Presentation at Semantics 2017, Sep 2017, Amsterdam. By Atanas Kiryakov, Vladimir Alexiev, Plamen Tarkalanov

1. euBusinessGraph
Company and
Economic Data
Semantics Conference, Sep 2017

2. Presentation Outline
•Ontotext Introduction
•euBusinesGraph
•FactForge: Open data and news about people and organizations
•Relationship Discovery Examples
•Media Monitoring Examples & Popularity Ranking
•Global Legal Entity Identifier RDF-ization and DBPedia mapping
Sep 2017euBusinessGraph Company and Economic Data

3. Ontotext Introduction

4. History and Essential Facts
• Started in year 2000 as Semantic Web pioneer
− As R&D lab within Sirma – one of the biggest Bulgarian software companies
− Got spun-off and took VC investment in 2008
• 65 staff, R&D Center at Sofia; 80% sales in USA and UK
− Serving BBC, FT, Springer Nature, Wiley, Elsevier, OUP, IET…
• 400+ person-years invested in R&D
− Multiple innovation & technology awards: Washington Post, BBC, FT, BAIT, etc.
• Member of multiple industry bodies
− W3C, EDMC, ODI, LDBC, STI, DBPedia Foundation
euBusinessGraph Company and Economic Data Sep 2017

5. Commercial Company
Database
(e.g. D&B)
Link data!
Reveal more!
Social Media
News
Wikipedia
Private• Recognizing and linking
entities across text and
data requires knowledge
and context
• Knowledge Graphs
incorporate semantic
entity fingerprints for
entities and concepts
• Evolve knowledge graphs
and interlink them with
proprietary data
Sep 2017euBusinessGraph Company and Economic Data

6. Sep 2017euBusinessGraph Company and Economic Data

7. NOW: Linking News to Big Knowledge Graphs
• The Ontotext
platform
links text to
knowledge
graphs
• Navigate
from news to
concepts,
entities and
topics; from
there to other
news
Try it at
http://now.ontotext.com Sep 2017

8. Ontotext Portfolio
Sep 2017euBusinessGraph Company and Economic Data

9. Technology Excellence Delivered
• Powerful technology mix: Graph DB engine + Text mining
• Robust technology: We run BBC.CO.UK/SPORT and parts of FT.COM
• We serve some of the most knowledge intensive enterprises:
Sep 2017euBusinessGraph Company and Economic Data

10. euBusinessGraph

11. Sep 2017euBusinessGraph Company and Economic Data
• Integrate European company and economic data
• euBusinessGraph will overcome barriers in company data provisioning
• Technology and research partners: SINTEF (coord.), Ontotext, IJS, Uni. Milano

12. Sep 2017euBusinessGraph Company and Economic Data
euBusinessGraph

13. Sep 2017euBusinessGraph Company and Economic Data
• Global Legal Entity Identifier (GLEI)
• Business Registers Interconnection System (BRIS)
• Financial Industry Business Ontology (FIBO)
• OpenCorporates schema
• Bulgarian Trade Register schema
• W3C: Organization ontology, Registered Organization ontology, Location ontology
• Investigative journalism datasets: Panama Papers dataset, Linked Leaks, Trump World dataset
• Wikidata properties for describing companies, especially company identifiers in various registers
• Other ontologies and code lists: Schema.org, Dublin Core, IANA language tags, NUTS and LAU (EU
administrative regions), NACE (EU economic activities), etc.
Company Datasets and Ontologies

14. Sep 2017euBusinessGraph Company and Economic Data
• The semantic data model combines
various data artefacts
− Includes detailed treatment of classes,
properties, values, scope notes, data provider
rules, URL conventions, etc.
• Tools:
− rdfpuml used to generate the diagrams
− Object-Role Modeling through the Norma
euBusinessGraph Semantic Data Model

15. Sep 2017euBusinessGraph Company and Economic Data
Object-Role
Diagram of
Part of the
Semantic
Model

16. Sep 2017euBusinessGraph Company and Economic Data
euBusinessGraph Technologies
• Ontotext Cognitive
Cloud & GraphDB
• DataGraft
• Dandelion API
• Wikifier
• ABSTAT
• TARQL
• XSPARQL

17. FactForge: Open data and
news about people and
organizations
http://factforge.net

18. 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 (1611M explicit + 328M inferred statements) 1 939М
Sep 2017euBusinessGraph Company and Economic Data

19. 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
Sep 2017euBusinessGraph Company and Economic Data

20. 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
Sep 2017euBusinessGraph Company and Economic Data

21. Class Hierarchy Map (by number of instances)
Left: The big picture
Right: dbo:Agent class (2.7M organizations and persons)
Sep 2017euBusinessGraph Company and Economic Data

22. 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
Sep 2017euBusinessGraph Company and Economic Data

23. Relationship Discovery
Examples

24. 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
Sep 2017euBusinessGraph Company and Economic Data

25. 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
Sep 2017euBusinessGraph Company and Economic Data

26. 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 .
}
Sep 2017euBusinessGraph Company and Economic Data

27. Media Monitoring
Examples
Sep 2017euBusinessGraph Company and Economic Data

28. Semantic Media Monitoring
For each
entity:
•popularity
trends
•relevant
news
•related
entities
•knowledge
graph
information
Try it at http://now.ontotext.com
Sep 2017euBusinessGraph Company and Economic Data

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)
Sep 2017euBusinessGraph Company and Economic Data

