriate data layer architecture. One such example is reasoning with virtual KG, which is often infeasible.
Linked Data Experiences at Springer NatureMichele Pasin
An overview of how we're using semantic technologies at Springer Nature, and an introduction to our latest product: www.scigraph.com
(Keynote given at http://2016.semantics.cc/, Leipzig, Sept 2016)
Diving in Panama Papers and Open Data to Discover Emerging NewsOntotext
Get guidance through the gigantic sea of freely released data from Panama Papers as well as Linked Open Data could. You will learn how it can empower you understanding of today’s news or any other information source.
Gain Super Powers in Data Science: Relationship Discovery Across Public DataOntotext
The document summarizes a webinar on relationship discovery across public data. It outlines the webinar agenda which includes use cases of relation discovery and media monitoring. It also describes examples of relationship discovery from datasets like the Panama Papers and media monitoring examples. It discusses linking news to knowledge graphs and semantic media monitoring. Finally, it covers mapping additional datasets to DBPedia to facilitate relationship discovery.
Build Narratives, Connect Artifacts: Linked Open Data for Cultural HeritageOntotext
Many issues are faced by scholars, book researchers, museum directors who try to find the underlying connection between resources. Scholars in particular continuously emphasizes the role of digital humanities and the value of linked data in cultural heritage information systems.
Enterprise knowledge graphs use semantic technologies like RDF, RDF Schema, and OWL to represent knowledge as a graph consisting of concepts, classes, properties, relationships, and entity descriptions. They address the "variety" aspect of big data by facilitating integration of heterogeneous data sources using a common data model. Key benefits include providing background knowledge for various applications and enabling intra-organizational data sharing through semantic integration. Challenges include ensuring data quality, coherence, and managing updates across the knowledge graph.
Choosing the Right Graph Database to Succeed in Your ProjectOntotext
The document discusses choosing the right graph database for projects. It describes Ontotext, a provider of graph database and semantic technology products. It outlines use cases for graph databases in areas like knowledge graphs, content management, and recommendations. The document then examines Ontotext's GraphDB semantic graph database product and how it can address key use cases. It provides guidance on choosing a GraphDB option based on project stage from learning to production.
Efficient Practices for Large Scale Text Mining ProcessOntotext
Text mining is a need when managing large scale textual collections. It facilitates access to, otherwise, hard to organise unstructured and heterogeneous documents, allows for extraction of hidden knowledge and opens new dimensions in data exploration.
In this webinar, Ivelina Nikolova, PhD, shares best practices and text analysis examples from successful text mining process in domains like news, financial and scientific publishing, pharma industry and cultural heritage.
[Conference] Cognitive Graph Analytics on Company Data and NewsOntotext
Ontotext introduced their cognitive analytics platform that performs cognitive graph analytics on company data and news. The platform builds large knowledge graphs by integrating data from multiple sources and uses text mining to link news articles to entities in the knowledge graph. It provides functionality for node ranking, similarity analysis and data cleaning to consolidate and reconcile company records across datasets. The platform was demonstrated through a knowledge graph containing over 2 billion facts built by integrating datasets like DBpedia, Geonames, and news article metadata.
How to Reveal Hidden Relationships in Data and Risk AnalyticsOntotext
Imagine risk analysis manager or compliance officer who can discover easily relationships like this: Big Bucks Café out of Seattle controls My Local Café in NYC through an offshore company. Such discovery can be a game changer if My Local Café pretends to be an independent small enterprise, while recently Big Bucks experiences financial difficulties.
The document discusses big data and linked data. It presents the three V's of big data - volume, velocity, and variety. It shows the semantic web layer cake and how linked data provides a lingua franca for data integration. It provides examples of using linked data for sensor data, supply chain data, and as a bridge between online and offline systems. Finally, it discusses adding a linked data layer to the existing internet architecture and engaging more stakeholders with the technology.
Linking Open, Big Data Using Semantic Web Technologies - An IntroductionRonald Ashri
The Physics Department of the University of Cagliari and the Linkalab Group invited me to talk about the Semantic Web and Linked Data - this is simply an introduction to the technologies involved.
Using the Semantic Web Stack to Make Big Data SmarterMatheus Mota
The document discusses using semantic web technologies to make big data smarter. It provides an overview of key concepts in semantic web, including linked data and ontologies. It describes how semantic web can add structure and meaning to unstructured data through modeling data as graphs and defining relationships and properties. The goal is to publish and query interconnected data at scale to enable new types of queries and inferences over big data.
1) The document compares different methods for representing statement-level metadata in RDF, including RDF reification, singleton properties, and RDF*.
2) It benchmarks the storage size and query execution time of representing biomedical data using each method in the Stardog triplestore.
3) The results show that RDF* requires fewer triples but the database size is larger, and it outperforms the other methods for complex queries.
A Semantic Data Model for Web ApplicationsArmin Haller
This presentation gives a short overview of the Semantic Web, RDFa and Linked Data. The second part briefly discusses ActiveRaUL, our model and system for developing form-based Web applications using Semantic Web technologies.
Linked data experience at Macmillan: Building discovery services for scientif...Michele Pasin
Macmillan is developing a linked data platform and semantic data model to power discovery services for scientific content. They have created an RDF-based data model and ontology to organize over 270 million triples of metadata. They are focusing on internal use cases and have implemented a hybrid architecture using MarkLogic and a triplestore to optimize query performance and deliver content in under 200ms. Going forward, they aim to expand the ontology, enable more advanced querying, and establish the semantic data model as a core enterprise asset.
This document discusses creating a knowledge graph for Irish history as part of the Beyond 2022 project. It will include digitized records from core partners documenting seven centuries of Irish history. Entities like people, places, and organizations will be extracted from source documents and related in a knowledge graph using semantic web technologies. An ontology was created to provide historical context and meaning to the relationships between entities in Irish history. Tools will be developed to explore and search the knowledge graph to advance historical research.
