Gain Super Powers in Data Science: Relationship Discovery Across Public Data
39
Relationship Discovery Across Public Data Webinar Ontotext, 2016
Transcript of Gain Super Powers in Data Science: Relationship Discovery Across Public Data
Relationship Discovery Across Public Data Webinar
Ontotext, 2016
Relationship Discovery Webinar
Presentation Outline
• Use cases: Relation discovery and Media monitoring• FactForge-News open-data playground• Relationship Discovery Examples• Media Monitoring Examples• Panama Papers and Global Legal Entity Identifier as Open Data• Mapping Datasets to DBPedia with the GraphDB Lucene Connector• Tracing Panama Papers entities in the news
Aug 2016
Using FIBO and Open Data to Discover Relationships
Relation Discovery Case
Mar 2016 #3
• Find suspicious relationships like:− Company in USA controls
− Another company in USA
− Through a company in an off-shore zone
• Show news relevant to them
Relationship Discovery Webinar
Linking News to Big Knowledge Graphs
Aug 2016
• 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
Relationship Discovery Webinar
Semantic Media Monitoring
Aug 2016
For each entity:
• popularity trends
• Relevant news
• Related entities
• Knowledge graph information
Try it at http://now.ontotext.com
Relationship Discovery Webinar
Presentation Outline
• Use cases: Relation discovery and Media monitoring• FactForge-News open-data playground• Relationship Discovery Examples• Media Monitoring Examples• Panama Papers and Global Legal Entity Identifier as Open Data• Mapping Datasets to DBPedia with the GraphDB Lucene Connector• Tracing Panama Papers entities in the news
Aug 2016
Relationship Discovery Webinar
Our approach to Big Data
1. Integrate relevant data from many sources− Build a Big Knowledge Graph from proprietary databases and
taxonomies integrated with millions of facts of Linked Data
2. Infer new facts and unveil relationships− Performing reasoning across data from different sources
3. Interlink text and with big data− Using text-mining to automatically discover references to
concepts and entities
4. Use NoSQL graph database for metadata management, querying and search
Aug 2016
Relationship Discovery Webinar
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
• Panama Papers DB (#LinkedLeaks) 20M statements
• News metadata (from NOW) 145M statements
• Total size: 1 026М statements− Mapped to FIBO; 724M explicit statements + 302M inferred statementsAug 2016
Relationship Discovery Webinar
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
Aug 2016
Relationship Discovery Webinar
News Metadata
Aug 2016
Category Count International 52 074Science and Technology 23 201Sports 20 714Business 15 155Lifestyle 11 684
122 828
Mentions / entity type Count Keyphrase 2 589 676Organization 1 276 441Location 1 260 972Person 1 248 784Work 309 093Event 258 388RelationPersonRole 236 638Species 180 946
Relationship Discovery Webinar
Class Hierarchy Map (by number of instances)
Aug 2016
Left: The big pictureRight: dbo:Agent class (2.7M organizations and persons)
Relationship Discovery Webinar
Sample queries at http://ff-news.ontotext.comF1: 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://ff-news.ontotext.com/sparql
Note 1: Open Saved Queries with the folder icon in the upper-right corner
Note 2: FF-NEWS is still in Beta testing ! But available to play with
Aug 2016
Relationship Discovery Webinar
Presentation Outline
• Use cases: Relation discovery and Media monitoring• FactForge-News open-data playground• Relationship Discovery Examples• Media Monitoring Examples• Panama Papers and Global Legal Entity Identifier as Open Data• Mapping Datasets to DBPedia with the GraphDB Lucene Connector• Tracing Panama Papers entities in the news
Aug 2016
Relationship Discovery Webinar