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
Sep 2017euBusinessGraph Company and Economic Data

31. 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
Sep 2017euBusinessGraph Company and Economic Data

32. News popularity ranking of companies
• Rankings can be customized by specifying a geographic region, news
category (e.g., business, sport, lifestyle, etc.) and time period.
• Unique features:
− It is based on live streaming news
− Tracks also mentions of subsidiaries
• Rank uses the industry sectors of DBPedia with several refinements
− About 40 top-industry sectors
− Sectors are linked in a hierarchical taxonomy (all together 251 sectors)
− Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

33. Rank uses NOW, FactForge and GraphDB
• This ranking service is entirely based on FactForge
− FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts,
which is loaded in GraphDB
− GraphDB is a semantic graph database engine of Ontotext
− Unlike FactForge, this service is aimed at non-technical users as it does not require any knowledge
of SPARQL or other technology.
− But it allows users to see the SPARQL query for each ranking and to customize it
• Try http://rank.ontotext.com

34. rank.ontotext.com demonstrator
Try it at http://rank.ontotext.com Sep 2017euBusinessGraph Company and Economic Data

35. rank.ontotext.com demonstrator
Try it at http://rank.ontotext.com Sep 2017euBusinessGraph Company and Economic Data

36. Global Legal Entity
Identifier as Open Data

37. Global Legal Entity Identifier (GLEI) data
•Global Legal Entity Identifier Foundation (GLEIF) Utility data
−Global Legal Entity Identifier Foundation (GLEIF) is tasked to support the implementation and use
of the Legal Entity Identifier (LEI)
−The foundation is backed and overseen by the LEI Regulatory Oversight Committee
•The data dump
−We downloaded as XML data dump from
https://www.gleif.org/en/lei-data/gleif-concatenated-file/download-the-concatenated-file.
−We used these 2 provided dumps
‣ Level 1 Data (Who is who)
‣ Level 2 Data (Who Owns Whom)
Sep 2017euBusinessGraph Company and Economic Data

38. Global Legal Entity Identifier (GLEI) data
•RDF-ized company records
−20M explicit statements for 505 thousand organizations
▪ For comparison, there are 296,544 organizations in DBPeda and D&B covers 200+ million
▪ A year ago GLEI had only 3M statements about 211 thousand organizations
−9,105 parent/child relationships, 16,150 associated organization
•9 705 organizations from the GLEI mapped to DBPediа
•Modeling the company data to FIBO
•XSPARQL as the transformation engine
https://github.com/Ontotext-AD/GLEI
Sep 2017euBusinessGraph Company and Economic Data

39. GLEI Data Model
Sep 2017euBusinessGraph Company and Economic Data

40. 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
Sep 2017euBusinessGraph Company and Economic Data

41. 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
GLEI Data Stats: 2016 (OLD)
Sep 2017euBusinessGraph Company and Economic Data

42. GLEI Data Stats: 2017
Sep 2017euBusinessGraph Company and Economic Data
Ultimate Parent Children Country
1 LLOYDS BANKING GROUP PLC 619 GB
2 HSBC HOLDINGS PLC 542 GB
3 THE ROYAL BANK OF SCOTLAND … 378 GB
4 DEUTSCHE BANK AKTIENGESELLSCHAFT 174 DE
5 BANK OF SCOTLAND PLC 111 GB
6 LLOYDS BANK PLC 93 GB
7 Swedbank AB (Publ) 90 SE
8
ROYAL LONDON MUTUAL INSURANCE
SOCIETY,LIMITED(THE) 89 GB
9 Lincoln Investment Advisors Corporation 88 US
10 Swedbank Robur AB 85 SE
Country Companies
1 US 136 889
2 IT 50 021
3 DE 48 850
4 FR 33 412
5 GB 32 015
6 CA 22 107
7 LU 22 075
8 NL 20 327
9 ES 19 569
10 SE 11 272

43. Mapping Datasets to
DBPedia with the
GraphDB Lucene
Connector
Sep 2017euBusinessGraph Company and Economic Data

44. 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, the data from GLEI doesn’t contain any extra information about the companies,
e.g. industry sector, products, etc.
• Specific conditions: we had to map by names and locations
− There’re little features common for both for the GLEI and DBPedia data
▪ Address and country attributes are present, but those appeared to be marginally useful for mapping
− We mapped locations only in terms of countries and/or cities not finer grained locations
▪ For this purpose DBPedia geographic data is sufficient and it is also well mapped with GeoNames
Sep 2017euBusinessGraph Company and Economic Data

45. 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
Sep 2017euBusinessGraph Company and Economic Data

46. Mapping GLEI to DBPedia
• Data Pre-processing in DBPedia
− We generated primary city and primary country for each organization in DBPedia
▪ Also cleaned up data about HQ locations, etc.
▪ We used a series of SPARQL queries for this
• Iterative matching
− Match first those that have high relevance and match better constraints by location and country
• Matching outcome
− skos:exactMatch: 3880 matches
− skos:closeMatch: 5825 matches
Sep 2017euBusinessGraph Company and Economic Data

47. Thank you!
Experience the technology with our demonstrators
NOW: Semantic News Portal http://now.ontotext.com
RANK: News popularity ranking for companies http://rank.ontotext.com
FactForge: Hub for open data and news about People and Organizations
http://factforge.net
Sep 2017euBusinessGraph Company and Economic Data