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Boost Your Data Analytics with Open
Data and Public News Content
Ontotext Webinar, 24 Mar 2016
Presentation Outline – PART I
• Quick news-analytics case
• Technology approach
• FactForge-News: Data architecture
• Sample queries on Linked Open Data
• News analytics examples
• Today’s News Map
Mar 2016Open Data & News Analytics 2
Quick news-analytics case
Mar 2016Open Data & News Analytics 3
• Our Dynamic Semantic
Publishing platform
already offers linking
of text with big open
data graphs
• One can get navigate
from text to concepts,
get trends, related
entities and news
• Try it at
http://now.ontotext.com
Presentation Outline
• Quick news-analytics case
• Technology approach
• FactForge-News: Data architecture
• Sample queries on Linked Open Data
• News analytics examples
• Today’s News Map
Mar 2016Open Data & News Analytics 4
Our approach to Big Data
1. Integrate relevant data from many sources
− Build a Big Knowledge Graph from proprietary databases and
taxonomies integrated with millions of facts of Linked Data
2. Infer new facts and unveil relationships
− Performing reasoning across data from different sources
3. Interlink text and with big data
− Using text-mining to automatically discover references to
concepts and entities
4. Use NoSQL graph database for metadata
management, querying and search
Mar 2016Open Data & News Analytics #5
NoSQL Graph
Database
Mar 2016Open Data & News Analytics 6
myData: Maria
ptop:Agent
ptop:Person
ptop:Woman
ptop:childOf
ptop:parentOf
rdfs:range
owl:inverseOf
inferred
myData:Ivan
owl:relativeOf
owl:inverseOfowl:SymmetricProperty
rdfs:subPropertyOf
owl:inverseOf
owl:inverseOf
rdf:type
rdf:type
rdf:type
• The hottest NoSQL trend
• W3C standards
• Efficient Data Integration
− Using logical inference
− For data integration and BI
Analyzing Text
Mar 2016Open Data & News Analytics 7
• Full spectrum of NLP
weaponry
• Semantic indexing
− Tag references with entity IDs
− Generate semantic metadata
descriptions of documents
− Store metadata in GraphDB
Presentation Outline
• Quick news-analytics case
• Technology approach
• FactForge-News: Data architecture
• Sample queries on Linked Open Data
• News analytics examples
• Today’s News Map
Mar 2016Open Data & News Analytics 8
The Web of Linked Data in 2007
Mar 2016Open Data & News Analytics #9
structured database
version of Wikipedia
database of all
locations on Earth
product
reviews
semantic synonym
dictionary
Note: Each bubble represents a dataset.
Arrows represent mappings across datasets;
e.g. dbpedia:Paris owl:sameAs geo:2988507
The Web of Linked Data is Gaining Mass
Mar 2016Open Data & News Analytics #10
The Web of Data is Gaining Mass (2011)
Mar 2016Open Data & News Analytics #11
The Web of Linked Data is Gaining Mass
Mar 2016Open Data & News Analytics #12
• 2013 stats: 2 289 public
datasets
− http://stats.lod2.eu/
• Growing exponentially
− see the dotted trend line
• Structured markup
− Schema.org; semantic SEO
• Enables better semantic
tagging!