ROI in Linking Content to CRM by Applying the Linked Data StackMartin Voigt
Today, decision makers in enterprises have to rely more and more on a variety of data sets that are internally but also externally available in heterogeneous formats. Therefore, intelligent processes are required to build an integrated knowledge-base. Unfortunately, the adoption of the Linked Data lifecycle within enterprises, which targets the extraction, interlinking, publishing and analytics of distributed data, lags behind the public domain due to missing frameworks that are efficiently to deploy and ease to use. In this paper, we present our adoption of the lifecycle through our generic, enterprise-ready Linked Data workbench. To judge its benefits, we describe its application within a real-world Customer Relationship Management scenario. It shows (1) that sales employee could significantly reduce their workload and (2) that the integration of sophisticated Linked Data tools come with an obvious positive Return on Investment.
EC-WEB: Validator and Preview for the JobPosting Data Model of Schema.orgJindřich Mynarz
The presentation describes a tool for validating and previewing instances of Schema.org JobPosting described in structured data markup embedded in web pages. The validator and preview was developed to assist users of Schema.org to produce data of better quality. In this way, it tries to enhance usability of a part of Schema.org covering the domain of job postings. The paper discusses implementation of the tool and design of its validation rules based on SPARQL 1.1. Results of experimental validation of a job posting corpus harvested from the Web are presented. Among other findings, the results indicate that publishers of Schema.org JobPosting data often misunderstand precedence rules employed by markup parsers and that they ignore case-sensitivity of vocabulary names.
This document discusses various use cases for linked data and semantic web technology, including linked data for cross-domain knowledge bases like DBpedia and Freebase, linked geographic data like GeoNames and LinkedGeoData, linked government data from data portals like Data.gov and data.gov.uk, linked media data from projects like MusicBrainz, BBC, and LinkedMDB, linked data for user generated content from projects like flickr wrappr and Revyu.com, and linked life science data. It provides an overview of the concepts of linked data, RDF, URIs and describes several popular linked open datasets.
How is the Semantic Web vision unfolding and what does it take for the Web to fully reach its potential and evolve from a Web of Documents to a Web of Data through universal data representation standards.
Best Practices for Large Scale Text Mining ProcessingOntotext
Q&A:
NOW facilitates semantic search by having annotations attached to search strings. How compolex does that get, e.g. with wildcards between annotated strings?
NOW’s searchbox is quite basic at the moment, but still supports a few scenarios.
1. Pure concept/faceted search - search for all documents containing a concept or where a set of concepts are co-occurring. Ranking is based on frequence of occurrence.
2. Concept/faceted + Full Text search - search for both concepts and particular textual term of phrase.
3. Full text search
With search, pretty much anything can be done to customise it. For the NOW showcase we’ve kept it fairly simple, as usually every client has a slightly different case and wants to tune search in a slightly different direction.
The search in NOW is faceted which means that you search with concepts (facets) and you retrieve all documents which contain mentions of the searched concept. If you search by more than one facet the engine retrieves documents which contain mentions of both concepts but there is no restriction that they occur next to each other.
Is the tagging service expandable (say with custom ontologies)? also is it a something you offer as a service? it is unclear to me from the website.
The TAG service is used for demonstration purposes only. The models behind it are trained for annotating news articles. The pipeline is customizable for every concrete scenario, different domains and entities of interest. You can access several of our pipelines as a service through the S4 platform or you can have them hosted as an on premise solution. In some cases our clients want domain adaptation or improvements in particular area, or to tag with their internal dataset - in this case we offer again an on premise deployment and also a managed service hosted on our hardware.
Hdoes your system accomodate cluster analysis using unsupervised keyword/phrase annotation for knowledge discovery?
As much as the patterns of user behaviour are also considered knowledge discovery we employ these for suggesting related reads. Apart from these we have experience tailoring custom clustering pipelines which also rely on features like keyword and named entities.
For topic extraction how many topics can we extract? from twitter corpus wgat csn we infer?
For topic extraction we have determined that we obtain best results when suggesting 3 categories. These are taken from IPTC but only the uppermost levels which are less than 20.
The twitter corpus example is from a project Ontotext participates in called Pheme. The goal of the project is to detect rumours and to check their veracity, thus help journalists in their hunt for attractive news.
Do you provide Processing Resources and JAPE rules for GATE framework and that can be used with GATE embedded?
We are contributing to the GATE framework and everything which has been wrapped up as PRs has been included the corresponding GATE distributions.
1. Relational databases dominated data storage from the 1980s by storing data in tables but struggle with today's exponentially growing and interconnected data.
2. A graph database represents an alternative that allows storing highly connected data through nodes, edges, and properties, avoiding the need to create additional tables to represent relationships.
3. In a graph database, relationships are implicitly part of the data model so there is no need to create junction tables to represent connections like in a relational database.
Thank you for your interest in downloading our webinar, Analytics for 2014: The Numbers that Matter.
In this webinar, you'll learn about important metrics for web, email, mobile and social channels. When you collect the numbers that matter, you'll learn what is happening. From there, you can hypothesize why of certain analytics and create a plan to optimize and improve. Now that's Smart Marketing!
This document discusses an enterprise speech analytics solution presented by Surya Putchala. The solution analyzes customer service call audio data to provide actionable insights. It can interpret voice to text, analyze sentiment and mood in real-time, and score service representatives. Analytics dashboards provide insights into topics, response times, top customers and representatives, call volumes, lengths, and more. The benefits of the solution include improving customer experience, service quality, reducing costs, and increasing revenue through up-selling and cross-selling while reducing attrition.
The document discusses designing teams and processes to adapt to changing needs. It recommends structuring teams so members can work within their competencies and across projects fluidly with clear roles and expectations. The design process should support the team and their work, and be flexible enough to change with team, organization, and project needs. An effective team culture builds an environment where members feel free to be themselves, voice opinions, and feel supported.
Antidot Semantic Publishing - Réussir un site éditorial agrégeant plusieurs s...Antidot
Pour lancer à l'été 2013 Ilosport.fr, le premier portail Internet multisports dédié à la pratique sportive, L'Équipe souhaitait proposer une information complète autour des disciplines sportives les plus pratiquées en France avec la genèse et l'histoire de chaque sport, des conseils forme, matériel et sécurité, et toutes les informations utiles sur les lieux de pratique ainsi qu'un agenda d'événements.Pour offrir cette richesse de contenus, L'Équipe s'est appuyée sur plusieurs fournisseurs de données, institutionnels et privés. Antidot Information Factory a facilité la constitution d'une base d'informations très riche et le moteur de recherche sémantique Antidot Finder Suite a simplifié la restitution de ces informations au sein d'une interface web.