Offshore control exampleQuery: Find companies, which control other companies in the same country, through company in an off-shore zone
How it works:
1. Establish control-relationship
2. Establish a company-country mapping good for the purpose
3. Establish an “off-shore criteria”
4. SPARQL it
Aug 2016
Relationship Discovery Webinar
Off-shore company control exampleSELECT *FROM onto:disable-sameAsWHERE { ?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 .}
Aug 2016
Relationship Discovery Webinar
Presentation Outline
• Use cases: Relation discovery and Media monitoring• FactForge-News open-data playground• Relationship Discovery Examples• Media Monitoring Examples• Panama Papers and Global Legal Entity Identifier as Open Data• Mapping Datasets to DBPedia with the GraphDB Lucene Connector• Tracing Panama Papers entities in the news
Aug 2016
Relationship Discovery Webinar
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)
Aug 2016
Relationship Discovery Webinar
Media Monitoring QueriesF5: 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
Aug 2016
Relationship Discovery Webinar
News Popularity Ranking: Automotive
Rank Company News # Rank Company incl. mentions of child companies News #
1 General Motors 2722 1 General Motors 46202 Tesla Motors 2346 2 Volkswagen Group 39993 Volkswagen 2299 3 Fiat Chrysler Automobiles 26584 Ford Motor Company 1934 4 Tesla Motors 23705 Toyota 1325 5 Ford Motor Company 21256 Chevrolet 1264 6 Toyota 16567 Chrysler 1054 7 Renault-Nissan Alliance 13328 Fiat Chrysler Automobiles 1011 8 Honda 8649 Audi AG 972 9 BMW 715
10 Honda 717 10 Takata Corporation 547
Aug 2016
Relationship Discovery Webinar
News Popularity: Finance
Rank Company News # Rank Company incl. mentions of controlled News #1 Bloomberg L.P. 3203 1 Intra Bank 2616672 Goldman Sachs 1992 2 Hinduja Bank (Switzerland) 497313 JP Morgan Chase 1712 3 China Merchants Bank 382884 Wells Fargo 1688 4 Alphabet Inc. 226015 Citigroup 1557 5 Capital Group Companies 40766 HSBC Holdings 1546 6 Bloomberg L.P. 36117 Deutsche Bank 1414 7 Exor 27048 Bank of America 1335 8 Nasdaq, Inc. 20829 Barclays 1260 9 JP Morgan Chase 1972
10 UBS 694 10 Sentinel Capital Partners 1053
Note: Including investment funds, stock exchanges, agencies, etc.
Aug 2016
Relationship Discovery Webinar
News Popularity: Banking
Rank Company News # Rank Company incl. mentions of controlled News #1 Goldman Sachs 996 1 China Merchants Bank * 382882 JP Morgan Chase 856 2 JP Morgan Chase 19723 HSBC Holdings 773 3 Goldman Sachs 10304 Deutsche Bank 707 4 HSBC 9665 Barclays 630 5 Bank of America 7716 Citigroup 519 6 Deutsche Bank 7427 Bank of America 445 7 Barclays 6818 Wells Fargo 422 8 Citigroup 6309 UBS 347 9 Wells Fargo 428
10 Chase 126 10 UBS 347
Aug 2016
Using FIBO and Open Data to Discover Relationships #22
Relations extracted from text
Apr 2016
Subject Object Countdbr:Chrysler dbr:Fiat_Chrysler_Automobiles 455
dbr:NASA dbr:Goddard_Space_Flight_Center 69
dbr:Time_Warner_Cable dbr:Comcast 44
dbr:National_Football_League dbr:New_England_Patriots 40
dbr:DirecTV dbr:AT&T 33
dbr:Alcatel-Lucent dbr:Nokia 31
dbr:AOL dbr:Verizon_Communications 30
dbr:University_of_Pennsylvania dbr:Perelman_School_of_Medicine_at_... UPEN 29
dbr:Time_Warner_Cable dbr:Charter_Communications 27
dbr:Continental_Airlines dbr:United_Airlines 26
Note: relation types "RelationOrganizationAffiliatedWithOrganization" "RelationAcquisition" "RelationMerger"
Relationship Discovery Webinar
Presentation Outline
• Use cases: Relation discovery and Media monitoring• FactForge-News open-data playground• Relationship Discovery Examples• Media Monitoring Examples• Panama Papers and Global Legal Entity Identifier as Open Data• Mapping Datasets to DBPedia with the GraphDB Lucene Connector• Tracing Panama Papers entities in the news
