AI-SDV 2022: Copyright Clearance Center

•

0 recomendaciones•894 vistas

Copyright Clearance Center A pioneer in voluntary collective licensing, CCC (Copyright Clearance Center) helps organizations integrate, access, and share information through licensing, content, software, and professional services. With expertise in copyright and information management, CCC and its subsidiary RightsDirect collaborate with stakeholders to design and deliver innovative information solutions that power decision-making by helping people integrate and navigate data sources and content assets. CCC recently acquired the assets and technology of Deep SEARCH 9 (DS9), a knowledge management platform that leverages machine learning to help customers perform semantic search, tag content, and discover new insights.

Internet

Exploring Expert
Networks with
Knowledge Graphs

CCC Expert View
Draw actionable learnings from a
large volume of interconnected
data to identify qualified experts in
a domain of research.
• Find information on qualified experts
• Discover KOLs for a topic of interest
• Explore an expert’s network

RightFind Navigate
Make content more discoverable
and accessible.
• Break down information silos
• Integrate multiple data sources
• Use a single intuitive userface

RightFind XML
Powering AI discovery with scientific
articles.
• A single point of access to full-text journal article
content in normalized XML format across a wide
range of leading STM publishers.
• Choose from a variety of flexible models to
access normalized, full-text scientific literature in
XML format, licensed for commercial text and
data mining.
• Enrich AI, machine learning and data visualization
projects with insights that can only be found in
the full text of scientific articles.

Advancing copyright.
Accelerating knowledge.
Powering innovation.

Más contenido relacionado

Similar a AI-SDV 2022: Copyright Clearance Center

Federated to library discovery platfoms

Nikesh Narayanan

Open archives initiatives(final)

floyd taag

Open archives initiatives(final)

floyd taag

Open archives initiatives(final)

floyd taag

-Open Archives Initiatives(final)

floyd taag

Union catalogandknowledge engineering for teldap

AAT Taiwan

Advanced Searching

Ros Pan

Searching of Web and Electronic Resources

Bramesha B

Computer Science Masters Library Training - June 2017

pvhead123

K3 edith falk_discoverytoolslibrary

evaminerva

Elsevier - Smart Data and Algorithms for the Publishing Industry

Antonio Gulli

Semtech 2011 Elsevier PureDiscovery

visha1gupta

Itct year1 mitchell

Melanie Parlette-Stewart

Precision Content™ Tools, Techniques, and Technology

dclsocialmedia

Vivo ontology overviewanddirections.2013-04-25

joncr

In the fast-paced realm of data science, staying ahead requires a deep understanding of the tools and technologies that drive insights from data. From programming languages to advanced frameworks, the world of data science technologies is vast and dynamic. In this blog, we embark on a comprehensive guide, navigating through the essential tools that empower data scientists to unravel the mysteries hidden within datasets and shape the future of information analysis. For those seeking a structured and immersive learning experience, complementing this tech-centric journey with a well-crafted data science course is the key to unlocking boundless opportunities in this evolving field.

A Comprehensive Guide to Data Science Technologies.pdf

GeethaPratyusha

COMM1170 - MSE - Level 8 (Alcock and Guo) March 2013

Melanie Parlette-Stewart

Welcoming the research scholars, scientists around the globe in the Open Access Dimension, IJORCS is now accepting manuscripts for its next issue (Volume 4, Issue 4). Authors are encouraged to contribute to the research community by submitting to IJORCS, articles that clarify new research results, projects, surveying works and industrial experiences that describe significant advances in field of computer science. All paper submissions (http://www.ijorcs.org/submit-paper) are received and managed electronically by IJORCS Team. Detailed instructions about the submission procedure are available on IJORCS website (http://www.ijorcs.org/author-guidelines)

Call for Papers - IJORCS, Volume 4 Issue 4

IJORCS

Introduction to Structured Authoring

dclsocialmedia

Ultra search

Stefanos Anastasiadis

Similar a AI-SDV 2022: Copyright Clearance Center (20)

Federated to library discovery platfoms

Open archives initiatives(final)

-Open Archives Initiatives(final)