Avec le témoignage de Frédérique Lancien, Directrice Digital et New Business du groupe L'Équipe
This document discusses the agINFRA project's efforts to enhance interoperability between agricultural data sources by developing a linked data framework for germplasm data. The agINFRA Germplasm Working Group aims to identify relevant standards, analyze existing schemas and vocabularies, and propose recommendations for exposing germplasm resources as linked open data. Key outcomes include a dossier of germplasm information and engagement with stakeholders. The proposed methodology involves defining a base schema, publishing local classifications as linked data, and linking data from different sources using common vocabularies. Implementation plans include publishing germplasm vocabularies and phenotypic data in 2014.
Publishing Germplasm Vocabularies as Linked DataValeria Pesce
What has already been published?
What may still be needed?
How to do it?
This presentation is a part of the 3rd Session of the 1st International e-Conference on Germplasm Data Interoperability https://sites.google.com/site/germplasminteroperability/
What is GraphDB and how can it help you run a smart data-driven business?
Learn about GraphDB through the solutions it offers in a simple and easy to understand way. In the slides below we have unpacked GraphDB for you, using as little tech talk as possible.
European agrobiodioversity, ECPGR network meeting on EURISCO, Central Crop Da...Dag Endresen
Presentation on the Darwin Core standard for data exchange and the germplasm extension for genebanks during the 2014 workshop of the ECPGR Documentation and Information Working Group "Tailoring the Documentation of Plant Genetic Resources in Europe to the Needs of the User" (http://www.ecpgr.cgiar.org/working_groups/documentation_information/docinfo2014.html) in Prague-Ruzyně, Czech Republic, 20th May 2014.
Short URL: https://goo.gl/C5UEnU
DOI: http://doi.org/10.13140/RG.2.2.10865.28006
Linked Open Data-enabled Strategies for Top-N RecommendationsCataldo Musto
Linked Open Data-enabled Strategies for Top-N Recommendations - Cataldo Musto, Pierpaolo Basile, Pasquale Lops, Marco De Gemmis and Giovanni Semeraro - 1st Workshop on New Trends in Content-based Recommender Systems, co-located with ACM Recommender Systems 2014
What is #LODLAM?! Understanding linked open data in libraries, archives [and ...Alison Hitchens
This document provides an overview of linked open data (LOD) and the Resource Description Framework (RDF) and their applications in libraries, archives, and museums (LODLAM). It begins by defining linked data and how it extends standard web technologies to share structured data between computers. The document then discusses using structured, machine-readable data to describe resources like people, and how to structure this data using RDF. It provides examples of libraries and archives sharing controlled vocabularies, unique resources and holdings data as linked open data. The document concludes by reviewing current LODLAM projects and the potential for libraries and archives to both contribute and consume linked open data.
Intro to Linked Open Data in Libraries Archives & Museums.Jon Voss
This document discusses a presentation on Linked Open Data in libraries, archives, and museums. The presentation introduces Linked Open Data and how it is being used in cultural heritage institutions. It discusses representing data as graphs using triples and RDF, important vocabularies and ontologies, and following Tim Berners-Lee's principles of Linked Data. The presentation also covers legal and licensing considerations for publishing open cultural data on the web.
Powerful Information Discovery with Big Knowledge Graphs –The Offshore Leaks ...Connected Data World
Borislav Popov's slides from his lightning talk at Connected Data London. Borislav - a Director of Business Development at Ontotext presented Ontotext's approach to tackling the Panama Papers leak. Using a technology that is a mix between semantic web and graph databases.
Boost your data analytics with open data and public news contentOntotext
Get guidance through the gigantic sea of freely available Open Data and learn how it can empower you analysis of any kind of sources.
This webinar is a live demo of news and data analytics, based on rich links within big knowledge graphs. It will show you how to:
Build ranking reports (e.g for people and organisations)
View topics linked implicitly (e.g. daughter companies, key personnel, products …)
Draw trend lines
Extend your analytics with additional data sources
This document discusses using open data and news analytics. It demonstrates how a semantic publishing platform can link text to concepts in knowledge graphs to enable navigation from text to entities and related news. It provides examples of queries over linked data from DBpedia, Geonames, and news metadata to retrieve information about cities, people related to Google, airports near London, and news mentioning companies. Graphs and rankings show the popularity and relationships of entities in the news by industry such as automotive, finance, and banking.
This slide deck has been prepared for a workshop on Linked Data Publishing and Semantic Processing using the Redlink platform (http://redlink.co). The workshop delivered at the Department of Information Engineering, Computer Science and Mathematics at Università degli Studi dell'Aquila aimed at providing a general understanding of Semantic Web Technologies and how these can be used in real world use cases such as Salzburgerland Tourismus.
A brief introduction has been also included on MICO (Media in Context) a European Union part-funded research project to provide cross-media analysis solutions for online multimedia producers.
As part of the final BETTER Hackathon, project partners prepared 4 hackathon exercises. Fraunhofer IAIS organised this exercise in conjunction with external partner MKLab ITI-CERTH (EOPEN project). This step-by-step exercise featured the setup of local Docker images on Linux OS featuring Dcoker Compose and (pre-installed) Python, SANSA, Hadoop, Apache Spark and Apache Zeppelin. It featured semantic transformation and and the use of SANSA (Scalable Semantic Analytics Stack - http://sansa-stack.net/) libraries on a sample of tweets ahead of geo-clustering.
Project website (Hackathon information): https://www.ec-better.eu/pages/2nd-hackathon
Github repository: https://github.com/ec-better/hackathon-2020-semanticgeoclustering
This document provides an overview of relevant approaches for accessing open data programmatically and data-as-a-service (DaaS) solutions. It discusses common data access methods like web APIs, OData, and SPARQL and describes several DaaS platforms that simplify publishing and consuming open data. It also outlines requirements for a proposed open DaaS platform called DaPaaS that aims to address challenges in open data management and application development.
