[webinar] factforge debuts: trump world data and instant ranking of industry leaders
TRANSCRIPT
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Trump World DataRDF-ization and Analytics
Thank you
Experience the technology with RANK
httprankontotextcom df httpnowontotextcom
and play with open data at
httpfactforgenet
Thank you
Experience the technology with RANK
httprankontotextcom df httpnowontotextcom
and play with open data at
httpfactforgenet