− As there are more concepts and
richer descriptions to refer to
27 43 89 162
295
822
2,289
2007 2008 2009 2010 2011 2012 2013
LinkedDataDatasets
The FactForge Data
• DBpedia (the English version only): 496M statements
• Geonames: 150M statements
− SameAs links between DBpedia and Geonames: 471K statements
• NOW data – metadata about news: 128M statements
• Total size: 938М statements
− 656M explicit statements + 281M inferred statements
− RDFRank and geo-spatial indices enabled to allow for ranking and efficient geo-region constraints
Mar 2016Open Data & News Analytics 13
News Metadata
• Metadata from Ontotext’s Dynamic Semantic Publishing platform
− Automatically generated as part of the NOW.ontotext.com semantic news showcase
• News corpus from Google since Feb 2015, about 10k news/month
• ~70 tags (annotations) per news article
• Tags link text mentions of concepts to the knowledge graph
− Technically these are URIs for entities (people, organizations, locations, etc.) and key phrases
Mar 2016Open Data & News Analytics 14
News Metadata
Mar 2016Open Data & News Analytics 15
News Metadata
Mar 2016Open Data & News Analytics 16
Category Count
International 52 074
Science and Technology 23 201
Sports 20 714
Business 15 155
Lifestyle 11 684
122 828
Mentions / entity type Count
Keyphrase 2 589 676
Organization 1 276 441
Location 1 260 972
Person 1 248 784
Work 309 093
Event 258 388
RelationPersonRole 236 638
Species 180 946
News Geographic Coverage
Mar 2016Open Data & News Analytics 17
• Quite focused on USA!
Class Hierarchy Map (by number of instances)
Mar 2016Open Data & News Analytics 18
Left: The big picture
Right: dbo:Agent class (2.7M organizations and persons)
Presentation Outline
• Quick news-analytics case
• Technology approach
• FactForge-News: Data architecture
• Sample queries on Linked Open Data
• News analytics examples
• Today’s News Map
Mar 2016Open Data & News Analytics 19
Sample queries
• There is a rich set of sample queries that allow exploration of this
combination of DBPedia, GeoNames and news metadata
• We will showcase few of those, starting from the simple once
• In bold we marked the “parameters” of the queires
Mar 2016Open Data & News Analytics 20
Query: Big Cities in Eastern Europe
# benefits from inference over transitive gn:parentFeature
# benefits from owl:sameAs mapping between DBPedia and Geonames
PREFIX dbr: <http://dbpedia.org/resource/>
PREFIX onto: <http://www.ontotext.com/>
PREFIX gn: <http://www.geonames.org/ontology#>
PREFIX dbo: <http://dbpedia.org/ontology/>
select *
from onto:disable-sameAs
where {
?loc gn:parentFeature dbr:Eastern_Europe ; gn:featureClass gn:P.
?loc dbo:populationTotal ?population ; dbo:country ?country .
FILTER(?population > 300000 )
} order by ?country
Mar 2016Open Data & News Analytics 21
Query: People and Organizations related to Google
# benefits from inference over transitive dbo:parent
# RDFRank makes it easy to see the “top suspects” in a list of 93 entities
PREFIX dbo: <http://dbpedia.org/ontology/>
PREFIX rank: <http://www.ontotext.com/owlim/RDFRank#>
PREFIX dbr: <http://dbpedia.org/resource/>
select distinct ?related_entity ?rank
where {
BIND (dbr:Google as ?entity)
{ ?related_entity a dbo:Person ; ?p ?entity . } UNION
{ ?related_entity a dbo:Organisation ; dbo:parent ?entity . }
?related_entity rank:hasRDFRank ?rank
} order by desc(?rank)
Mar 2016Open Data & News Analytics 22
Query: Airports near London
# GraphDB’s geo-spatial plug-in allows efficient evaluation of near-by
# RDFRank brings the top 6 passanger airports at the top of a list of 80
PREFIX dbr: <http://dbpedia.org/resource/>
PREFIX geo-pos: <http://www.w3.org/2003/01/geo/wgs84_pos#>
PREFIX gdb-geo: <http://www.ontotext.com/owlim/geo#>
PREFIX dbo: <http://dbpedia.org/ontology/>
PREFIX gdb: <http://www.ontotext.com/owlim/>
SELECT distinct ?airport ?rrank
WHERE {
{ SELECT * { dbr:London geo-pos:lat ?lat ; geo-pos:long ?long . } LIMIT 10 }
?airport gdb-geo:nearby(?lat ?long "50mi");
a dbo:Airport ;
gdb:hasRDFRank ?rrank .
