[webinar] factforge debuts: trump world data and instant ranking of industry leaders

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FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

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Page 1: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

FactForge Debuts Trump World Data and

Instant Ranking of IndustryLeaders

Presentation Outline

bull Demo of Rankontotextcom

bull Introduce FactForge and NOW

bull Explain Rank

bull Demonstration on Trump World Data

Use cases Relation discoveryand Media monitoring

Commercial Company

Database

(eg DampB)

Link dataReveal more

Social Media

News

Wikipedia

Privatebull Link diverse data in a

Knowledge Graph

bull Analyze News and Social Content

bull Extract facts and link content to data

bull Interpret data in context of big linked data

Content Analytics amp Exploration Platform

GraphDB Linked Open Data

Relation Discovery Case

bull Find suspicious relationships like

minus Company in USA

minus Controls another company in USA

minus Through a company in an off-shore zone

bull Show news relevant to these companies

Linking News to Big Knowledge Graphs

bull The DSP platform links text to knowledge graphs

bull One can navigate from news to concepts entities and topics and from there to other news

Try it at httpnowontotextcom

Semantic Media MonitoringFor each entity

bull popularity trends

bull relevant news

bull related entities

bull knowledge graph information

Try it at httpnowontotextcom

GraphDB OntoRefine

conversion of tabular

data in RDF

OntoRefine Data Transformation to RDF

bull Based on OpenRefine and integrated in the GraphDB Workbench

bull Allows converting tabular data into RDF

minus Supported formats are TSV CSV SV XLS XLSX JSON XML RDF as XML and Google sheet

minus Easily filter your data edit its inconsistencies

minus View the cleaned data as RDF

bull Exposes a GraphDB SPARQL endpoint

minus Transform your data using SPIN functions

minus Import your data straight into a GraphDB repository

The Power of Semantic Technologies to Explore Linked Open Data 10

FactForge Open data and news about people and organizations

httpfactforgenet

FactForge Data Integration

DBpedia (the English version) 496M

Geonames (all geographic features on Earth) 150M

owlsameAs 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 articlesday enriched by NOW) gt 600M

Total size (1313M explicit + 327M inferred statements) 1 640М

News Metadata

bull Metadata from Ontotextrsquos Dynamic Semantic Publishing platform

minus News stream from Google

minus Automatically generated as part of the NOWontotextcom semantic news showcase

bull News stream from Google since Feb 2015 about 50k newsmonthminus 700 000 news articles

minus ~70 tags (annotations) per news article 43M tags all together

minus 400 000 unique entities mentioned

bull Tags link text mentions of concepts to the knowledge graph

minus Technically these are URIs for entities (people organizations locations etc) and key phrases

Relationship Discovery Examples

Offshore control example

bull Query Find companies which control other companies in the same country through company in an off-shore zone

bull How it works

bull Establish control-relationship

bull Establish a company-country mapping

bull Establish an ldquooff-shore criteriardquo

bull SPARQL it

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 2: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

Presentation Outline

bull Demo of Rankontotextcom

bull Introduce FactForge and NOW

bull Explain Rank

bull Demonstration on Trump World Data

Use cases Relation discoveryand Media monitoring

Commercial Company

Database

(eg DampB)

Link dataReveal more

Social Media

News

Wikipedia

Privatebull Link diverse data in a

Knowledge Graph

bull Analyze News and Social Content

bull Extract facts and link content to data

bull Interpret data in context of big linked data

Content Analytics amp Exploration Platform

GraphDB Linked Open Data

Relation Discovery Case

bull Find suspicious relationships like

minus Company in USA

minus Controls another company in USA

minus Through a company in an off-shore zone

bull Show news relevant to these companies

Linking News to Big Knowledge Graphs

bull The DSP platform links text to knowledge graphs

bull One can navigate from news to concepts entities and topics and from there to other news

