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Machine reading for the Semantic Web

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The document discusses machine reading for the semantic web, focusing on knowledge extraction from diverse web content, such as natural language text. It highlights tools like Fred, which perform event extraction and semantic role labeling while addressing open issues in knowledge representation. Research challenges include complex relation extraction, sentiment analysis, and integrating social norms and practices into machine reading applications.

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Machine reading for the Semantic Web Aldo Gangemi and Valentina Presutti STLab, ISTC-CNR, Italy
Semantic Technology Laboratory Institute of Cognitive Sciences and Technologies Consiglio Nazionale delle Ricerche Italy Misael Mongiovì Daria Spampinato Aldo Gangemi Head of the Lab Valentina Presutti Andrea Nuzzolese Sergio Consoli Luigi Asprino Giorgia Lodi Diego ReforgiatoMartina Sangiovanni Paolo Ciancarini
COMPETENCES Ontology Design Linked Open Data design and publishing Knowledge Extraction Machine readingOpinion Mining and Sentiment analysis Software architectures for knowledge-intensive applications Large-sale data integration
Open Knowledge Extraction aka Machine Reading for the Semantic Web
WWW Information Resources Data Non-Information Resources Agents The Web as an integration hub
WWW Information Resources Data Non-Information Resources Agents Web 1.0 Web 2.0 Web 3.0 aka Semantic Web “John Coltrane” this is the guy this is a picture John Coltrane, also known as Trane, was an American jazz saxophonist and composer. Working in the bebop and hard bop idioms early in his career, Coltrane helped pioneer the use of modes in jazz and was later at the forefront of free jazz. The Web as an integration hub
WWW Information Resources Data Non-Information Resources Agents Web 1.0 Web 2.0 Web 3.0 “John Coltrane” this is the guy this is a picture John Coltrane, also known as Trane, was an American jazz saxophonist and composer. Working in the bebop and hard bop idioms early in his career, Coltrane helped pioneer the use of modes in jazz and was later at the forefront of free jazz. The Web as an integration hub
• Most of the Web of Data derived from structured data (typically databases) or semi- structured data (e.g. Wikipedia infoboxes) • Web content is mostly natural language text (web sites, news, forums, reviews, etc.) • Such content is highly valuable for the Semantic Web (question answering, opinion mining, knowledge summarization, etc.) 8 Limits and motivation
WWW Information Resources Data Non-Information Resources Agents Web 1.0 Web 2.0 Web 3.0 “John Coltrane” this is the guy this is a picture John Coltrane, also known as Trane, was an American jazz saxophonist and composer. Working in the bebop and hard bop idioms early in his career, Coltrane helped pioneer the use of modes in jazz and was later at the forefront of free jazz. The Web as an integration hub
John Coltrane, also known as Trane, was an American jazz saxophonist and composer. Working in the bebop and hard bop idioms early in his career, Coltrane helped pioneer the use of modes in jazz and was later at the forefront of free jazz. ?
To extract as much relevant knowledge as possible from web textual content and publish it in the form of Semantic Web triples unsupervised, open domain, grounded 11 Open Knowledge Extraction
John Coltrane, also known as Trane, was an American jazz saxophonist and composer. Working in the bebop and hard bop idioms early in his career, Coltrane helped pioneer the use of modes in jazz and was later at the forefront of free jazz. knowledge extraction entity and data linking data enrichment
WWW Information Resources Data Non-Information Resources Agents Web 1.0 Web 2.0 Web 3.0 “John Coltrane” this is the guy this is a picture John Coltrane, also known as Trane, was an American jazz saxophonist and composer. Working in the bebop and hard bop idioms early in his career, Coltrane helped pioneer the use of modes in jazz and was later at the forefront of free jazz. The Web as an integration hub
Approaches to linked data and ontology learning • Most attention to enrich the Web of Data by learning standard relations: e.g., membership (rdf:type), class taxonomy (rdfs:subClassOf), entity linking (owl:sameAs) • What about general factual relations? • e.g. roles in events, part, participation, causality, location, friendship, etc.