} ORDER BY DESC(?rrank)
Mar 2016Open Data & News Analytics 23
Query: Top-level Industries by number of companies
# benefits from mapping and consolidation of industry classifications
# and predicates in DBPedia (ff-map)
PREFIX dbo: <http://dbpedia.org/ontology/>
PREFIX ff-map: <http://factforge.net/ff2016-mapping/>
select distinct ?topIndustry (count(?company) as ?companies)
where {
?company dbo:industry ?industry .
?industrySum ff-map:industryVariant ?industry .
?industrySum ff-map:industryCenter ?topIndustry .
} group by ?topIndustry order by desc(?companies)
Mar 2016Open Data & News Analytics 24
Presentation Outline
• Quick news-analytics case
• Technology approach
• FactForge-News: Data architecture
• Sample queries on Linked Open Data
• News analytics examples
• Today’s News Map
Mar 2016Open Data & News Analytics 25
Semantic Press-Clipping
• We can trace references to a specific company in the news
− This is pretty much standard, however we can deal with syntactic variations in the names, because state
of the art Named Entity Recognition technology is used
− What’s more important, we distinguish correctly in which mention “Paris” refers to which of the
following: Paris (the capital of France), Paris in Texas, Paris Hilton or to Paris (the Greek hero)
• We can trace and consolidate references to daughter companies
• We have comprehensive industry classification
− The one from DBPedia, but refined to accommodate identifier variations and specialization (e.g.
company classified as dbr:Bank will also be considered classified as dbr:FinancialServices)
Mar 2016Open Data & News Analytics 26
Query: News Mentioning an IBM
# technical example to demonstrate how news metadata can be accessed
PREFIX pub-old: <http://ontology.ontotext.com/publishing#>
PREFIX pub: <http://ontology.ontotext.com/taxonomy/>
PREFIX dbr: <http://dbpedia.org/resource/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
select distinct ?news ?title ?date ?pub_entity
where {
?news pub-old:containsMention / pub-old:hasInstance ?pub_entity .
?pub_entity pub:exactMatch dbr:IBM .
?news pub-old:creationDate ?date; pub-old:title ?title .
FILTER ( (?date > "2015-10-01T00:02:00Z"^^xsd:dateTime) &&
(?date < "2015-11-01T00:02:00Z"^^xsd:dateTime))
} limit 100
Mar 2016Open Data & News Analytics 27
Query: News Mentioning Gazprom and Its Related Entities
# benefits from inference over transitive dbo:parent relation and mappings to it
select distinct ?news ?title ?date ?related_entity
where {
{ select distinct ?related_entity {
BIND (dbr:Gazprom as ?entity)
{ ?related_entity a dbo:Person ; ?p ?entity .
FILTER NOT EXISTS { ?related_entity dbo:club ?entity } } UNION
{ ?related_entity a dbo:Organisation ; dbo:parent ?entity . } UNION
{ BIND(?entity as ?related_entity) }
} }
?news pub-old:containsMention / pub-old:hasInstance ?pub_entity .
?pub_entity pub:exactMatch ?related_entity .
?news pub-old:creationDate ?date; pub-old:title ?title .
} order by desc(?date) limit 1000
Mar 2016Open Data & News Analytics 28
Query: Most Popular in the News Automotive Companies
# benefits from mapping and consolidation of industry classifications
select distinct ?pub_entity (max(?entity_label) as ?label)
(count(?news) as ?news_count)
where {
?news pub-old:containsMention / pub-old:hasInstance ?pub_entity .
?pub_entity pub:exactMatch ?entity; pub:preferredLabel ?entity_label.
dbr:Automotive ff-map:industryVariant ?industry .
?entity dbo:industry ?industry .
?news pub-old:creationDate ?date .
} group by ?pub_entity order by desc(?news_count)
Mar 2016Open Data & News Analytics 29
Query: Most Popular in the News, including children
# benefits from mapping and consolidation of industry classifications
select distinct ?parent (count(?news) as ?news_count)
where {
{ select distinct ?parent ?entity {
BIND(dbr:Software as ?industry)
?industry ff-map:industryVariant ?industryVar .
?parent dbo:industry ?industryVar .