Try it at httpnowontotextcom

Semantic Media MonitoringFor each entity

bull popularity trends

bull relevant news

bull related entities

bull knowledge graph information

Try it at httpnowontotextcom

GraphDB OntoRefine

conversion of tabular

data in RDF

OntoRefine Data Transformation to RDF

bull Based on OpenRefine and integrated in the GraphDB Workbench

bull Allows converting tabular data into RDF

minus Supported formats are TSV CSV SV XLS XLSX JSON XML RDF as XML and Google sheet

minus Easily filter your data edit its inconsistencies

minus View the cleaned data as RDF

bull Exposes a GraphDB SPARQL endpoint

minus Transform your data using SPIN functions

minus Import your data straight into a GraphDB repository

The Power of Semantic Technologies to Explore Linked Open Data 10

FactForge Open data and news about people and organizations

httpfactforgenet

FactForge Data Integration

DBpedia (the English version) 496M

Geonames (all geographic features on Earth) 150M

owlsameAs 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 articlesday enriched by NOW) gt 600M

Total size (1313M explicit + 327M inferred statements) 1 640М

News Metadata

bull Metadata from Ontotextrsquos Dynamic Semantic Publishing platform

minus News stream from Google

minus Automatically generated as part of the NOWontotextcom semantic news showcase

bull News stream from Google since Feb 2015 about 50k newsmonthminus 700 000 news articles

minus ~70 tags (annotations) per news article 43M tags all together

minus 400 000 unique entities mentioned

bull Tags link text mentions of concepts to the knowledge graph

minus Technically these are URIs for entities (people organizations locations etc) and key phrases

Relationship Discovery Examples

Offshore control example

bull Query Find companies which control other companies in the same country through company in an off-shore zone

bull How it works

bull Establish control-relationship

bull Establish a company-country mapping

bull Establish an ldquooff-shore criteriardquo

bull SPARQL it

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 3: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

Use cases Relation discoveryand Media monitoring

Commercial Company

Database

(eg DampB)

Link dataReveal more

Social Media

News

Wikipedia

Privatebull Link diverse data in a

Knowledge Graph

bull Analyze News and Social Content

bull Extract facts and link content to data

bull Interpret data in context of big linked data

Content Analytics amp Exploration Platform

GraphDB Linked Open Data

Relation Discovery Case

bull Find suspicious relationships like

minus Company in USA

minus Controls another company in USA

minus Through a company in an off-shore zone

bull Show news relevant to these companies

Linking News to Big Knowledge Graphs

bull The DSP platform links text to knowledge graphs

bull One can navigate from news to concepts entities and topics and from there to other news

Try it at httpnowontotextcom

Semantic Media MonitoringFor each entity

bull popularity trends

bull relevant news

bull related entities

bull knowledge graph information

Try it at httpnowontotextcom

GraphDB OntoRefine

conversion of tabular

data in RDF

OntoRefine Data Transformation to RDF

bull Based on OpenRefine and integrated in the GraphDB Workbench

bull Allows converting tabular data into RDF

minus Supported formats are TSV CSV SV XLS XLSX JSON XML RDF as XML and Google sheet

minus Easily filter your data edit its inconsistencies

minus View the cleaned data as RDF

bull Exposes a GraphDB SPARQL endpoint

minus Transform your data using SPIN functions

minus Import your data straight into a GraphDB repository

The Power of Semantic Technologies to Explore Linked Open Data 10

FactForge Open data and news about people and organizations

httpfactforgenet

FactForge Data Integration

DBpedia (the English version) 496M

Geonames (all geographic features on Earth) 150M

owlsameAs 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 articlesday enriched by NOW) gt 600M

Total size (1313M explicit + 327M inferred statements) 1 640М

News Metadata

bull Metadata from Ontotextrsquos Dynamic Semantic Publishing platform

minus News stream from Google

minus Automatically generated as part of the NOWontotextcom semantic news showcase

bull News stream from Google since Feb 2015 about 50k newsmonthminus 700 000 news articles

minus ~70 tags (annotations) per news article 43M tags all together

minus 400 000 unique entities mentioned

bull Tags link text mentions of concepts to the knowledge graph

minus Technically these are URIs for entities (people organizations locations etc) and key phrases