• The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914 events nega(on modality par(cipants more  par(cipants quality coreference need for “deep” machine reading event  rela(on date
Open Information Extraction pc5: NLPapps mac$ java -Xmx512m -jar reverb-latest.jar <<<"The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914." Initializing ReVerb extractor...Done. Initializing confidence function...Done. Initializing NLP tools...Done. Starting extraction. stdin 1 he arrived in Sarajevo 13 14 14 16 16 10.2200632195721161 The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th , 1914 . DT NNP NNP MD RB VB VBN TO RB VB NNP NNP IN PRP VBD IN NNP IN NNP JJ , CD . B-NP I-NP I-NP B-VP I-VP I-VP I-VP I-VP I-VP I-VP B-NP I-NP B-SBAR B-NP B-VP B-PP B-NP B-PP B-NP I-NP I-NP I-NP O he arrive in sarajevo Done with extraction. Summary: 1 extractions, 1 sentences, 0 files, 1 seconds
FRED:A Machine Reader for the Semantic Web
LOD and ODP design Aligned to WordNet, VerbNet, FrameNet, DOLCE+DnS, DBpedia, schema.org http://wit.istc.cnr.it/stlab-tools/fred “The SemanticWeb will extremely love FRED’s reading” RESTful, Python lib Earmark, NIF RDF, OWL Apache Stanbol DRT- and Frame-based High EE and RE accuracy FRED integrates NER, SenseTagging, WSD, Tax. Ind., Relation/Event/Role Extraction
machine reading to rdf “The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914” type  induc(on nega(on modality taxonomy  induc(on seman(c  roles NER indirect  type  induc(on + configurable namespaces and Earmark/NIF text spans with semiotic relations to graph entities (denotes, hasInterpretant) events quali(es tense  representa(on WSD/alignment event  rela(ons
FRED’s architecture
<http://www.ontologydesignpatterns.org/ont/fred/domain.owl#offset_31_45_hard_bop+idiom> a <http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif- core#OffsetBasedString> ; <http://www.w3.org/2000/01/rdf-schema#label> "Hard_bop Idiom"^^<http://www.w3.org/2001/XMLSchema#string> , "hard_bop idiom"^^<http://www.w3.org/2001/XMLSchema#string> ; <http://ontologydesignpatterns.org/cp/owl/semiotics.owl#denotes> <http://www.ontologydesignpatterns.org/ont/fred/domain.owl#Hard_bopIdiom> ; <http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core#beginIndex> "31"^^<http://www.w3.org/2001/XMLSchema#nonNegativeInteger> ; <http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core#endIndex> "45"^^<http://www.w3.org/2001/XMLSchema#nonNegativeInteger> ; <http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core#referenceContext> <http://www.ontologydesignpatterns.org/ont/fred/domain.owl#docuverse> . Text annotation with NLP Interchange Format and EARMARK
FRED’s REST API
FRED’s Python library to access FRED by “motif”
Landscape analysis of KE tools • Hint at FRED performing best on term, relation, event extraction, taxonomy induction, and frame detection • terminology extraction F1 = .87 • taxonomy induction F1 = .83 • relation extraction F1 = .76 • frame detection F1 =, 93 • event detection F1 = .82
Evaluation against a motif-based gold standard “mo(fs” text  types
Evaluation of frame detection against FrameNet corpus • Precision is equivalent (p = .75) to the state-of-art tool (Semafor), recall is lower (r = .58 against .75), but Semafor trained on the corpus itself • FRED is one order of magnitude faster • FRED’s frame occurrences are formally represented
Evaluation of FRED-based Tìpalo typing tool • Tìpalo is a tool that automatically creates type taxonomies to entities, based on their definitions in natural language provided by their corresponding Wikipedia pages • Evaluation on a corpus of Wikipedia resources: • F1 = .92 for entity typing • F1 = .75 if state-of-the-art WSD is considered
• Sentilo identifies opinion holders, detects topics, and scores opinions • Evaluations on a corpus of user-based hotel reviews • F1 = .95 for holder detection • F1 = .66 for topic detection • F1 = .80 for subtopic detection • .81 is the correlation with open-rating 5-star scores given for reviews Evaluation of FRED-based Sentilo sentiment analysis tool
Research challenges and applications • Open Knowledge Extraction • Semantic Web Machine Reading • Semantic Sentiment Analysis • Complex Relation Extraction and Representation • Abstractive Summarization • Robotic natural language understanding • Integration of action schemas, linked data, and OKE frames • Social practices and norms • Irony • Modality
Semantic Web Machine Reading Miles Davis was an american jazz musician. Ongoing research: temporal series of graphs, motif-based evaluation
31 Tìpalo: a FRED app http://wit.istc.cnr.it/stlab-tools/tipalo/
32 Linking  to  WN  supersenses DBpedia  resource Extracted  type Disambiguated  sense Inferred  superclass Linked  resource Disambiguated  WordNet  sense A chaise longue is an upholstered sofa in the shape of a chair that is long enough to support the legs. A chaise longue (English /ˌʃeɪz ˈlɔːŋ/;[1] French pronunciation: [ʃɛzlɔ̃ŋɡ(ə)], "long chair") is an upholstered sofa in the shape of a chair that is long enough to support the legs. Linking  to  DOLCE Rich  taxonomical   data! hJp://en.wikipedia.org/wiki/Chaise_longue
Elwood Buchanan slapped Miles Davis' knuckles every time Miles was using heavy vibrato. Miles Davis was an american jazz musician. Graph series and reconciliation Open issues: generalized reconciliation with relevance
Semantic Sentiment Analysis Miles Davis hated Betty Mabry because of her radical promiscuity. Open issues: better sentic resources, contextual scoring, etc.