?parent a dbo:Company .
FILTER NOT EXISTS { ?parent dbo:parent / dbo:industry / ff-map:industryVariant ?industry }
{ ?entity dbo:parent ?parent . } UNION
{ BIND(?parent as ?entity) }
} }
?news pub-old:containsMention / pub-old:hasInstance ?pub_entity .
?pub_entity pub:exactMatch ?entity .
?news pub-old:creationDate ?date .
} group by ?parent order by desc(?news_count)
Mar 2016Open Data & News Analytics 30
News Popularity Ranking: Automotive
Mar 2016Open Data & News Analytics 31
Rank Company News # Rank Company incl. mentions of controlled News #
1 General Motors 2722 1 General Motors 4620
2 Tesla Motors 2346 2 Volkswagen Group 3999
3 Volkswagen 2299 3 Fiat Chrysler Automobiles 2658
4 Ford Motor Company 1934 4 Tesla Motors 2370
5 Toyota 1325 5 Ford Motor Company 2125
6 Chevrolet 1264 6 Toyota 1656
7 Chrysler 1054 7 Renault-Nissan Alliance 1332
8 Fiat Chrysler Automobiles 1011 8 Honda 864
9 Audi AG 972 9 BMW 715
10 Honda 717 10 Takata Corporation 547
News Popularity: Finance
Mar 2016Open Data & News Analytics 32
Rank Company News # Rank Company incl. mentions of controlled News #
1 Bloomberg L.P. 3203 1 China Merchants Bank 40940
2 Goldman Sachs 1992 2 Alphabet Inc. 24219
3 JP Morgan Chase 1712 3 Capital Group Companies 4379
4 Wells Fargo 1688 4 Bloomberg L.P. 3893
5 Citigroup 1557 5 Exor (company) 2775
6 HSBC Holdings 1546 6 JP Morgan Chase 2715
7 Deutsche Bank 1414 7 Nasdaq, Inc. 2178
8 Bank of America 1335 8 Oaktree Capital Management 1757
9 Barclays 1260 9 Goldman Sachs 1085
10 UBS 694 10 Sentinel Capital Partners 1064
Note: Including investment funds, stock exchanges, agencies, etc.
News Popularity: Banking
Mar 2016Open Data & News Analytics 33
Rank Company News # Rank Company incl. mentions of controlled News #
1 Goldman Sachs 996 1 China Merchants Bank * 38288
2 JP Morgan Chase 856 2 JP Morgan Chase 1972
3 HSBC Holdings 773 3 Goldman Sachs 1030
4 Deutsche Bank 707 4 HSBC 966
5 Barclays 630 5 Bank of America 771
6 Citigroup 519 6 Deutsche Bank 742
7 Bank of America 445 7 Barclays 681
8 Wells Fargo 422 8 Citigroup 630
9 UBS 347 9 Wells Fargo 428
10 Chase 126 10 UBS 347
Note: including investment funds, stock exchanges, agencies, etc.
Presentation Outline
• Quick news-analytics case
• Technology approach
• FactForge-News: Data architecture
• Sample queries on Linked Open Data
• News analytics examples
• Today’s News Map
Mar 2016Open Data & News Analytics 34
Today’s News Map: Business
Mar 2016Open Data & News Analytics 35
Today’s News Map: International
Mar 2016Open Data & News Analytics 36
Expect in Part II
• Mentions of entity and related by month
• Most relevant co-occurrnig entities
• Most relevant co-occurrnig entities per month
• Related News
• and more
Mar 2016Open Data & News Analytics 37
Thank you!