Relationship Discovery Examples

Offshore control example

bull Query Find companies which control other companies in the same country through company in an off-shore zone

bull How it works

bull Establish control-relationship

bull Establish a company-country mapping

bull Establish an ldquooff-shore criteriardquo

bull SPARQL it

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 4: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

Commercial Company

Database

(eg DampB)

Link dataReveal more

Social Media

News

Wikipedia

Privatebull Link diverse data in a

Knowledge Graph

bull Analyze News and Social Content

bull Extract facts and link content to data

bull Interpret data in context of big linked data

Content Analytics amp Exploration Platform

GraphDB Linked Open Data

Relation Discovery Case

bull Find suspicious relationships like

minus Company in USA

minus Controls another company in USA

minus Through a company in an off-shore zone

bull Show news relevant to these companies

Linking News to Big Knowledge Graphs

bull The DSP platform links text to knowledge graphs

bull One can navigate from news to concepts entities and topics and from there to other news

Try it at httpnowontotextcom

Semantic Media MonitoringFor each entity

bull popularity trends

bull relevant news

bull related entities

bull knowledge graph information

Try it at httpnowontotextcom

GraphDB OntoRefine

conversion of tabular

data in RDF

OntoRefine Data Transformation to RDF

bull Based on OpenRefine and integrated in the GraphDB Workbench

bull Allows converting tabular data into RDF

minus Supported formats are TSV CSV SV XLS XLSX JSON XML RDF as XML and Google sheet

minus Easily filter your data edit its inconsistencies

minus View the cleaned data as RDF

bull Exposes a GraphDB SPARQL endpoint

minus Transform your data using SPIN functions

minus Import your data straight into a GraphDB repository

The Power of Semantic Technologies to Explore Linked Open Data 10

FactForge Open data and news about people and organizations

httpfactforgenet

FactForge Data Integration

DBpedia (the English version) 496M

Geonames (all geographic features on Earth) 150M

owlsameAs 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 articlesday enriched by NOW) gt 600M

Total size (1313M explicit + 327M inferred statements) 1 640М

News Metadata

bull Metadata from Ontotextrsquos Dynamic Semantic Publishing platform

minus News stream from Google

minus Automatically generated as part of the NOWontotextcom semantic news showcase

bull News stream from Google since Feb 2015 about 50k newsmonthminus 700 000 news articles

minus ~70 tags (annotations) per news article 43M tags all together

minus 400 000 unique entities mentioned

bull Tags link text mentions of concepts to the knowledge graph

minus Technically these are URIs for entities (people organizations locations etc) and key phrases

Relationship Discovery Examples

Offshore control example

bull Query Find companies which control other companies in the same country through company in an off-shore zone

bull How it works

bull Establish control-relationship

bull Establish a company-country mapping

bull Establish an ldquooff-shore criteriardquo

bull SPARQL it

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 5: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

Content Analytics amp Exploration Platform

GraphDB Linked Open Data

Relation Discovery Case

bull Find suspicious relationships like

minus Company in USA

minus Controls another company in USA

minus Through a company in an off-shore zone

bull Show news relevant to these companies

Linking News to Big Knowledge Graphs

bull The DSP platform links text to knowledge graphs

bull One can navigate from news to concepts entities and topics and from there to other news

Try it at httpnowontotextcom

Semantic Media MonitoringFor each entity

bull popularity trends

bull relevant news

bull related entities

bull knowledge graph information

Try it at httpnowontotextcom

GraphDB OntoRefine

conversion of tabular

data in RDF

OntoRefine Data Transformation to RDF

bull Based on OpenRefine and integrated in the GraphDB Workbench

bull Allows converting tabular data into RDF

minus Supported formats are TSV CSV SV XLS XLSX JSON XML RDF as XML and Google sheet

minus Easily filter your data edit its inconsistencies

minus View the cleaned data as RDF

bull Exposes a GraphDB SPARQL endpoint

minus Transform your data using SPIN functions

minus Import your data straight into a GraphDB repository

The Power of Semantic Technologies to Explore Linked Open Data 10

FactForge Open data and news about people and organizations

httpfactforgenet

FactForge Data Integration

DBpedia (the English version) 496M

Geonames (all geographic features on Earth) 150M

owlsameAs 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 articlesday enriched by NOW) gt 600M