Complex Relation Extraction and Representation Abstractive Summarization Open issues: coverage outside DBpedia, better naming, skolemized entities, alignment with existing properties, clustering of generated properties George E. Krug graduated from Lafayette College in Easton, Pennsylvania, in the class of 1884. He went on to study architecture in Philadelphia, at the Fine Arts Institute of the University of Pennsylvania.
• Event identity: FRED focuses on events expressed by verbs, propositions, terms, and named entities (possibly resolved), as well as on event graphs • Event classification: FRED uses Linked Data-oriented induction of types for identified events, reusing e.g.VerbNet,WordNet, DBpedia, schema.org, and DOLCE+DnS as reference ontologies • Event unity: FRED applies semantic role labeling to verbs and propositions (“situations”) in order to detect event boundaries, and frame detection for resolving roles against a shared event ontology (VerbNet, FrameNet, ...) • Event modifiers: FRED extracts logical negation, basic modalities, and adverbial qualities, applied to verbs and propositions, which can also be used as event judgment indicators • Event relations: FRED relates events via the role structure of verbs and propositions, and extracts tense and entailment relations between them Event Extraction
e.g. from: “The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914” type  induc(on nega(on modality taxonomy  induc(on seman(c  roles NER indirect  type  induc(on events quali(es tense  representa(on WSD/alignment event  rela(ons
SPARQL-based event extraction PREFIX dul: <http://www.ontologydesignpatterns.org/ont/dul/DUL.owl#> PREFIX vnrole: <http://www.ontologydesignpatterns.org/ont/vn/abox/role/> PREFIX boxing: <http://www.ontologydesignpatterns.org/ont/boxer/boxing.owl#> PREFIX boxer: <http://www.ontologydesignpatterns.org/ont/boxer/boxer.owl#> PREFIX d0: <http://www.ontologydesignpatterns.org/ont/d0.owl#> PREFIX schemaorg: <http://schema.org/> PREFIX : <http://www.ontologydesignpatterns.org/ont/boxer/test.owl#> CONSTRUCT {?e :agent ?x . ?e ?r ?e1 . ?e :patient ?y . ?e ?aspect ?mod . ?e ?oblique ?z . ?e rdf:type ?et . ?et rdfs:subClassOf ?et1 . ?et1 rdfs:subClassOf ?et2} WHERE { {{?e a boxing:Situation} UNION {?e rdf:type/rdfs:subClassOf* dul:Event} UNION {?e rdf:type/rdfs:subClassOf* schemaorg:Event} UNION {?e rdf:type/rdfs:subClassOf* d0:Event}} OPTIONAL {?e ?agent ?x FILTER (?agent = vnrole:Agent || ?agent = boxer:agent || ?agent = vnrole:Experiencer || ?agent = vnrole:Actor || ?agent = vnrole:Actor1 || ?agent = vnrole:Actor2 || ?agent = vnrole:Cause)} OPTIONAL {?e ?atheme ?x FILTER (?atheme = vnrole:Theme || ?atheme = vnrole:Theme1 || ?atheme = vnrole:Theme2 || ?atheme = boxer:theme) MINUS {?e ?agent1 ?a   FILTER (?agent1 = vnrole:Agent || ?agent1 = boxer:agent || ?agent1 = vnrole:Experiencer || ?agent1 = vnrole:Actor || ?agent1 = vnrole:Actor1 || ?agent1 = vnrole:Actor2 || ?agent1 = vnrole:Cause) FILTER (?x != ?a)}} OPTIONAL {{?e ?r ?e1} {{?e1 a boxing:Situation} UNION {?e1 rdf:type/rdfs:subClassOf* dul:Event}} FILTER (?e != ?e1)} OPTIONAL {?e ?patient ?y FILTER (?patient = vnrole:Topic || ?patient = vnrole:Beneficiary || ?patient = vnrole:Patient || ?patient = vnrole:Patient1 || ?patient = vnrole:Patient2 || ?patient = boxer:patient || ?patient = boxing:declaration)} OPTIONAL {?e ?ptheme ?y FILTER (?ptheme = vnrole:Theme || ?ptheme = vnrole:Theme1 || ?ptheme = vnrole:Theme2 || ?ptheme = boxer:theme) {?e ?agent1 ?a FILTER (?agent1 = vnrole:Agent || ?agent1 = boxer:agent || ?agent1 = vnrole:Experiencer || ?agent1= vnrole:Actor || ?agent1= vnrole:Actor1 || ?agent1= vnrole:Actor2 || ?agent1 = vnrole:Cause)   FILTER (?y != ?a)}} OPTIONAL {?e ?aspect ?mod FILTER (?aspect = owl:sameAs || ?aspect = dul:hasQuality || ?aspect = boxing:hasModality || ?aspect = boxing:hasTruthValue)} OPTIONAL {?e ?oblique ?z FILTER (?oblique != vnrole:Topic && ?oblique != vnrole:Beneficiary && ?oblique != vnrole:Patient && ?oblique != vnrole:Patient1 && ?oblique != vnrole:Patient2 && ?patient != boxer:patient && ?oblique != vnrole:Theme && ?patient != boxer:patient && ?oblique != vnrole:Agent && ?oblique != boxer:agent && ?oblique != vnrole:Experiencer && ?oblique != vnrole:Actor && ?oblique != vnrole:Actor2 && ? oblique != vnrole:Cause && ?oblique != boxer:theme && ?oblique != vnrole:Theme1 && ?oblique != vnrole:Theme2 && ?oblique != rdf:type)} OPTIONAL {?e rdf:type ?et . ?et rdfs:subClassOf+ ?et1 OPTIONAL {?et1 rdfs:subClassOf+ ?et2}} }
event subgraph
OKE named graphs • Why open-world? Because it’s the web, with incomplete knowledge • Integration between NLP and SW • “The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914” • d:Sarajevo • d:Sarajevo :locatedIn d:FormerAustrianEmpire , d:Bosnia • d:Bosnia :partOf d:Yugoslavia
ng1914 • Why open-world? Because it’s the web, with incomplete knowledge • Integration between NLP and SW • “The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914” • d:Sarajevo • d:Sarajevo :locatedIn d:FormerAustrianEmpire , d:Bosnia • d:Bosnia :partOf d:Yugoslavia OKE named graphs
• Why open-world? Because it’s the web, with incomplete knowledge • Integration between NLP and SW • “The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914” • d:Sarajevo • d:Sarajevo :locatedIn d:FormerAustrianEmpire , d:Bosnia • d:Bosnia :partOf d:Yugoslavia ng1929 OKE named graphs
• Why open-world? Because it’s the web, with incomplete knowledge • Integration between NLP and SW • “The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914” • d:Sarajevo • d:Sarajevo :locatedIn d:FormerAustrianEmpire , d:Bosnia • d:Bosnia :partOf d:Yugoslavia ng1995 OKE named graphs
• More with events, relations, tense representation, sentiment … • “The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914” • … • …
Robotic machine reading challenges … “my birthday is on Saturday” “ok, then on March 10th you’ll be one year older”
“would you like to have a party?” Open issues: Integration of action schemas with linked data and OKE frames, social practices and norms, irony, modality, … “my birthday is on Saturday” Robotic machine reading challenges …
Related publications • Valentina Presutti, Francesco Draicchio and Aldo Gangemi. Knowledge Extraction based on Discourse Representation Theory and Linguistic Frames. A. ten Teije and J. Völker (eds.): Proceedings of the Conference on Knowledge Engineering and Knowledge Management (EKAW2012), LNCS, Springer, 2012 • Aldo Gangemi, Andrea Giovanni Nuzzolese, Valentina Presutti, Francesco Draicchio, Alberto Musetti and Paolo Ciancarini. Automatic Typing of DBpedia Entities. Proceedings of ISWC2012, the Tenth International Semantic Web Conference, LNCS, Springer, 2012 • Aldo Gangemi. A Comparison of Knowledge Extraction Tools for the Semantic Web. Proceedings of ESWC2013, LNCS, Springer, 2013 • Aldo Gangemi, Francesco Draicchio, Valentina Presutti, Andrea Giovanni Nuzzolese, Diego Reforgiato. A Machine Reader for the Semantic Web. Proceedings of ISWC2013, Springer, 2013 • Aldo Gangemi, Valentina Presutti, Diego Reforgiato Recupero. Frame-based detection of opinion holders and topics: a model and a tool. IEEE Computational Intelligence, 9(1), 2014 • Valentina Presutti, Sergio Consoli, Andrea Giovanni Nuzzolese, Diego Reforgiato Recupero, Aldo Gangemi, Ines Bannour, Haïfa Zargayouna. Uncovering the semantics of Wikipedia Pagelinks. Proceedings of the Conference on Knowledge Engineering and Knowledge Management (EKAW2014), Springer, Berlin, 2014 • Diego Reforgiato Recupero, Valentina Presutti, Sergio Consoli, Aldo Gangemi, Andrea Giovanni Nuzzolese. Sentilo: Frame-Based Sentiment Analysis. Cognitive Computation, http://dx.doi.org/10.1007/ s12559-014-9302-z, 2014

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Machine reading for the Semantic Web