Experience the technology with NOW: Semantic News Portal
http://now.ontotext.com
Start using GraphDB and text-mining with S4 in the cloud
http://s4.ontotext.com
Learn more at our website or simply get in touch
info@ontotext.com, @ontotext
Mar 2016Open Data & News Analytics 38

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Boost your data analytics with open data and public news content

  • 1. Boost Your Data Analytics with Open Data and Public News Content Ontotext Webinar, 24 Mar 2016
  • 2. Presentation Outline – PART I • Quick news-analytics case • Technology approach • FactForge-News: Data architecture • Sample queries on Linked Open Data • News analytics examples • Today’s News Map Mar 2016Open Data & News Analytics 2
  • 3. Quick news-analytics case Mar 2016Open Data & News Analytics 3 • Our Dynamic Semantic Publishing platform already offers linking of text with big open data graphs • One can get navigate from text to concepts, get trends, related entities and news • Try it at http://now.ontotext.com
  • 4. Presentation Outline • Quick news-analytics case • Technology approach • FactForge-News: Data architecture • Sample queries on Linked Open Data • News analytics examples • Today’s News Map Mar 2016Open Data & News Analytics 4
  • 5. Our approach to Big Data 1. Integrate relevant data from many sources − Build a Big Knowledge Graph from proprietary databases and taxonomies integrated with millions of facts of Linked Data 2. Infer new facts and unveil relationships − Performing reasoning across data from different sources 3. Interlink text and with big data − Using text-mining to automatically discover references to concepts and entities 4. Use NoSQL graph database for metadata management, querying and search Mar 2016Open Data & News Analytics #5
  • 6. NoSQL Graph Database Mar 2016Open Data & News Analytics 6 myData: Maria ptop:Agent ptop:Person ptop:Woman ptop:childOf ptop:parentOf rdfs:range owl:inverseOf inferred myData:Ivan owl:relativeOf owl:inverseOfowl:SymmetricProperty rdfs:subPropertyOf owl:inverseOf owl:inverseOf rdf:type rdf:type rdf:type • The hottest NoSQL trend • W3C standards • Efficient Data Integration − Using logical inference − For data integration and BI
  • 7. Analyzing Text Mar 2016Open Data & News Analytics 7 • Full spectrum of NLP weaponry • Semantic indexing − Tag references with entity IDs − Generate semantic metadata descriptions of documents − Store metadata in GraphDB
  • 8. Presentation Outline • Quick news-analytics case • Technology approach • FactForge-News: Data architecture • Sample queries on Linked Open Data • News analytics examples • Today’s News Map Mar 2016Open Data & News Analytics 8
  • 9. The Web of Linked Data in 2007 Mar 2016Open Data & News Analytics #9 structured database version of Wikipedia database of all locations on Earth product reviews semantic synonym dictionary Note: Each bubble represents a dataset. Arrows represent mappings across datasets; e.g. dbpedia:Paris owl:sameAs geo:2988507
  • 10. The Web of Linked Data is Gaining Mass Mar 2016Open Data & News Analytics #10
  • 11. The Web of Data is Gaining Mass (2011) Mar 2016Open Data & News Analytics #11
  • 12. The Web of Linked Data is Gaining Mass Mar 2016Open Data & News Analytics #12 • 2013 stats: 2 289 public datasets − http://stats.lod2.eu/ • Growing exponentially − see the dotted trend line • Structured markup − Schema.org; semantic SEO • Enables better semantic tagging! − As there are more concepts and richer descriptions to refer to 27 43 89 162 295 822 2,289 2007 2008 2009 2010 2011 2012 2013 LinkedDataDatasets
  • 13. The FactForge Data • DBpedia (the English version only): 496M statements • Geonames: 150M statements − SameAs links between DBpedia and Geonames: 471K statements • NOW data – metadata about news: 128M statements • Total size: 938М statements − 656M explicit statements + 281M inferred statements − RDFRank and geo-spatial indices enabled to allow for ranking and efficient geo-region constraints Mar 2016Open Data & News Analytics 13
  • 14. News Metadata • Metadata from Ontotext’s Dynamic Semantic Publishing platform − Automatically generated as part of the NOW.ontotext.com semantic news showcase • News corpus from Google since Feb 2015, about 10k news/month • ~70 tags (annotations) per news article • Tags link text mentions of concepts to the knowledge graph − Technically these are URIs for entities (people, organizations, locations, etc.) and key phrases Mar 2016Open Data & News Analytics 14
  • 15. News Metadata Mar 2016Open Data & News Analytics 15
  • 16. News Metadata Mar 2016Open Data & News Analytics 16 Category Count International 52 074 Science and Technology 23 201 Sports 20 714 Business 15 155 Lifestyle 11 684 122 828 Mentions / entity type Count Keyphrase 2 589 676 Organization 1 276 441 Location 1 260 972 Person 1 248 784 Work 309 093 Event 258 388 RelationPersonRole 236 638 Species 180 946
  • 17. News Geographic Coverage Mar 2016Open Data & News Analytics 17 • Quite focused on USA!