Open Data Portals: 9 Solutions and How they CompareSafe Software
Get a comparison of CKAN, Socrata, ArcGIS Open Data and other top open data solutions. Plus get answers to best practice questions such as: Which datasets are important to share? What are the approximate costs? Which file formats should the data be shared in? How often should the data get updated? And overall, how can we ensure success with our open data portal?
An introduction deck for the Web of Data to my team, including basic semantic web, Linked Open Data, primer, and then DBpedia, Linked Data Integration Framework (LDIF), Common Crawl Database, Web Data Commons.
The document discusses data discovery, conversion, integration and visualization using RDF. It covers topics like ontologies, vocabularies, data catalogs, converting different data formats to RDF including CSV, XML and relational databases. It also discusses federated SPARQL queries to integrate data from multiple sources and different techniques for visualizing linked data including analyzing relationships, events, and multidimensional data.
Linked Data (1st Linked Data Meetup Malmö)Anja Jentzsch
This document discusses Linked Data and outlines its key principles and benefits. It describes how Linked Data extends the traditional web by creating a single global data space using RDF to publish structured data on the web and by setting links between data items from different sources. The document outlines the growth of Linked Data on the web, with over 31 billion triples from 295 datasets as of 2011. It provides examples of large Linked Data sources like DBpedia and discusses best practices for publishing, consuming, and working with Linked Data.
How google is using linked data today and vision for tomorrowVasu Jain
In this presentation, I will discuss how modern search engines, such as Google, make use of Linked Data spread inWeb pages for displaying Rich Snippets. Also i will present an example of the technology and analyze its current uptake.
Then i sketched some ideas on how Rich Snippets could be extended in the future, in particular for multimedia documents.
Original Paper :
http://scholar.google.com/citations?view_op=view_citation&hl=en&user=K3TsGbgAAAAJ&authuser=1&citation_for_view=K3TsGbgAAAAJ:u-x6o8ySG0sC
Another Presentation by Author: https://docs.google.com/present/view?id=dgdcn6h3_185g8w2bdgv&pli=1
On-Demand RDF Graph Databases in the CloudMarin Dimitrov
slides from the S4 webinar "On-Demand RDF Graph Databases in the Cloud"
RDF database-as-a-service running on the Self-Service Semantic Suite (S4) platform: http://s4.ontotext.com
video recording of the talk is available at http://info.ontotext.com/on-demand-rdf-graph-database
Enabling Low-cost Open Data Publishing and ReuseMarin Dimitrov
In the space of just a few years we’ve seen the transformational power of open data; both for transparency and accountability in public data, and efficiency and innovation with businesses in private data. In its first year, institutions and individuals throughout Europe have supported public sector bodies in releasing data and numerous start-ups, developers and SMEs in reusing this data for economic benefit.
However, we are still at the beginning of the open data movement, and there is still more that can be done to make open data simpler to use and to make it available to a wider audience.
The core goal of the DaPaaS project is to provide a Data- and Platform-as-a-Service environment, where 3rd parties (such as governmental organisations, SMEs, developers and larger companies) can publish and host both data sets and data-intensive applications, which can then be accessed by end-user applications in a cross-platform manner. You can find out more about DaPaaS on the detailed about page.
Essentially, DaPaaS aims to make publishing, consumption, and reuse of open data, as well as deploying open data applications, easier and cheaper for SMEs and small public bodies which otherwise may not have sufficient technical expertise, infrastructure and resources required to do so.
see also http://www.slideshare.net/eswcsummerschool/wed-roman-tutopendatapub-38742186
Analytical Innovation: How to Build the Next Generation Data PlatformVMware Tanzu
There was a time when the Enterprise Data Warehouse (EDW) was the only way to provide a 360-degree analytical view of the business. In recent years many organizations have deployed disparate analytics alternatives to the EDW, including: cloud data warehouses, machine learning frameworks, graph databases, geospatial tools, and other technologies. Often these new deployments have resulted in the creation of analytical silos that are too complex to integrate, seriously limiting global insights and innovation.
Join guest speaker, 451 Research’s Jim Curtis and Pivotal’s Jacque Istok for an interactive discussion about some of the overarching trends affecting the data warehousing market, as well as how to build a next generation data platform to accelerate business innovation. During this webinar you will learn:
- The significance of a multi-cloud, infrastructure-agnostic analytics
- What is working and what isn’t, when it comes to analytics integration
- The importance of seamlessly integrating all your analytics in one platform
- How to innovate faster, taking advantage of open source and agile software
Speakers: James Curtis, Senior Analyst, Data Platforms & Analytics, 451 Research & Jacque Istok, Head of Data, Pivotal
GeoLinked Data (.es) is an open initiative whose aim is to enrich the Web of Data with Spanish geospatial data. This initiative started off by publishing diverse information sources belonging to the Spanish National Geographic Institute. Such sources are made available as RDF (Resource Description Framework) knowledge bases according to the Linked Data principles. With this work, Spain has joined the Linked Data initiative, in which the United Kingdom and Germany are already participating. In this presentation, we provide an overview of the process that has been followed for the development of this initiative.
How do we develop open source software to help open data ? (MOSC 2013)Sammy Fung
Sammy Fung discusses using open source tools like Python and Scrapy to extract open weather data from websites like the Hong Kong Observatory in order to make it more accessible. He created an open source project called hk0weather that scrapes current weather reports and exports the data to JSON format. The goal is to develop open source projects that can create open data in standard machine-readable formats to help citizens access public data more easily.
"Semantic Integration Is What You Do Before The Deep Learning". dev.bg Machine Learning seminar, 13 May 2019.