  • 1. Machine reading for the Semantic Web Aldo Gangemi and Valentina Presutti STLab, ISTC-CNR, Italy
  • 2. Semantic Technology Laboratory Institute of Cognitive Sciences and Technologies Consiglio Nazionale delle Ricerche Italy Misael Mongiovì Daria Spampinato Aldo Gangemi Head of the Lab Valentina Presutti Andrea Nuzzolese Sergio Consoli Luigi Asprino Giorgia Lodi Diego ReforgiatoMartina Sangiovanni Paolo Ciancarini
  • 3. COMPETENCES Ontology Design Linked Open Data design and publishing Knowledge Extraction Machine readingOpinion Mining and Sentiment analysis Software architectures for knowledge-intensive applications Large-sale data integration
  • 4. Open Knowledge Extraction aka Machine Reading for the Semantic Web
  • 6. WWW Information Resources Data Non-Information Resources Agents Web 1.0 Web 2.0 Web 3.0 aka Semantic Web “John Coltrane” this is the guy this is a picture John Coltrane, also known as Trane, was an American jazz saxophonist and composer. Working in the bebop and hard bop idioms early in his career, Coltrane helped pioneer the use of modes in jazz and was later at the forefront of free jazz. The Web as an integration hub
  • 7. WWW Information Resources Data Non-Information Resources Agents Web 1.0 Web 2.0 Web 3.0 “John Coltrane” this is the guy this is a picture John Coltrane, also known as Trane, was an American jazz saxophonist and composer. Working in the bebop and hard bop idioms early in his career, Coltrane helped pioneer the use of modes in jazz and was later at the forefront of free jazz. The Web as an integration hub
  • 8. • Most of the Web of Data derived from structured data (typically databases) or semi- structured data (e.g. Wikipedia infoboxes) • Web content is mostly natural language text (web sites, news, forums, reviews, etc.) • Such content is highly valuable for the Semantic Web (question answering, opinion mining, knowledge summarization, etc.) 8 Limits and motivation
  • 9. WWW Information Resources Data Non-Information Resources Agents Web 1.0 Web 2.0 Web 3.0 “John Coltrane” this is the guy this is a picture John Coltrane, also known as Trane, was an American jazz saxophonist and composer. Working in the bebop and hard bop idioms early in his career, Coltrane helped pioneer the use of modes in jazz and was later at the forefront of free jazz. The Web as an integration hub
  • 10. John Coltrane, also known as Trane, was an American jazz saxophonist and composer. Working in the bebop and hard bop idioms early in his career, Coltrane helped pioneer the use of modes in jazz and was later at the forefront of free jazz. ?
  • 11. To extract as much relevant knowledge as possible from web textual content and publish it in the form of Semantic Web triples unsupervised, open domain, grounded 11 Open Knowledge Extraction
  • 12. John Coltrane, also known as Trane, was an American jazz saxophonist and composer. Working in the bebop and hard bop idioms early in his career, Coltrane helped pioneer the use of modes in jazz and was later at the forefront of free jazz. knowledge extraction entity and data linking data enrichment
  • 13. WWW Information Resources Data Non-Information Resources Agents Web 1.0 Web 2.0 Web 3.0 “John Coltrane” this is the guy this is a picture John Coltrane, also known as Trane, was an American jazz saxophonist and composer. Working in the bebop and hard bop idioms early in his career, Coltrane helped pioneer the use of modes in jazz and was later at the forefront of free jazz. The Web as an integration hub
  • 14. Approaches to linked data and ontology learning • Most attention to enrich the Web of Data by learning standard relations: e.g., membership (rdf:type), class taxonomy (rdfs:subClassOf), entity linking (owl:sameAs) • What about general factual relations? • e.g. roles in events, part, participation, causality, location, friendship, etc.