  • 18. Class Hierarchy Map (by number of instances) Mar 2016Open Data & News Analytics 18 Left: The big picture Right: dbo:Agent class (2.7M organizations and persons)
  • 19. Presentation Outline • Quick news-analytics case • Technology approach • FactForge-News: Data architecture • Sample queries on Linked Open Data • News analytics examples • Today’s News Map Mar 2016Open Data & News Analytics 19
  • 20. Sample queries • There is a rich set of sample queries that allow exploration of this combination of DBPedia, GeoNames and news metadata • We will showcase few of those, starting from the simple once • In bold we marked the “parameters” of the queires Mar 2016Open Data & News Analytics 20
  • 21. Query: Big Cities in Eastern Europe # benefits from inference over transitive gn:parentFeature # benefits from owl:sameAs mapping between DBPedia and Geonames PREFIX dbr: <http://dbpedia.org/resource/> PREFIX onto: <http://www.ontotext.com/> PREFIX gn: <http://www.geonames.org/ontology#> PREFIX dbo: <http://dbpedia.org/ontology/> select * from onto:disable-sameAs where { ?loc gn:parentFeature dbr:Eastern_Europe ; gn:featureClass gn:P. ?loc dbo:populationTotal ?population ; dbo:country ?country . FILTER(?population > 300000 ) } order by ?country Mar 2016Open Data & News Analytics 21
  • 22. Query: People and Organizations related to Google # benefits from inference over transitive dbo:parent # RDFRank makes it easy to see the “top suspects” in a list of 93 entities PREFIX dbo: <http://dbpedia.org/ontology/> PREFIX rank: <http://www.ontotext.com/owlim/RDFRank#> PREFIX dbr: <http://dbpedia.org/resource/> select distinct ?related_entity ?rank where { BIND (dbr:Google as ?entity) { ?related_entity a dbo:Person ; ?p ?entity . } UNION { ?related_entity a dbo:Organisation ; dbo:parent ?entity . } ?related_entity rank:hasRDFRank ?rank } order by desc(?rank) Mar 2016Open Data & News Analytics 22
  • 23. Query: Airports near London # GraphDB’s geo-spatial plug-in allows efficient evaluation of near-by # RDFRank brings the top 6 passanger airports at the top of a list of 80 PREFIX dbr: <http://dbpedia.org/resource/> PREFIX geo-pos: <http://www.w3.org/2003/01/geo/wgs84_pos#> PREFIX gdb-geo: <http://www.ontotext.com/owlim/geo#> PREFIX dbo: <http://dbpedia.org/ontology/> PREFIX gdb: <http://www.ontotext.com/owlim/> SELECT distinct ?airport ?rrank WHERE { { SELECT * { dbr:London geo-pos:lat ?lat ; geo-pos:long ?long . } LIMIT 10 } ?airport gdb-geo:nearby(?lat ?long "50mi"); a dbo:Airport ; gdb:hasRDFRank ?rrank . } ORDER BY DESC(?rrank) Mar 2016Open Data & News Analytics 23
  • 24. Query: Top-level Industries by number of companies # benefits from mapping and consolidation of industry classifications # and predicates in DBPedia (ff-map) PREFIX dbo: <http://dbpedia.org/ontology/> PREFIX ff-map: <http://factforge.net/ff2016-mapping/> select distinct ?topIndustry (count(?company) as ?companies) where { ?company dbo:industry ?industry . ?industrySum ff-map:industryVariant ?industry . ?industrySum ff-map:industryCenter ?topIndustry . } group by ?topIndustry order by desc(?companies) Mar 2016Open Data & News Analytics 24
  • 25. Presentation Outline • Quick news-analytics case • Technology approach • FactForge-News: Data architecture • Sample queries on Linked Open Data • News analytics examples • Today’s News Map Mar 2016Open Data & News Analytics 25