Total size (1313M explicit + 327M inferred statements) 1 640М

News Metadata

bull Metadata from Ontotextrsquos Dynamic Semantic Publishing platform

minus News stream from Google

minus Automatically generated as part of the NOWontotextcom semantic news showcase

bull News stream from Google since Feb 2015 about 50k newsmonthminus 700 000 news articles

minus ~70 tags (annotations) per news article 43M tags all together

minus 400 000 unique entities mentioned

bull Tags link text mentions of concepts to the knowledge graph

minus Technically these are URIs for entities (people organizations locations etc) and key phrases

Relationship Discovery Examples

Offshore control example

bull Query Find companies which control other companies in the same country through company in an off-shore zone

bull How it works

bull Establish control-relationship

bull Establish a company-country mapping

bull Establish an ldquooff-shore criteriardquo

bull SPARQL it

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 6: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

Relation Discovery Case

bull Find suspicious relationships like

minus Company in USA

minus Controls another company in USA

minus Through a company in an off-shore zone

bull Show news relevant to these companies

Linking News to Big Knowledge Graphs

bull The DSP platform links text to knowledge graphs

bull One can navigate from news to concepts entities and topics and from there to other news

Try it at httpnowontotextcom

Semantic Media MonitoringFor each entity

bull popularity trends

bull relevant news

bull related entities

bull knowledge graph information

Try it at httpnowontotextcom

GraphDB OntoRefine

conversion of tabular

data in RDF

OntoRefine Data Transformation to RDF

bull Based on OpenRefine and integrated in the GraphDB Workbench

bull Allows converting tabular data into RDF

minus Supported formats are TSV CSV SV XLS XLSX JSON XML RDF as XML and Google sheet

minus Easily filter your data edit its inconsistencies

minus View the cleaned data as RDF

bull Exposes a GraphDB SPARQL endpoint

minus Transform your data using SPIN functions

minus Import your data straight into a GraphDB repository

The Power of Semantic Technologies to Explore Linked Open Data 10

FactForge Open data and news about people and organizations

httpfactforgenet

FactForge Data Integration

DBpedia (the English version) 496M

Geonames (all geographic features on Earth) 150M

owlsameAs 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 articlesday enriched by NOW) gt 600M

Total size (1313M explicit + 327M inferred statements) 1 640М

News Metadata

bull Metadata from Ontotextrsquos Dynamic Semantic Publishing platform

minus News stream from Google

minus Automatically generated as part of the NOWontotextcom semantic news showcase

bull News stream from Google since Feb 2015 about 50k newsmonthminus 700 000 news articles

minus ~70 tags (annotations) per news article 43M tags all together

minus 400 000 unique entities mentioned

bull Tags link text mentions of concepts to the knowledge graph

minus Technically these are URIs for entities (people organizations locations etc) and key phrases

Relationship Discovery Examples

Offshore control example

bull Query Find companies which control other companies in the same country through company in an off-shore zone

bull How it works

bull Establish control-relationship

bull Establish a company-country mapping

bull Establish an ldquooff-shore criteriardquo

bull SPARQL it

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 7: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

Linking News to Big Knowledge Graphs

bull The DSP platform links text to knowledge graphs

bull One can navigate from news to concepts entities and topics and from there to other news