  • 15. • The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914 events nega(on modality par(cipants more  par(cipants quality coreference need for “deep” machine reading event  rela(on date
  • 16. Open Information Extraction pc5: NLPapps mac$ java -Xmx512m -jar reverb-latest.jar <<<"The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914." Initializing ReVerb extractor...Done. Initializing confidence function...Done. Initializing NLP tools...Done. Starting extraction. stdin 1 he arrived in Sarajevo 13 14 14 16 16 10.2200632195721161 The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th , 1914 . DT NNP NNP MD RB VB VBN TO RB VB NNP NNP IN PRP VBD IN NNP IN NNP JJ , CD . B-NP I-NP I-NP B-VP I-VP I-VP I-VP I-VP I-VP I-VP B-NP I-NP B-SBAR B-NP B-VP B-PP B-NP B-PP B-NP I-NP I-NP I-NP O he arrive in sarajevo Done with extraction. Summary: 1 extractions, 1 sentences, 0 files, 1 seconds
  • 17. FRED:A Machine Reader for the Semantic Web
  • 18. LOD and ODP design Aligned to WordNet, VerbNet, FrameNet, DOLCE+DnS, DBpedia, schema.org http://wit.istc.cnr.it/stlab-tools/fred “The SemanticWeb will extremely love FRED’s reading” RESTful, Python lib Earmark, NIF RDF, OWL Apache Stanbol DRT- and Frame-based High EE and RE accuracy FRED integrates NER, SenseTagging, WSD, Tax. Ind., Relation/Event/Role Extraction
  • 19. machine reading to rdf “The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914” type  induc(on nega(on modality taxonomy  induc(on seman(c  roles NER indirect  type  induc(on + configurable namespaces and Earmark/NIF text spans with semiotic relations to graph entities (denotes, hasInterpretant) events quali(es tense  representa(on WSD/alignment event  rela(ons
  • 21. <http://www.ontologydesignpatterns.org/ont/fred/domain.owl#offset_31_45_hard_bop+idiom> a <http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif- core#OffsetBasedString> ; <http://www.w3.org/2000/01/rdf-schema#label> "Hard_bop Idiom"^^<http://www.w3.org/2001/XMLSchema#string> , "hard_bop idiom"^^<http://www.w3.org/2001/XMLSchema#string> ; <http://ontologydesignpatterns.org/cp/owl/semiotics.owl#denotes> <http://www.ontologydesignpatterns.org/ont/fred/domain.owl#Hard_bopIdiom> ; <http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core#beginIndex> "31"^^<http://www.w3.org/2001/XMLSchema#nonNegativeInteger> ; <http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core#endIndex> "45"^^<http://www.w3.org/2001/XMLSchema#nonNegativeInteger> ; <http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core#referenceContext> <http://www.ontologydesignpatterns.org/ont/fred/domain.owl#docuverse> . Text annotation with NLP Interchange Format and EARMARK
  • 23. FRED’s Python library to access FRED by “motif”
  • 24. Landscape analysis of KE tools • Hint at FRED performing best on term, relation, event extraction, taxonomy induction, and frame detection • terminology extraction F1 = .87 • taxonomy induction F1 = .83 • relation extraction F1 = .76 • frame detection F1 =, 93 • event detection F1 = .82
  • 25. Evaluation against a motif-based gold standard “mo(fs” text  types
  • 26. Evaluation of frame detection against FrameNet corpus • Precision is equivalent (p = .75) to the state-of-art tool (Semafor), recall is lower (r = .58 against .75), but Semafor trained on the corpus itself • FRED is one order of magnitude faster • FRED’s frame occurrences are formally represented
  • 27. Evaluation of FRED-based Tìpalo typing tool • Tìpalo is a tool that automatically creates type taxonomies to entities, based on their definitions in natural language provided by their corresponding Wikipedia pages • Evaluation on a corpus of Wikipedia resources: • F1 = .92 for entity typing • F1 = .75 if state-of-the-art WSD is considered
  • 28. • Sentilo identifies opinion holders, detects topics, and scores opinions • Evaluations on a corpus of user-based hotel reviews • F1 = .95 for holder detection • F1 = .66 for topic detection • F1 = .80 for subtopic detection • .81 is the correlation with open-rating 5-star scores given for reviews Evaluation of FRED-based Sentilo sentiment analysis tool