  • 26. Semantic Press-Clipping • We can trace references to a specific company in the news − This is pretty much standard, however we can deal with syntactic variations in the names, because state of the art Named Entity Recognition technology is used − What’s more important, we distinguish correctly in which mention “Paris” refers to which of the following: Paris (the capital of France), Paris in Texas, Paris Hilton or to Paris (the Greek hero) • We can trace and consolidate references to daughter companies • We have comprehensive industry classification − The one from DBPedia, but refined to accommodate identifier variations and specialization (e.g. company classified as dbr:Bank will also be considered classified as dbr:FinancialServices) Mar 2016Open Data & News Analytics 26
  • 27. Query: News Mentioning an IBM # technical example to demonstrate how news metadata can be accessed PREFIX pub-old: <http://ontology.ontotext.com/publishing#> PREFIX pub: <http://ontology.ontotext.com/taxonomy/> PREFIX dbr: <http://dbpedia.org/resource/> PREFIX xsd: <http://www.w3.org/2001/XMLSchema#> select distinct ?news ?title ?date ?pub_entity where { ?news pub-old:containsMention / pub-old:hasInstance ?pub_entity . ?pub_entity pub:exactMatch dbr:IBM . ?news pub-old:creationDate ?date; pub-old:title ?title . FILTER ( (?date > "2015-10-01T00:02:00Z"^^xsd:dateTime) && (?date < "2015-11-01T00:02:00Z"^^xsd:dateTime)) } limit 100 Mar 2016Open Data & News Analytics 27
  • 28. Query: News Mentioning Gazprom and Its Related Entities # benefits from inference over transitive dbo:parent relation and mappings to it select distinct ?news ?title ?date ?related_entity where { { select distinct ?related_entity { BIND (dbr:Gazprom as ?entity) { ?related_entity a dbo:Person ; ?p ?entity . FILTER NOT EXISTS { ?related_entity dbo:club ?entity } } UNION { ?related_entity a dbo:Organisation ; dbo:parent ?entity . } UNION { BIND(?entity as ?related_entity) } } } ?news pub-old:containsMention / pub-old:hasInstance ?pub_entity . ?pub_entity pub:exactMatch ?related_entity . ?news pub-old:creationDate ?date; pub-old:title ?title . } order by desc(?date) limit 1000 Mar 2016Open Data & News Analytics 28
  • 29. Query: Most Popular in the News Automotive Companies # benefits from mapping and consolidation of industry classifications select distinct ?pub_entity (max(?entity_label) as ?label) (count(?news) as ?news_count) where { ?news pub-old:containsMention / pub-old:hasInstance ?pub_entity . ?pub_entity pub:exactMatch ?entity; pub:preferredLabel ?entity_label. dbr:Automotive ff-map:industryVariant ?industry . ?entity dbo:industry ?industry . ?news pub-old:creationDate ?date . } group by ?pub_entity order by desc(?news_count) Mar 2016Open Data & News Analytics 29
  • 30. Query: Most Popular in the News, including children # benefits from mapping and consolidation of industry classifications select distinct ?parent (count(?news) as ?news_count) where { { select distinct ?parent ?entity { BIND(dbr:Software as ?industry) ?industry ff-map:industryVariant ?industryVar . ?parent dbo:industry ?industryVar . ?parent a dbo:Company . FILTER NOT EXISTS { ?parent dbo:parent / dbo:industry / ff-map:industryVariant ?industry } { ?entity dbo:parent ?parent . } UNION { BIND(?parent as ?entity) } } } ?news pub-old:containsMention / pub-old:hasInstance ?pub_entity . ?pub_entity pub:exactMatch ?entity . ?news pub-old:creationDate ?date . } group by ?parent order by desc(?news_count) Mar 2016Open Data & News Analytics 30