It's well known that 80\% of the effort of a data scientist is spent on data preparation. Semantic integration is arguably the best way to spend this effort more efficiently and to reuse it between tasks, projects and organizations. Knowledge Graphs (KG) and Linked Open Data (LOD) have become very popular recently. They are used by Google, Amazon, Bing, Samsung, Springer Nature, Microsoft Academic, AirBnb… and any large enterprise that would like to have a holistic (360 degree) view of its business. The Semantic Web (web 3.0) is a way to build a Giant Global Graph, just like the normal web is a Global Web of Documents. IEEE already talks about Big Data Semantics. We review the topic of KGs and their applicability to Machine Learning.
Presentation about http://worldwidesemanticweb.org/ given at SugarCamp#3 in Paris on April 12-13. The slides introduce the activities of the WWSW group centred around adapting Semantic Web technologies to be usable in challenging conditions.
Similar a The Power of Semantic Technologies to Explore Linked Open Data (20)
It Don’t Mean a Thing If It Ain’t Got SemanticsOntotext
With the tons of bits of data around enterprises and the challenge to turn these data into knowledge, meaning is arguably in the systems of the best database holder.
Turning data pieces into actionable knowledge and data-driven decisions takes a good and reliable database. The RDF database is one such solution.
It captures and analyzes large volumes of diverse data while at the same time is able to manage and retrieve each and every connection these data ever get to enter in.
In our latest slides, you will find out why we believe RDF graph databases work wonders with serving information needs and handling the growing amounts of diverse data every organization faces today.
[Webinar] GraphDB Fundamentals: Adding Meaning to Your DataOntotext
In this webinar, Desislava Hristova demonstrated how to install and set-up GraphDB™ and how one can generate RDF dataset. She also showed how one can quickly integrate complex and highly interconnected data using RDF, how to write some simple SPARQL queries and more.
In a nutshell, this webinar is suitable for those who are new to RDF databases and would like to learn how they can smartly manage their data assets with GraphDB™.
Hercule: Journalist Platform to Find Breaking News and Fight Fake OnesOntotext
Hercule: a platform to help journalists detect emerging news topics, check their veracity, track an event as it unfolds and find the various angles in a story as it develops.
How to migrate to GraphDB in 10 easy to follow steps Ontotext
GraphDB Migration Service helps you institute Ontotext GraphDB™ as your new semantic graph database. GraphDB Migration Service helps you institute Ontotext GraphDB™ as your new semantic graph database.
Designed with a view to making your transitioning to GraphDB frictionless and resource-effective, GraphDB Migration Service provides the technical support and expertise you and your team of developers need to build a highly efficient architecture for semantic annotation, indexing and retrieval of digital assets.
With GraphDB Migration Services you will:
* Optimize the cost of managing the RDF database;
* Improve the performance of your system;
* Get the maximum value from your semantic solution.
GraphDB Cloud: Enterprise Ready RDF Database on DemandOntotext
GraphDB Cloud is an enterprise grade RDF graph database providing high-performance querying over large volumes of RDF data. On this webinar, Ontotext demonstrates how to instantly create and deploy a fully managed Graph Database, then import & query data with the (OpenRDF) GraphDB Workbench, and finally explore and visualize data with the build in visualization tools.
Smarter content with a Dynamic Semantic Publishing PlatformOntotext
Personalized content recommendation systems enable users to overcome the information overload associated with rapidly changing deep and wide content streams such as news. This webinar discusses Ontotext’s latest improvements to its Dynamic Semantic Publishing (DSP) platform NOW (News on the Web). The Platform includes social data mining, web usage mining, behavioral and contextual semantic fingerprinting, content typing and rich relationship search.
Semantic Data Normalization For Efficient Clinical Trial ResearchOntotext
This document discusses semantic data normalization of clinical trial data to make it more structured and amenable to analysis. It describes converting unstructured clinical data like conditions, interventions, adverse events and eligibility criteria into RDF triples. The goal is to extract key phrases and concepts, identify qualifiers and relationships to formally represent the data. Examples show how condition texts, drug annotations and criteria can be modeled. Current work has normalized over 215,000 clinical studies from ClinicalTrials.gov into over 80 million RDF triples. The normalized data is pre-loaded in GraphDB and Ontotext S4 Cloud and can be explored and analyzed more easily.
Gaining Advantage in e-Learning with Semantic Adaptive TechnologyOntotext
In this presentation, we will introduce you to a solution that involves adaptive semantic technology for educational institutions and e-learning providers. You will learn how to integrate 3rd party resources, legacy assets, and other content sources to create the so-called knowledge graph of all structured and unstructured data.
Why Semantics Matter? Adding the semantic edge to your content,right from au...Ontotext
We’ll address a few of the basic industry pain points and show how semantics can come to the rescue, including:
How semantics can add value across the various phases of digital product development lifecycle.
Contextual authoring and content curation through automated editorial workflow solutions.
Enhanced content discoverability through relevant recommendations.
Coming together of bulletproof content delivery platform and dynamic semantic publishing technology
Adding Semantic Edge to Your Content – From Authoring to DeliveryOntotext
Within the last few years we see and ever increasing demand for more accurate user specific content which on the other hand overwhelms content providers.This is where smart publishing platforms come into play. They aim at bringing the right content at the right time – digested, easy to comprehend, fast to navigate, and tailored to the readers’ personal interests.
The technologies that power them help publishers to automate the metadata enrichment process, making it more consistent, accurate and rich.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
The Power of Semantic Technologies to Explore Linked Open Data
1. The Power of
Semantic Technologies
to Explore
Linked Open Data
Graphorum & Smart Data Conference, Jan 2017
2. You will learn how to:
• Convert tabular data into RDF
• Combine local and remote data in a single query
• Graphically explore the connectivity patterns in big diverse data
− 1B+ triples, 1000+ classes, 8 datasets
• Detect suspicious patterns of company control
• Filter news based on relationships between companies and people
• Rank companies per industry and region
3. Presentation Outline
•Use cases: Relation discovery and Media monitoring
•GraphDB’s OntoRefine conversion of tabular data in RDF
•FactForge: Open data and news about people and organizations
•Relationship Discovery Examples
•Media Monitoring Examples & Popularity Ranking
•Panama Papers and Global Legal Entity Identifier as Open Data
•Tracing Panama Papers entities in the news
5. Commercial Company
Database
(e.g. D&B)
Link data!