Try it at httpnowontotextcom

Semantic Media MonitoringFor each entity

bull popularity trends

bull relevant news

bull related entities

bull knowledge graph information

Try it at httpnowontotextcom

GraphDB OntoRefine

conversion of tabular

data in RDF

OntoRefine Data Transformation to RDF

bull Based on OpenRefine and integrated in the GraphDB Workbench

bull Allows converting tabular data into RDF

minus Supported formats are TSV CSV SV XLS XLSX JSON XML RDF as XML and Google sheet

minus Easily filter your data edit its inconsistencies

minus View the cleaned data as RDF

bull Exposes a GraphDB SPARQL endpoint

minus Transform your data using SPIN functions

minus Import your data straight into a GraphDB repository

The Power of Semantic Technologies to Explore Linked Open Data 10

FactForge Open data and news about people and organizations

httpfactforgenet

FactForge Data Integration

DBpedia (the English version) 496M

Geonames (all geographic features on Earth) 150M

owlsameAs 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 articlesday enriched by NOW) gt 600M

Total size (1313M explicit + 327M inferred statements) 1 640М

News Metadata

bull Metadata from Ontotextrsquos Dynamic Semantic Publishing platform

minus News stream from Google

minus Automatically generated as part of the NOWontotextcom semantic news showcase

bull News stream from Google since Feb 2015 about 50k newsmonthminus 700 000 news articles

minus ~70 tags (annotations) per news article 43M tags all together

minus 400 000 unique entities mentioned

bull Tags link text mentions of concepts to the knowledge graph

minus Technically these are URIs for entities (people organizations locations etc) and key phrases

Relationship Discovery Examples

Offshore control example

bull Query Find companies which control other companies in the same country through company in an off-shore zone

bull How it works

bull Establish control-relationship

bull Establish a company-country mapping

bull Establish an ldquooff-shore criteriardquo

bull SPARQL it

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 8: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

Semantic Media MonitoringFor each entity

bull popularity trends

bull relevant news

bull related entities

bull knowledge graph information

Try it at httpnowontotextcom

GraphDB OntoRefine

conversion of tabular

data in RDF

OntoRefine Data Transformation to RDF

bull Based on OpenRefine and integrated in the GraphDB Workbench

bull Allows converting tabular data into RDF

minus Supported formats are TSV CSV SV XLS XLSX JSON XML RDF as XML and Google sheet

minus Easily filter your data edit its inconsistencies

minus View the cleaned data as RDF

bull Exposes a GraphDB SPARQL endpoint

minus Transform your data using SPIN functions

minus Import your data straight into a GraphDB repository

The Power of Semantic Technologies to Explore Linked Open Data 10

FactForge Open data and news about people and organizations

httpfactforgenet

FactForge Data Integration

DBpedia (the English version) 496M

Geonames (all geographic features on Earth) 150M

owlsameAs 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 articlesday enriched by NOW) gt 600M

Total size (1313M explicit + 327M inferred statements) 1 640М

News Metadata

bull Metadata from Ontotextrsquos Dynamic Semantic Publishing platform

minus News stream from Google

minus Automatically generated as part of the NOWontotextcom semantic news showcase

bull News stream from Google since Feb 2015 about 50k newsmonthminus 700 000 news articles

minus ~70 tags (annotations) per news article 43M tags all together

minus 400 000 unique entities mentioned

bull Tags link text mentions of concepts to the knowledge graph

minus Technically these are URIs for entities (people organizations locations etc) and key phrases

Relationship Discovery Examples

Offshore control example

bull Query Find companies which control other companies in the same country through company in an off-shore zone

bull How it works

bull Establish control-relationship

bull Establish a company-country mapping

bull Establish an ldquooff-shore criteriardquo

bull SPARQL it

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 9: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

GraphDB OntoRefine

conversion of tabular

data in RDF

OntoRefine Data Transformation to RDF

bull Based on OpenRefine and integrated in the GraphDB Workbench

bull Allows converting tabular data into RDF

minus Supported formats are TSV CSV SV XLS XLSX JSON XML RDF as XML and Google sheet

minus Easily filter your data edit its inconsistencies

minus View the cleaned data as RDF

bull Exposes a GraphDB SPARQL endpoint

minus Transform your data using SPIN functions

minus Import your data straight into a GraphDB repository

The Power of Semantic Technologies to Explore Linked Open Data 10

FactForge Open data and news about people and organizations

httpfactforgenet

FactForge Data Integration

DBpedia (the English version) 496M

Geonames (all geographic features on Earth) 150M

owlsameAs 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 articlesday enriched by NOW) gt 600M