Union catalogandknowledge engineering for teldap

Advanced Searching

Searching of Web and Electronic Resources

Computer Science Masters Library Training - June 2017

K3 edith falk_discoverytoolslibrary

Elsevier - Smart Data and Algorithms for the Publishing Industry

Semtech 2011 Elsevier PureDiscovery

Itct year1 mitchell

Precision Content™ Tools, Techniques, and Technology

Vivo ontology overviewanddirections.2013-04-25

A Comprehensive Guide to Data Science Technologies.pdf

COMM1170 - MSE - Level 8 (Alcock and Guo) March 2013

Call for Papers - IJORCS, Volume 4 Issue 4

Introduction to Structured Authoring

Ultra search

Más de Dr. Haxel Consult

AI-SDV 2022: Henry Chang Patent Intelligence and Engineering Management

Dr. Haxel Consult

Knowledge Graphs are an increasingly relevant approach to store detailed knowledge in many domains. Recent advances in NLP allow to enrich Knowledge Graphs through automated analysis of large volumes of literature, reducing a lot the efforts in traditional manual information capturing. In our presentation we report the approach taken in a project with partner Fraunhofer SCAI in the life sciences where a knowledge graph organising detailed facts about psychiatric diseases has been computed. Information of cause-effect relations between proteins, genes, drugs and diseases has been encoded in the BEL (Biological Expression Language) and imported into a Graph database to approach an indication-wide Knowledge Graph for the selected therapeutic area. Ultimately, updating the graph will amount to just rerunning the analysis on the newly published literature.

AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...

Dr. Haxel Consult

In 2019 the UK was the first major economy to embrace a legal obligation to achieve net zero carbon emissions by 2050. More broadly, the 2021 UK Innovation Strategy sets out the UK government’s vision to make the UK a global hub for innovation by 2035 with a target of increasing public and private sector R&D expenditure to 2.4% of GDP to support the UK being a science superpower with a world-class research and innovation system. IP rights create an incentive for R&D which ultimately leads to innovation. Analysis and insights from IP data can therefore help provide a better understanding of how the IP system is being used and where and what innovation is taking place. Research and analysis of IP data is a key input to the ongoing work of the UKIPO’s Green Tech Working Group which seeks to: further the UK’s status as a global leader by making the UK’s IP environment the best for innovating green technology; develop and deliver IP policies to support government’s ambition on climate change and green technologies; and to help innovators best protect and commercialise their green tech innovations both at home and internationally. The UKIPO has been developing a broad portfolio of ‘green’ IP analytics research. A series of patent analytics reports have been published looking at green technologies, and analysis of how the UK’s Green Channel scheme for accelerated processing of green patent applications has been conducted. Patents have been used to identify technological comparative advantage within different green technologies at a country level, and new insights uncovered by mapping green technology patents to the UN Sustainable Development Goals (SDGs). Trade mark data provides a timeliness and closeness to market factor that patent data does not, and complementary trade mark analysis of UK ‘green’ trade marks, identified using a machine learning algorithm, provides a commercialisation angle to our research.

AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...

Dr. Haxel Consult

Word embeddings, deep learning, transformer models and other pre-trained neural language models (sometimes recently referred to as "foundational models") have fundamentally changed the way state-of-the-art systems for natural language processing and information access are built today. The "Data-to-Value" process methodology (Leidner 2013; Leidner 2022a,b) has been devised to embody best practices for the construction of natural language engineering solutions; it can assist practitioners and has also been used to transfer industrial insights into the university classroom. This talk recaps how the methodology supports engineers in building systems more consistently and then outlines the changes in the methodology to adapt it to the deep learning age. The cost and energy implications will also be discussed.

AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...

Dr. Haxel Consult

In the patent domain, all types of issues, from very specific search requirements to the linguistic characteristics of the text domain, are accentuated. Consequently, to develop patent text mining tools for scientists and patent experts, we need to understand their daily work tasks, as well as the linguistic character of the text genre (i.e., patentese). Patent text is a mixture of legal and domain-specific terms. In processing technical English texts, a multi-word unit method is often deployed as a word-formation strategy to expand the working vocabulary, i.e., introducing a new concept without the invention of an entirely new word. This productive word formation is a well-known challenge for traditional natural language processing tools utilizing supervised machine learning algorithms due to limited domain-specific training data. Deep learning technologies have been introduced to overcome the reduction in performance of traditional NLP tools. In the Artificial Researcher technologies, we have integrated explicit and implicit linguistic knowledge into the deep learning algorithms, essential for domain-specific text mining tools. In this talk, we will present a step-by-step process of how we have developed the mentioned text mining tools. For the final outline, we will also demonstrate how these tools can be integrated in a cross-genre passage retrieval system, based on a technology from 2016 that still holds the state-of-the-art within the patent text mining research community in 2022.

AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...

Dr. Haxel Consult

In 2013 we witnessed an evolutionary change in the NLP field evolved thanks to the introduction of space embeddings that, with the use of deep learning architectures, achieved human-level performances in many NLP tasks. With the introduction of the Attention mechanism in 2017 the results were further improved and, as result, embeddings are quickly becoming the de facto standards in solving many NLP problems. In this presentation, you will learn how generate and use space embedding for search purposes and provide comparison metrics to more traditional relevance-based search engines. Moreover, I will provide some initial results on a paper currently under review that provides an insight on hyperparameter tuning during the generation of embeddings.

AI-SDV 2022: Embedding-based Search Vs. Relevancy Search: comparing the new w...

Dr. Haxel Consult

AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...

Dr. Haxel Consult

AI-SDV 2022: Machine learning based patent categorization: A success story in...

Dr. Haxel Consult

AI-SDV 2022: Machine learning based patent categorization: A success story in...

Dr. Haxel Consult

It is relatively easy for a human to read a document and quickly figure out which concepts are important. However, this task is a difficult challenge for a machine. During the past few decades, there have been two main approaches for concept identification: Natural Language Processing and Machine Learning. During the early part of this century, Machine Learning made great strides as new techniques came into wider use (SVM’s, Topic Modeling, etc..). Sensing the competition, Natural Language Processing responded with deployment of new emerging techniques (sematic networks, finite state automata, etc..). Neither approach has completely solved the WHAT problem. Advances in Artificial Intelligence have the potential to significantly improve the situation. Where AI is making the most impact is as an enhancement to make Machine Learning and Natural Language Processing work better and, more importantly, work together. This presentation looks at some of this history and what might happen in the future when we blend the interpretation of language with pattern prediction.

AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...

Dr. Haxel Consult

Trademarks serve as key leading indicators for innovation and economic growth. As the vanguards of new and expanding enterprises, trademarks can be used to study entrepreneurship and shifting market demands in response to varying economic factors. This responsiveness has been seen as recently as the COVID-19 pandemic, where trademark research revealed key insights about business reaction to the global upheaval. At CIPO, we have been delving more deeply than ever before into trademark analysis by leveraging cutting-edge natural language processing (NLP) tools to derive actionable business intelligence from trademark data. In this presentation, we present a survey of NLP in use at CIPO and the insights we have learned applying them. These insights include COVID-19 responses, line-of-business trends based on firm characteristics, and more. We also discuss ongoing and future trademark research projects at CIPO. These projects include emerging technology detection methods and high-resolution trademark classification systems. We conclude that artificial intelligence-enhanced tools like NLP are key components of future exploitation of trademark data for business and economic intelligence.

AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...

Dr. Haxel Consult

In our customer projects involving automated document processing, we often encounter document types providing crucial data in the form of tables. While established text analytics algorithms are usually optimized to operate on running text, they tend to produce rather poor results on tables as they do not capture the non-sequential relations inside them (e.g. interpret the content of a table cell relative to its column title, interpret line breaks inside a cell differently from line breaks between cells or rows). While there are elaborate information extraction products in the market for a few highly specific types of tabular documents, there is no general approach out there. The main cause for this is the fact that table structures can be encoded by a heterogenous range of layout means (e.g. column boundaries can be signaled by lines vs. aligned text vs. white space). In this talk, we will illustrate several solutions that we have developed for a range of challenges occurring in this context, both for scanned and digitally generated documents.

AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...

Dr. Haxel Consult

Most scientific journals request, that the complete set of research data is published simultaneously with the peer-reviewed paper. The publication of the research data usually is carried out as so-called "Supplementary Material", attached to the original paper, or on a "Research Data Repository". Both forms have in common, that the data is published usually unstructured and not in an uniform machine processable format. This makes its further use in electronic tools for AI or data mining unnecessarily difficult or even impossible. A concept is presented, in which the data is digitally recorded, following the principle of FAIR data, as part of the publication process. This digital capture makes the data available to the scientific community for easy use in data mining and AI tools. The data in the repository contains links to the publication to document its origin. The concept is applicable for preprints, peer-review papers, diploma and doctoral theses and is particularly suitable for open access publications. Moreover, the presentation highlights correspondent activities, which were released in scientific publications recently.

AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...

Dr. Haxel Consult

AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...

Dr. Haxel Consult

How do you find video when you only have sparse data? While you can wander the stacks (if you can still find open stacks) for inspiration, video either physical or digital, is difficult to discover. Wandering the virtual stacks is, well, virtually impossible. Discovery platforms on the whole have not replicated the inspirational experience of wandering the stacks. More companies are using archivable video for internal communication of the various research projects, product developments, test results, and more that are being considered, in progress, or completed. Showing how an experiment was conducted can convey considerably more information that is very difficult to communicate via text. How do you find a company video that might be helpful for your project? A case study is presented of the problems and the solutions that were implemented by a large, multinational chemical company. A suite of content discovery technologies was used including a video to text to tagging system connected to their documents database and automatically indexed using several chemical as well as conceptual systems (rule-based, NLP, inference engine). To build the system and support the manuscript and video submission there is a metadata extraction program which pulls and inserts the metadata into the submission forms so the author can move quickly through that process.

AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...

Dr. Haxel Consult

Lighthouse IP is the world’s leading provider of intellectual property content. The core business of Lighthouse IP is sourcing and creating content from the world’s most challenging authorities. Specialized in IP data, Lighthouse IP provides over 160 countries coverage for patents, over 200 authorities for trademarks and over 90 authorities for designs. Lighthouse IP data is available via several partners. The company is headquartered in Schiphol-Rijk in the Netherlands and has offices in the United States, China, Thailand, Vietnam, Egypt, Indonesia and Belarus. Globally a team of 150 experts works on the creation of this unique data collection.

AI-SDV 2022: Lighthouse IP

Dr. Haxel Consult

CENTREDOC was created in 1964 as the technical information center of the swiss watchmaking industry. Building on a strong team of engineers, CENTREDOC now offers a complete range of services and solutions for the monitoring of strategic, technological and competitive information. CENTREDOC is also a leader in the research of patent, technical and business intelligence, and offers consulting expertise in the implementation of monitoring solutions.

AI-SDV 2022: New Product Introductions: CENTREDOC

Dr. Haxel Consult

AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...

Dr. Haxel Consult

What if there was a platform where literature, conference abstracts, patents, clinical trials, news, grants and other sources were fully integrated? What if the data would be harmonized, enriched with standardized concepts and ready for analysis? After building our patent analytics platform we didn’t stop dreaming and built our big data analytics platform by semantically integrating text-rich, scientific sources. In my presentation I will talk about what we built and why we built it. And, of course, I will also address the challenges and hurdles along the way. Was it worth it and what comes next? Let’s talk about it!

AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...

Dr. Haxel Consult

AI-SDV 2022 The Meeting AI-SDV 2022 Conference and Exhibition is planned to be held physically and virtually. Speakers, attendees and exhibitors are invited to join physically and virtually in Vienna, Austria. We are developing a range of digital formats that respond precisely to the speakers, attendees and exhibitors needs, enabling individuals and exhibitors from AI industry and related sectors worldwide to participate in the AI-SDV 2022 in Vienna. The Artificial Intelligence Conference on Search, Data and Text Mining, Analytics and Visualization. The 2022 AI-SDV Conference in Vienna, Austria, 10th - 11th October 2022. AI-SDV 2022 is the place to be for everyone involved in advanced search and data applications, text mining and visualization technologies. Individuals and companies that are shaping the future of this exciting space that surrounds us and impacts us all will present their latest research findings, tech developments and visions for the future. Enjoy a full-on two days of learning, networking and exploring technologies and concepts that are state of the art and will continue to change way we as individuals and organisations work, rest and play. The focus is on Artificial Intelligence (AI), Digitization 2.0 (about making companies, processes and people ready for AI), Deep Learning and other topics identified by our community, who are specialists working in scientific and technical information. The event features approximately 22 speakers over two days, plus an exhibition to complementing the conference programme. As past attendees tell us, Vienna is a fabulous location and beyond the formal meetings taking place in the conference and exhibition space, we make the most of this gorgeous location for informal evening receptions and networking dinners.

The Artificial Intelligence Conference on Search, Data and Text Mining, Analy...

Dr. Haxel Consult

Más de Dr. Haxel Consult (20)