Reveal more!
Social Media
News
Wikipedia
Private
• Link diverse data in a
Knowledge Graph
• Analyze News and
Social Content
• Extract facts and
link content to data
• Interpret data in context
of big linked data
7. Relation Discovery Case
• Find suspicious
relationships like:
− Company in USA
− Controls another
company in USA
− Through a company in
an off-shore zone
• Show news
relevant to these
companies
8. Linking News to Big Knowledge Graphs
• The DSP platform
links text to
knowledge
graphs
• One can navigate
from news to
concepts,
entities and
topics, and from
there to other
news
Try it at http://now.ontotext.com
9. Semantic Media Monitoring
For each
entity:
•popularity
trends
•relevant
news
•related
entities
•knowledge
graph
information
Try it at http://now.ontotext.com
11. OntoRefine: Data Transformation to RDF
• Based on OpenRefine and integrated in the GraphDB Workbench
• Allows converting tabular data into RDF
− Supported formats are TSV, CSV, *SV, XLS, XLSX, JSON, XML, RDF as XML, and Google sheet
− Easily filter your data, edit its inconsistencies
− View the cleaned data as RDF
• Exposes a GraphDB SPARQL endpoint
− Transform your data using SPIN functions
− Import your data straight into a GraphDB repository
The Power of Semantic Technologies to Explore Linked Open Data Jan 2017 #11
12. OntoRefine: Uploading data
• Create new project
− From local / remote files
▪ Supported formats are TSV, CSV,
*SV, XLS, XLSX, JSON, XML, RDF as
XML, and Google sheet
▪ With the first opening of the file,
OntoRefine tries to recognize the
encoding of the text file and all
delimiters.
▪ Allows further fine-tuning of the
table configurations
− From clipboard
• Open / import a project
The Power of Semantic Technologies to Explore Linked Open Data Jan 2017 #12
13. OntoRefine: Viewing tabular data as RDF
• OpenRefine supports RDF as input only
• OntoRefine also supports RDF as output
The Power of Semantic Technologies to Explore Linked Open Data Jan 2017 #13
•Data shown as either records or rows
− A record combines multiple rows identifying the same
object and sharing the first column
•Data stored in a separate repository
− must not be mistaken with the current repository available
through GraphDB Workbench SPARQL tab
14. OntoRefine: RDF-izing data
• Transform data using a CONSTRUCT query
− in the OntoRefine SPARQL endpoint
− directly in the GraphDB SPARQL endpoint
• GraphDB 8.O supports SPIN functions:
− SPARQL functions for splitting a string
− SPARQL functions for parsing dates
− SPARQL functions for encoding URIs
The Power of Semantic Technologies to Explore Linked Open Data Jan 2017 #14
15. OntoRefine: Importing data in GraphDB
• After transforming the data, import it in the
current repository without leaving the
GraphDB Workbench
− Copy the endpoint of the OntoRefine project
− Go to GraphDB SPARQL menu
− Execute a query to import the results
The Power of Semantic Technologies to Explore Linked Open Data Jan 2017 #15
16. Combine local and remote data
• SPARQL Federation allows one to retrieve data from a remote end-
point in the middle of a query to a local repository
•For instance, to combine local data for GDP with information about
the area of each country from DBPedia to calculated GDP/sq.km.
Query GDP/Sq.km.
17. Federation example: GDP per Sq. Km.
SELECT DISTINCT ?name
(STR(?area) AS ?areaSqKm) (STR(?GDPperKm) AS ?GDPperSqKm)
{
?gdp2015prop gdp:forYear 2015 .
?country gdp:gdpCountry_Name ?name ; ?gdp2015prop ?gdp2015 .
{ SELECT (STR(?n) as ?name) ?area {
SERVICE <http://dbpedia.org/sparql> {
?c a dbo:Country ; rdfs:label ?n; dbp:areaKm ?area .
} } }
BIND(STR(ROUND(xsd:decimal(?gdp2015/1000000000))) AS ?gdp2015bil)
BIND(xsd:integer((?gdp2015) / ?area ) AS ?GDPperKm)
} ORDER BY DESC(?GDPperKm) LIMIT 10
18. FactForge: Open data and
news about people and
organizations
http://factforge.net
19. Our approach to Big Data
1. Integrate data from many sources
− Build a Big Knowledge Graph that integrates relevant
data from proprietary databases and taxonomies plus
millions of facts of Linked Data
1. Infer new facts and unveil
relationships
− Performing reasoning across different data sources
1. Interlink text and with big data
− Using text-mining to automatically discover references
to concepts and entities
1. Use graph database for metadata
management, querying and search
20. FactForge: Data Integration
DBpedia (the English version) 496M
Geonames (all geographic features on Earth) 150M
owl:sameAs links between DBpedia and Geonames 471K
Company registry data (GLEI) 3M
Panama Papers DB (#LinkedLeaks) 20M
Other datasets and ontologies: WordNet, WorldFacts, FIBO
News metadata (2000 articles/day enriched by NOW) 473M
Total size (1152M explicit + 322M inferred statements) 1 475М
21. News Metadata
• Metadata from Ontotext’s Dynamic Semantic Publishing platform
− News stream from Google
− Automatically generated as part of the NOW.ontotext.com semantic news showcase
•News stream from Google since Feb 2015, about 50k news/month
− ~70 tags (annotations) per news article
• Tags link text mentions of concepts to the knowledge graph
− Technically these are URIs for entities (people, organizations, locations, etc.) and key phrases
22. New Metadata
Category Count
International 52 074
Science and Technology 23 201
Sports 20 714
Business 15 155
Lifestyle 11 684
122 828
Mentions / entity type Count
Keyphrase 2 589 676
Organization 1 276 441
Location 1 260 972
Person 1 248 784
Work 309 093
Event 258 388
RelationPersonRole 236 638
Species 180 946
News Metadata
23. Class Hierarchy Map (by number of instances)
Left: The big picture
Right: dbo:Agent class (2.7M organizations and persons)
24. Sample queries at http://factforge.net
• F1: Big cities in Eastern Europe
• F2: Airports near London
• F3: People and organizations related to Google
• F4: Top-level industries by number of companies
Available as Saved Queries at http://factforge.net/sparql
Note: Open Saved Queries with the folder icon in the upper-right corner
26. Offshore control example
• Query: Find companies, which control other companies in the same
country, through company in an off-shore zone
• How it works:
• Establish control-relationship
• Establish a company-country mapping
• Establish an “off-shore criteria”
• SPARQL it
27. Off-shore company control example
SELECT *
FROM onto:disable-sameAs
WHERE {
?c1 fibo-fnd-rel-rel:controls ?c2 .