Total size (1313M explicit + 327M inferred statements) 1 640М

News Metadata

bull Metadata from Ontotextrsquos Dynamic Semantic Publishing platform

minus News stream from Google

minus Automatically generated as part of the NOWontotextcom semantic news showcase

bull News stream from Google since Feb 2015 about 50k newsmonthminus 700 000 news articles

minus ~70 tags (annotations) per news article 43M tags all together

minus 400 000 unique entities mentioned

bull Tags link text mentions of concepts to the knowledge graph

minus Technically these are URIs for entities (people organizations locations etc) and key phrases

Relationship Discovery Examples

Offshore control example

bull Query Find companies which control other companies in the same country through company in an off-shore zone

bull How it works

bull Establish control-relationship

bull Establish a company-country mapping

bull Establish an ldquooff-shore criteriardquo

bull SPARQL it

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 10: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

OntoRefine Data Transformation to RDF

bull Based on OpenRefine and integrated in the GraphDB Workbench

bull Allows converting tabular data into RDF

minus Supported formats are TSV CSV SV XLS XLSX JSON XML RDF as XML and Google sheet

minus Easily filter your data edit its inconsistencies

minus View the cleaned data as RDF

bull Exposes a GraphDB SPARQL endpoint

minus Transform your data using SPIN functions

minus Import your data straight into a GraphDB repository

The Power of Semantic Technologies to Explore Linked Open Data 10

FactForge Open data and news about people and organizations

httpfactforgenet

FactForge Data Integration

DBpedia (the English version) 496M

Geonames (all geographic features on Earth) 150M

owlsameAs 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 articlesday enriched by NOW) gt 600M

Total size (1313M explicit + 327M inferred statements) 1 640М

News Metadata

bull Metadata from Ontotextrsquos Dynamic Semantic Publishing platform

minus News stream from Google

minus Automatically generated as part of the NOWontotextcom semantic news showcase

bull News stream from Google since Feb 2015 about 50k newsmonthminus 700 000 news articles

minus ~70 tags (annotations) per news article 43M tags all together

minus 400 000 unique entities mentioned

bull Tags link text mentions of concepts to the knowledge graph

minus Technically these are URIs for entities (people organizations locations etc) and key phrases

Relationship Discovery Examples

Offshore control example

bull Query Find companies which control other companies in the same country through company in an off-shore zone

bull How it works

bull Establish control-relationship

bull Establish a company-country mapping

bull Establish an ldquooff-shore criteriardquo

bull SPARQL it

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 11: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

FactForge Open data and news about people and organizations

httpfactforgenet

FactForge Data Integration

DBpedia (the English version) 496M

Geonames (all geographic features on Earth) 150M

owlsameAs 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 articlesday enriched by NOW) gt 600M

Total size (1313M explicit + 327M inferred statements) 1 640М

News Metadata

bull Metadata from Ontotextrsquos Dynamic Semantic Publishing platform

minus News stream from Google

minus Automatically generated as part of the NOWontotextcom semantic news showcase

bull News stream from Google since Feb 2015 about 50k newsmonthminus 700 000 news articles

minus ~70 tags (annotations) per news article 43M tags all together

minus 400 000 unique entities mentioned

bull Tags link text mentions of concepts to the knowledge graph

minus Technically these are URIs for entities (people organizations locations etc) and key phrases

Relationship Discovery Examples

Offshore control example

bull Query Find companies which control other companies in the same country through company in an off-shore zone

bull How it works

bull Establish control-relationship

bull Establish a company-country mapping

bull Establish an ldquooff-shore criteriardquo

bull SPARQL it

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 12: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

