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.

1. Powerful Information Discovery
with Big Knowledge Graphs –
The Offshore Leaks Case
Ontotext, July 2016

2. Data - Content - User
• Psycho-graphic vs. demographic profiles
• Build behavioural profiles on the basis of
semantic metadata associated with the assets
• Control results bias with runtime parameters
• Create semantic fingerprints of assets
• Driven off of a knowledge graph
• Automatically adapts through machine
learning
• Semantic Database
• Replication Cluster for enterprise clients
• Connectors to 3rd party indexing/storage
products & hybrid queries

3. Data Layer – the Core
Semantic Fingerprints of Content
Instance Data / Relationships / Facts
Ontology / Schema / Domain Model
GraphDB Node Zoom In
Node 1 Node 3
Master 1 Master 2
Enterprise

4. Semantic Enrichment Overview

5. Personalization – User Actions Model
perform
comments
votes
posts
preview
read
contains leads to
read
leads to
preview
Article
Search
Action
Result
Date
FTS Q. Tag
Cat
Tag set
results
cat
taxonomy
Search Log
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6. Quick news-analytics case
• Our Dynamic Semantic
Publishing platform
offers linking of text
with big open data
graphs
• One can navigate from
text to concepts, get
trends, related entities
and news
• Try it at
http://now.ontotext.com

7. FF-NEWS: Data Integration and Loading
• DBpedia (the English version only) 496M statements
• Geonames (all geographic features on Earth) 150M statements
− owl:sameAs links between DBpedia and Geonames 471K statements
• Company registry data (GLEI) 3M statements
• News metadata (from NOW) 128M statements
• Total size: 986М statements
− Mapped to FIBO; 667M explicit statements + 318M inferred statements
− RDFRank and geo-spatial indices enabled to allow for ranking and efficient geo-spatial constraints
Open data integration for news analytics

8. Technology: Semantic Content Enrichment

9. News Metadata
• Metadata from Ontotext’s Dynamic Semantic Publishing platform
− Automatically generated as part of the NOW.ontotext.com semantic news showcase
• News stream from Google since Feb 2015, about 10k 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
Apr 2016Hidden Relationships in Data and Risk Analytics

10. News Metadata
Apr 2016Hidden Relationships in Data and Risk Analytics
Category Count
International News 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

11. Sample queries at http://ff-news.ontotext.com
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
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
FF-NEWS is in Beta. Not officially launched, but available to play with.
Open data integration for news analytics

12. News Popularity Ranking: Automotive
Open data integration for news analytics
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

13. News Popularity: Finance
Open data integration for news analytics
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.

14. News Popularity: Banking
Open data integration for news analytics
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

15. 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/
Open data integration for news analytics

16. Offshore
Leaks
Database
Open data integration for news analytics

17. 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.
Open data integration for news analytics

18. 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)
Open data integration for news analytics

19. 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.
Open data integration for news analytics

20. 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

21. Play with semantically enriched news:
http://now.ontotext.com
Play with open data at
http://data.ontotext.com and http://ff-
news.ontotext.com