?c2 fibo-fnd-rel-rel:controls ?c3 .
?c1 ff-map:orgCountry ?c1_country .
?c2 ff-map:orgCountry ?c2_country .
?c3 ff-map:orgCountry ?c1_country .
FILTER (?c1_country != ?c2_country)
?c2_country ff-map:hasOffshoreProvisions true .
}
29. Semantic Media Monitoring/Press-Clipping
• We can trace references to a specific company in the news
− This is pretty much standard, however we can deal with syntactic variations in the names,
because state of the art Named Entity Recognition technology is used
− What’s more important, we distinguish correctly in which mention “Paris” refers to which of the
following: Paris (the capital of France), Paris in Texas, Paris Hilton or to Paris (the Greek hero)
• We can trace and consolidate references to daughter companies
• We have comprehensive industry classification
− The one from DBPedia, but refined to accommodate identifier variations and specialization (e.g.
company classified as dbr:Bank will also be considered classified as dbr:FinancialServices)
30. Media Monitoring Queries
• F5: Mentions in the news of an organization and its related entities
• F7: Most popular companies per industry, including children
• F8: Regional exposition of company – normalized
31. News Popularity Ranking: Automotive
Rank Company News # Rank
Company incl. mentions of child
companies News #
1 General Motors 2722 1 General Motors 4620
2 Tesla Motors 2346 2 Volkswagen Group 3999
3 Volkswagen 2299 3 Fiat Chrysler Automobiles 2658
4 Ford Motor Company 1934 4 Tesla Motors 2370
5 Toyota 1325 5 Ford Motor Company 2125
6 Chevrolet 1264 6 Toyota 1656
7 Chrysler 1054 7 Renault-Nissan Alliance 1332
8 Fiat Chrysler Automobiles 1011 8 Honda 864
9 Audi AG 972 9 BMW 715
10 Honda 717 10 Takata Corporation 547
32. News Popularity: Finance
Rank Company News # Rank Company incl. mentions of controlled News #
1 Bloomberg L.P. 3203 1 Intra Bank 261667
2 Goldman Sachs 1992 2 Hinduja Bank (Switzerland) 49731
3 JP Morgan Chase 1712 3 China Merchants Bank 38288
4 Wells Fargo 1688 4 Alphabet Inc. 22601
5 Citigroup 1557 5 Capital Group Companies 4076
6 HSBC Holdings 1546 6 Bloomberg L.P. 3611
7 Deutsche Bank 1414 7 Exor 2704
8 Bank of America 1335 8 Nasdaq, Inc. 2082
9 Barclays 1260 9 JP Morgan Chase 1972
10 UBS 694 10 Sentinel Capital Partners 1053
Note: Including investment funds, stock exchanges, agencies, etc.
33. News Popularity: Banking
Rank Company News # Rank Company incl. mentions of controlled News #
1 Goldman Sachs 996 1 China Merchants Bank * 38288
2 JP Morgan Chase 856 2 JP Morgan Chase 1972
3 HSBC Holdings 773 3 Goldman Sachs 1030
4 Deutsche Bank 707 4 HSBC 966
5 Barclays 630 5 Bank of America 771
6 Citigroup 519 6 Deutsche Bank 742
7 Bank of America 445 7 Barclays 681
8 Wells Fargo 422 8 Citigroup 630
9 UBS 347 9 Wells Fargo 428
10 Chase 126 10 UBS 347
35. Global Legal Entity Identifier (GLEI) data
• Global Markets Entity Identifier (GMEI) Utility data
− The Global Markets Entity Identifier (GMEI) utility is DTCC's legal entity identifier solution
offered in collaboration with SWIFT
− We downloaded as XML data dump from https://www.gmeiutility.org/
• RDF-ized company records
− Fields: LEI#, legal name, ultimate parent, registered country
− 3M explicit statements for 211 thousand organizations
▪ For comparison, there are 490 000 organizations in DBPeda and D&B covers above 200 million
− 10,821 ultimate parent relationships and 1632 ultimate parents
• 2 800 organizations from the GLEI dump mapped to DBPedia
36. GLEI Company Data Sample: ABN-AMRO
lei:businessRegistry Kamer van Koophandel
lei:businessRegistryNumber 34334259
lei:duplicateReference data:549300T5O0D0T4V2ZB28
lei:entityStatus ACTIVE
lei:headquartersCity Amsterdam
lei:headquartersState Noord-Holland
lei:legalForm NAAMLOZE VENNOOTSCHAP
lei:legalName ABN AMRO Bank N.V.