FactForge Data Integration

DBpedia (the English version) 496M

Geonames (all geographic features on Earth) 150M

owlsameAs 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 articlesday enriched by NOW) gt 600M

Total size (1313M explicit + 327M inferred statements) 1 640М

News Metadata

bull Metadata from Ontotextrsquos Dynamic Semantic Publishing platform

minus News stream from Google

minus Automatically generated as part of the NOWontotextcom semantic news showcase

bull News stream from Google since Feb 2015 about 50k newsmonthminus 700 000 news articles

minus ~70 tags (annotations) per news article 43M tags all together

minus 400 000 unique entities mentioned

bull Tags link text mentions of concepts to the knowledge graph

minus Technically these are URIs for entities (people organizations locations etc) and key phrases

Relationship Discovery Examples

Offshore control example

bull Query Find companies which control other companies in the same country through company in an off-shore zone

bull How it works

bull Establish control-relationship

bull Establish a company-country mapping

bull Establish an ldquooff-shore criteriardquo

bull SPARQL it

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 13: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

News Metadata

bull Metadata from Ontotextrsquos Dynamic Semantic Publishing platform

minus News stream from Google

minus Automatically generated as part of the NOWontotextcom semantic news showcase

bull News stream from Google since Feb 2015 about 50k newsmonthminus 700 000 news articles

minus ~70 tags (annotations) per news article 43M tags all together

minus 400 000 unique entities mentioned

bull Tags link text mentions of concepts to the knowledge graph

minus Technically these are URIs for entities (people organizations locations etc) and key phrases

Relationship Discovery Examples

Offshore control example

bull Query Find companies which control other companies in the same country through company in an off-shore zone

bull How it works

bull Establish control-relationship

bull Establish a company-country mapping

bull Establish an ldquooff-shore criteriardquo

bull SPARQL it

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 14: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

Relationship Discovery Examples

Offshore control example

bull Query Find companies which control other companies in the same country through company in an off-shore zone

bull How it works

bull Establish control-relationship

bull Establish a company-country mapping

bull Establish an ldquooff-shore criteriardquo

bull SPARQL it

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 15: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

Offshore control example

bull Query Find companies which control other companies in the same country through company in an off-shore zone

bull How it works

bull Establish control-relationship

bull Establish a company-country mapping

bull Establish an ldquooff-shore criteriardquo

bull SPARQL it

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 16: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

Off-shore company control example

SELECT FROM ontodisable-sameAsWHERE

c1 fibo-fnd-rel-relcontrols c2 c2 fibo-fnd-rel-relcontrols c3 c1 ff-maporgCountry c1_country c2 ff-maporgCountry c2_country c3 ff-maporgCountry c1_country

FILTER (c1_country = c2_country) c2_country ff-maphasOffshoreProvisions true

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 17: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

Company Popularity Ranking

httprankontotextcom

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 18: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

News popularity ranking of companies

bull Rankings can be customized by specifying a geographic region news category (eg business sport lifestyle etc) and time period

bull Unique features

minus It is based on live streaming news

minus Tracks also mentions of subsidiaries

bull Rank uses the industry sectors of DBPedia with several refinements

minus About 40 top-industry sectors

minus Sectors are linked in a hierarchical taxonomy (all together 251 sectors)

minus Industry sectors are de-duplicated (all designators used in Wikipedia are about 9 000)

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 19: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

Rank uses NOW FactForge and GraphDB

bull This ranking service is entirely based on FactForge

minus FactForge allows public exploration and querying of a knowledge graph of more than 1 billion facts which is loaded in GraphDB

minus GraphDB is a semantic graph database engine of Ontotext

minus Unlike FactForge this service is aimed at non-technical users as it does not require any knowledge of SPARQL or other technology

minus But it allows users to see the SPARQL query for each ranking and to customize it

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 20: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

Trump World DataRDF-ization and Analytics

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet

Page 21: [Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry Leaders

Thank you

Experience the technology with RANK

httprankontotextcom df httpnowontotextcom

and play with open data at

httpfactforgenet