Aug 2016
Relationship Discovery Webinar
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 DBPediaAug 2016
Relationship Discovery Webinar
GLEI Company Data Sample: ABN-AMRO
Aug 2016
lei:businessRegistry "Kamer van Koophandel"^^xsd:string
lei:businessRegistryNumber "34334259"^^xsd:string
lei:duplicateReference data:549300T5O0D0T4V2ZB28
lei:entityStatus "ACTIVE"^^xsd:string
lei:headquartersCity "Amsterdam"^^xsd:string
lei:headquartersState "Noord-Holland"^^xsd:string
lei:legalForm "NAAMLOZE VENNOOTSCHAP"^^xsd:string
lei:legalName "ABN AMRO Bank N.V."^^xsd:string
lei:lei "BFXS5XCH7N0Y05NIXW11"^^xsd:string
lei:registeredCity "Amsterdam"^^xsd:string
lei:registeredCountry "NL"^^xsd:string
lei:registeredPostCode "1082 PP"^^xsd:string
lei:registeredState "Noord-Holland"^^xsd:string
Relationship Discovery Webinar
Global Legal Entity Identifier (GLEI) data
Aug 2016
Ultimate parent Children Country1 The Goldman Sachs Group, Inc. 1 851 US2 United Technologies Corporation 427 US3 Honeywell International Inc. 341 US4 Morgan Stanley 228 US5 Cargill, Incorporated 217 US6 1832 Asset Management L.P. 202 CA7 Aegon N.V. 174 NL8 Union Bancaire Privée, UBP SA 138 CH9 Citigroup Inc. 135 US
10 State Street Corporation 128 US
Country Companies1 dbr:United_States 103 5482 dbr:Canada 17 4253 dbr:Luxembourg 13 9844 dbr:Sweden 7 9345 dbr:United_Kingdom 7 4216 dbr:Belgium 6 8687 dbr:Ireland 4 7628 dbr:Australia 4 3859 dbr:Germany 3 039
10 dbr:Netherlands 2 561
Relationship Discovery Webinar
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/
Aug 2016
Relationship Discovery Webinar
Offshore Leaks Database
Aug 2016
Relationship Discovery Webinar
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 DISCLAIMERSWe 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.
Aug 2016
Relationship Discovery Webinar
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)
Aug 2016
Relationship Discovery Webinar
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.
Aug 2016
Relationship Discovery Webinar
Sample queries at http://data.ontotext.comQ1: 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
Aug 2016
Relationship Discovery Webinar
Presentation Outline
• Use cases: Relation discovery and Media monitoring• FactForge-News open-data playground• Relationship Discovery Examples• Media Monitoring Examples• Panama Papers and Global Legal Entity Identifier as Open Data• Mapping Datasets to DBPedia with the GraphDB Lucene Connector• Tracing Panama Papers entities in the news
Aug 2016
Relationship Discovery Webinar
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
Aug 2016
Relationship Discovery Webinar
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
Aug 2016
Relationship Discovery Webinar
#LinkedLeaks Mapping Queries
Companies mapped by industry
Companies mapped in the Finance sector
Politicians mapped
Available as Saved Queries at http://ff-news.ontotext.com/sparql
Note 1: Open Saved Queries with the folder icon in the upper-right corner
Note 2: FF-NEWS is still in Beta testing ! But available to play with
Aug 2016
Relationship Discovery Webinar
Presentation Outline
• Use cases: Relation discovery and Media monitoring• FactForge-News open-data playground• Relationship Discovery Examples• Media Monitoring Examples• Panama Papers and Global Legal Entity Identifier as Open Data• Mapping Datasets to DBPedia with the GraphDB Lucene Connector• Tracing Panama Papers entities in the news
Aug 2016
Relationship Discovery Webinar
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 costAug 2016
Relationship Discovery Webinar
Thank you!
Experience the technology with NOW: Semantic News Portalhttp://now.ontotext.com
Play with open data at
http://data.ontotext.com and http://ff-news.ontotext.com
Aug 2016