lei:lei BFXS5XCH7N0Y05NIXW11
lei:registeredCity Amsterdam
lei:registeredCountry NL
lei:registeredPostCode 1082 PP
lei:registeredState Noord-Holland
GLEI Company Data Sample: ABN-AMRO
37. Ultimate parent Children Country
1 The Goldman Sachs Group, Inc. 1 851 US
2 United Technologies Corporation 427 US
3 Honeywell International Inc. 341 US
4 Morgan Stanley 228 US
5 Cargill, Incorporated 217 US
6 1832 Asset Management L.P. 202 CA
7 Aegon N.V. 174 NL
8 Union Bancaire Privée, UBP SA 138 CH
9 Citigroup Inc. 135 US
10 State Street Corporation 128 US
Country Companies
1 dbr:United_States 103 548
2 dbr:Canada 17 425
3 dbr:Luxembourg 13 984
4 dbr:Sweden 7 934
5 dbr:United_Kingdom 7 421
6 dbr:Belgium 6 868
7 dbr:Ireland 4 762
8 dbr:Australia 4 385
9 dbr:Germany 3 039
10 dbr:Netherlands 2 561
Global Legal Entity Identifier (GLEI) data
38. Offshore Leaks Database from ICIJ
• Published by the International Consortium of Investigative
Journalists (ICIJ) on 9th of May
• A “searchable database” about 320 000 offshore companies
− 214 000 extracted from Panama Papers (valid until 2015)
− More than 100 000 from 2013 Offshore leaks investigation (valid until 2010)
• CSV extract from a graph database available for download
• https://offshoreleaks.icij.org/
40. Offshore Leaks DB as Linked Open Data
• Ontotext published the Offshore Leaks DB as Linked Open Data
• Available for exploration, querying and download at
http://data.ontotext.com
• ONTOTEXT DISCLAIMERS
We use the data as is provided by ICIJ. We make no representations and warranties of any kind,
including warranties of title, accuracy, absence of errors or fitness for particular purpose. All
transformations, query results and derivative works are used only to showcase the service and
technological capabilities and not to serve as basis for any statements or conclusions.
41. Enrichment and structuring of the data
• Relationship type hierarchy
− About 80 types of relationship types in the original dataset got organized in a property hierarchy
• Classification of officers into Person and Company
− In the original database there is no way to distinguish whether an officer is a physical person
• Mapping to DBPedia:
− 209 countries referred in Offshore Leaks DB are mapped to DBPedia
− About 3000 persons and 300 companies mapped to DBPedia
• Overall size of the repository: 22M statements (20M explicit)
42. The RDF-ization Process
• Linked data variant produced without programming
− The raw CSV files are RDF-ized using TARQL, http://tarql.github.io/
− Data was further interlinked and enriched in GraphDB using SPARQL
• The process is documented in this README file
• All relevant artifacts are open-source, available at
• https://github.com/Ontotext-AD/leaks/
• The entire publishing and mapping took about 15 person-days !!!
− Including data.ontotext.com portal setup, promotion, documentation, etc.
43. Sample queries at http://data.ontotext.com
• Q1: Countries by number of entities related to them
• Q2: Country pairs by ownership statistics
• Q3: Statistics by incorporation year
• Q4: Officers and entities by number of capital relations
• Q5: Countries in Eastern Europe by number of owners
• Q6: Intermediaries in Asia by name
• Q7: The best connected officers
• Q8: Countries by number of Person and Company officers
45. Mapping datasets to DBPedia
• The task: map people, organizations and locations to IDs in DBPedia
− So that we can analyze the original data with the help of the extra information available in
DBPedia and other datasets that are related to it, e.g. Geonames
− For instance, #LinkedLeaks doesn’t contain any extra information about the companies, e.g.
industry sector, controlling or controlled companies, etc.
• Specific conditions: we had to map by names
− Other than names, the information about the entities in the source datasets couldn’t help the
mapping
▪ Address and country attributes are present, but those appeared to be marginally useful for mapping
− In both cases we mapped locations only in terms of countries and not finer grained locations
▪ For this purpose DBPedia geographic data is sufficient and it is also well mapped with GeoNames
46. Mapping datasets to DBPedia (2)
• We used the GraphDB connector to Lucene for these mappings
− Using the GraphDB connector, Lucene index was created for Organizations and People from
DBPedia, indexing all sorts of names, descriptions and other textual information for each entity
− The mapping process consists mostly of using the name of the entity from the 3rd party dataset
(in this case Panama Papers or GLEI) as a FTS query, embedded in a SPARQL query
• What is that Lucence does better than SPARQL?
− When there is little information other than the name, we benefit from the free text indexing of
Lucene, because it deals well with minor syntactic variations and sorts the results by relevance
− When mappings 300 000 organizations against another 500 000 organizations, without a key,
the complexity of a SPARQL query is 300 000 x 500 000, which is slower that 300 000 Lucene
queries
47. #LinkedLeaks Mapping Queries
• Companies mapped by industry
• Companies mapped in the Finance sector
• Politicians mapped
• Available as Saved Queries at http://factforge.net/sparql
• Note 1: Open Saved Queries with the folder icon in the upper-right
corner
50. Tracing Panama Papers entities in the news
• After mapping #LinkedLeaks entities to DBPedia identifiers, we can
load them, together with the mappings, in the FF-NEWS repository
• This way we have in a single repo, mapped to one another:
#LinkedLeaks data, DBPedia, News metadata
• We can make queries like: Give me news mentions of entities which
appear in the Panama Papers dataset
• This way the mapping enabled media monitoring at no extra cost
51. Thank you!
Experience the technology with NOW: Semantic News Portal
http://now.ontotext.com
and play with open data at
http://factforge.net
Notas del editor
ДИЗАЙН: Looks better with the graphics
A bit smaller title and more “air” around the logo would make it better – see the proposed re-attangement
ДИЗАЙН: Сивият фон прави слайда да изглежда различно и го разграничава от другите. Което е добре. От друга страна ми е някак „убито“
ДИЗАЙН: Сивият фон прави слайда да изглежда различно и го разграничава от другите. Което е добре. От друга страна ми е някак „убито“
ДИЗАЙН: новата графика е супер. На следващия слайд ще й пипна малко цветовете, защото наситеността и прозрачността на цветовете в случая трябва да отговаря на „плътността“ на данните. По-лек основен текст (не болд) – на конкретния слайд ми стои по-добре
Our vision is to enable machines to interpret data and text by interlinking those in big knowledge graphs.The web of open data in growing exponentially!
There are thousands of datasets from Wikipedia and Geonames to government statistical data and to Panama PapersWe link open data to analyze news. We extract data from news to produce more open data and analyze social media. We integrate all this with proprietary data and commercial databases.
Why???
To help journalists, banks, merchants, governments and citizens reveal more! Quicker, with less effort and less stress.
This is elevator pitch for our overall technology approach, proposition and applications
HOW MANY CONCEPTS A PERSON KNOWS?
HOW MANY CONCEPTS A PERSON KNOWS?
ДИЗАЙН: оранжавите ленти в случая слагаха твърде силен акцент