  • 31. News Popularity Ranking: Automotive Mar 2016Open Data & News Analytics 31 Rank Company News # Rank Company incl. mentions of controlled News # 1 General Motors 2722 1 General Motors 4620 2 Tesla Motors 2346 2 Volkswagen Group 3999 3 Volkswagen 2299 3 Fiat Chrysler Automobiles 2658 4 Ford Motor Company 1934 4 Tesla Motors 2370 5 Toyota 1325 5 Ford Motor Company 2125 6 Chevrolet 1264 6 Toyota 1656 7 Chrysler 1054 7 Renault-Nissan Alliance 1332 8 Fiat Chrysler Automobiles 1011 8 Honda 864 9 Audi AG 972 9 BMW 715 10 Honda 717 10 Takata Corporation 547
  • 32. News Popularity: Finance Mar 2016Open Data & News Analytics 32 Rank Company News # Rank Company incl. mentions of controlled News # 1 Bloomberg L.P. 3203 1 China Merchants Bank 40940 2 Goldman Sachs 1992 2 Alphabet Inc. 24219 3 JP Morgan Chase 1712 3 Capital Group Companies 4379 4 Wells Fargo 1688 4 Bloomberg L.P. 3893 5 Citigroup 1557 5 Exor (company) 2775 6 HSBC Holdings 1546 6 JP Morgan Chase 2715 7 Deutsche Bank 1414 7 Nasdaq, Inc. 2178 8 Bank of America 1335 8 Oaktree Capital Management 1757 9 Barclays 1260 9 Goldman Sachs 1085 10 UBS 694 10 Sentinel Capital Partners 1064 Note: Including investment funds, stock exchanges, agencies, etc.
  • 33. News Popularity: Banking Mar 2016Open Data & News Analytics 33 Rank Company News # Rank Company incl. mentions of controlled News # 1 Goldman Sachs 996 1 China Merchants Bank * 38288 2 JP Morgan Chase 856 2 JP Morgan Chase 1972 3 HSBC Holdings 773 3 Goldman Sachs 1030 4 Deutsche Bank 707 4 HSBC 966 5 Barclays 630 5 Bank of America 771 6 Citigroup 519 6 Deutsche Bank 742 7 Bank of America 445 7 Barclays 681 8 Wells Fargo 422 8 Citigroup 630 9 UBS 347 9 Wells Fargo 428 10 Chase 126 10 UBS 347 Note: including investment funds, stock exchanges, agencies, etc.
  • 34. Presentation Outline • Quick news-analytics case • Technology approach • FactForge-News: Data architecture • Sample queries on Linked Open Data • News analytics examples • Today’s News Map Mar 2016Open Data & News Analytics 34
  • 35. Today’s News Map: Business Mar 2016Open Data & News Analytics 35
  • 36. Today’s News Map: International Mar 2016Open Data & News Analytics 36
  • 37. Expect in Part II • Mentions of entity and related by month • Most relevant co-occurrnig entities • Most relevant co-occurrnig entities per month • Related News • and more Mar 2016Open Data & News Analytics 37
  • 38. Thank you! Experience the technology with NOW: Semantic News Portal http://now.ontotext.com Start using GraphDB and text-mining with S4 in the cloud http://s4.ontotext.com Learn more at our website or simply get in touch info@ontotext.com, @ontotext Mar 2016Open Data & News Analytics 38

Editor's Notes

  1. HOW MANY CONCEPTS A PERSON KNOWS?
  2. HOW MANY CONCEPTS A PERSON KNOWS?
  3. HOW MANY CONCEPTS A PERSON KNOWS?
  4. HOW MANY CONCEPTS A PERSON KNOWS?
  5. HOW MANY CONCEPTS A PERSON KNOWS?