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/.

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

4. Use cases: Relation
discovery
and Media monitoring

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

6. Content Analytics &
Exploration Platform
GraphDB Linked Open 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

10. GraphDB OntoRefine:
conversion of tabular
data in RDF

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

25. Relationship Discovery
Examples

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 .
}

28. Media Monitoring
Examples

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

34. Panama Papers and
Global Legal Entity
Identifier as Open Data

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/

39. Offshore
Leaks Database

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

44. Mapping Datasets to
DBPedia with the
GraphDB Lucene
Connector

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

48. Tracing Panama Papers
entities in the news

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