  • 29. Research challenges and applications • Open Knowledge Extraction • Semantic Web Machine Reading • Semantic Sentiment Analysis • Complex Relation Extraction and Representation • Abstractive Summarization • Robotic natural language understanding • Integration of action schemas, linked data, and OKE frames • Social practices and norms • Irony • Modality
  • 30. Semantic Web Machine Reading Miles Davis was an american jazz musician. Ongoing research: temporal series of graphs, motif-based evaluation
  • 31. 31 Tìpalo: a FRED app http://wit.istc.cnr.it/stlab-tools/tipalo/
  • 32. 32 Linking  to  WN  supersenses DBpedia  resource Extracted  type Disambiguated  sense Inferred  superclass Linked  resource Disambiguated  WordNet  sense A chaise longue is an upholstered sofa in the shape of a chair that is long enough to support the legs. A chaise longue (English /ˌʃeɪz ˈlɔːŋ/;[1] French pronunciation: [ʃɛzlɔ̃ŋɡ(ə)], "long chair") is an upholstered sofa in the shape of a chair that is long enough to support the legs. Linking  to  DOLCE Rich  taxonomical   data! hJp://en.wikipedia.org/wiki/Chaise_longue
  • 33. Elwood Buchanan slapped Miles Davis' knuckles every time Miles was using heavy vibrato. Miles Davis was an american jazz musician. Graph series and reconciliation Open issues: generalized reconciliation with relevance
  • 34. Semantic Sentiment Analysis Miles Davis hated Betty Mabry because of her radical promiscuity. Open issues: better sentic resources, contextual scoring, etc.
  • 35. Complex Relation Extraction and Representation Abstractive Summarization Open issues: coverage outside DBpedia, better naming, skolemized entities, alignment with existing properties, clustering of generated properties George E. Krug graduated from Lafayette College in Easton, Pennsylvania, in the class of 1884. He went on to study architecture in Philadelphia, at the Fine Arts Institute of the University of Pennsylvania.
  • 36. • Event identity: FRED focuses on events expressed by verbs, propositions, terms, and named entities (possibly resolved), as well as on event graphs • Event classification: FRED uses Linked Data-oriented induction of types for identified events, reusing e.g.VerbNet,WordNet, DBpedia, schema.org, and DOLCE+DnS as reference ontologies • Event unity: FRED applies semantic role labeling to verbs and propositions (“situations”) in order to detect event boundaries, and frame detection for resolving roles against a shared event ontology (VerbNet, FrameNet, ...) • Event modifiers: FRED extracts logical negation, basic modalities, and adverbial qualities, applied to verbs and propositions, which can also be used as event judgment indicators • Event relations: FRED relates events via the role structure of verbs and propositions, and extracts tense and entailment relations between them Event Extraction
  • 37. e.g. from: “The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914” type  induc(on nega(on modality taxonomy  induc(on seman(c  roles NER indirect  type  induc(on events quali(es tense  representa(on WSD/alignment event  rela(ons
  • 38. SPARQL-based event extraction PREFIX dul: <http://www.ontologydesignpatterns.org/ont/dul/DUL.owl#> PREFIX vnrole: <http://www.ontologydesignpatterns.org/ont/vn/abox/role/> PREFIX boxing: <http://www.ontologydesignpatterns.org/ont/boxer/boxing.owl#> PREFIX boxer: <http://www.ontologydesignpatterns.org/ont/boxer/boxer.owl#> PREFIX d0: <http://www.ontologydesignpatterns.org/ont/d0.owl#> PREFIX schemaorg: <http://schema.org/> PREFIX : <http://www.ontologydesignpatterns.org/ont/boxer/test.owl#> CONSTRUCT {?e :agent ?x . ?e ?r ?e1 . ?e :patient ?y . ?e ?aspect ?mod . ?e ?oblique ?z . ?e rdf:type ?et . ?et rdfs:subClassOf ?et1 . ?et1 rdfs:subClassOf ?et2} WHERE { {{?e a boxing:Situation} UNION {?e rdf:type/rdfs:subClassOf* dul:Event} UNION {?e rdf:type/rdfs:subClassOf* schemaorg:Event} UNION {?e rdf:type/rdfs:subClassOf* d0:Event}} OPTIONAL {?e ?agent ?x FILTER (?agent = vnrole:Agent || ?agent = boxer:agent || ?agent = vnrole:Experiencer || ?agent = vnrole:Actor || ?agent = vnrole:Actor1 || ?agent = vnrole:Actor2 || ?agent = vnrole:Cause)} OPTIONAL {?e ?atheme ?x FILTER (?atheme = vnrole:Theme || ?atheme = vnrole:Theme1 || ?atheme = vnrole:Theme2 || ?atheme = boxer:theme) MINUS {?e ?agent1 ?a   FILTER (?agent1 = vnrole:Agent || ?agent1 = boxer:agent || ?agent1 = vnrole:Experiencer || ?agent1 = vnrole:Actor || ?agent1 = vnrole:Actor1 || ?agent1 = vnrole:Actor2 || ?agent1 = vnrole:Cause) FILTER (?x != ?a)}} OPTIONAL {{?e ?r ?e1} {{?e1 a boxing:Situation} UNION {?e1 rdf:type/rdfs:subClassOf* dul:Event}} FILTER (?e != ?e1)} OPTIONAL {?e ?patient ?y FILTER (?patient = vnrole:Topic || ?patient = vnrole:Beneficiary || ?patient = vnrole:Patient || ?patient = vnrole:Patient1 || ?patient = vnrole:Patient2 || ?patient = boxer:patient || ?patient = boxing:declaration)} OPTIONAL {?e ?ptheme ?y FILTER (?ptheme = vnrole:Theme || ?ptheme = vnrole:Theme1 || ?ptheme = vnrole:Theme2 || ?ptheme = boxer:theme) {?e ?agent1 ?a FILTER (?agent1 = vnrole:Agent || ?agent1 = boxer:agent || ?agent1 = vnrole:Experiencer || ?agent1= vnrole:Actor || ?agent1= vnrole:Actor1 || ?agent1= vnrole:Actor2 || ?agent1 = vnrole:Cause)   FILTER (?y != ?a)}} OPTIONAL {?e ?aspect ?mod FILTER (?aspect = owl:sameAs || ?aspect = dul:hasQuality || ?aspect = boxing:hasModality || ?aspect = boxing:hasTruthValue)} OPTIONAL {?e ?oblique ?z FILTER (?oblique != vnrole:Topic && ?oblique != vnrole:Beneficiary && ?oblique != vnrole:Patient && ?oblique != vnrole:Patient1 && ?oblique != vnrole:Patient2 && ?patient != boxer:patient && ?oblique != vnrole:Theme && ?patient != boxer:patient && ?oblique != vnrole:Agent && ?oblique != boxer:agent && ?oblique != vnrole:Experiencer && ?oblique != vnrole:Actor && ?oblique != vnrole:Actor2 && ? oblique != vnrole:Cause && ?oblique != boxer:theme && ?oblique != vnrole:Theme1 && ?oblique != vnrole:Theme2 && ?oblique != rdf:type)} OPTIONAL {?e rdf:type ?et . ?et rdfs:subClassOf+ ?et1 OPTIONAL {?et1 rdfs:subClassOf+ ?et2}} }
  • 40. OKE named graphs • Why open-world? Because it’s the web, with incomplete knowledge • Integration between NLP and SW • “The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914” • d:Sarajevo • d:Sarajevo :locatedIn d:FormerAustrianEmpire , d:Bosnia • d:Bosnia :partOf d:Yugoslavia
  • 41. ng1914 • Why open-world? Because it’s the web, with incomplete knowledge • Integration between NLP and SW • “The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914” • d:Sarajevo • d:Sarajevo :locatedIn d:FormerAustrianEmpire , d:Bosnia • d:Bosnia :partOf d:Yugoslavia OKE named graphs
  • 42. • Why open-world? Because it’s the web, with incomplete knowledge • Integration between NLP and SW • “The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914” • d:Sarajevo • d:Sarajevo :locatedIn d:FormerAustrianEmpire , d:Bosnia • d:Bosnia :partOf d:Yugoslavia ng1929 OKE named graphs
  • 43. • Why open-world? Because it’s the web, with incomplete knowledge • Integration between NLP and SW • “The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914” • d:Sarajevo • d:Sarajevo :locatedIn d:FormerAustrianEmpire , d:Bosnia • d:Bosnia :partOf d:Yugoslavia ng1995 OKE named graphs
  • 44. • More with events, relations, tense representation, sentiment … • “The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914” • … • …
  • 45. Robotic machine reading challenges … “my birthday is on Saturday” “ok, then on March 10th you’ll be one year older”
  • 46. “would you like to have a party?” Open issues: Integration of action schemas with linked data and OKE frames, social practices and norms, irony, modality, … “my birthday is on Saturday” Robotic machine reading challenges …
  • 47. Related publications • Valentina Presutti, Francesco Draicchio and Aldo Gangemi. Knowledge Extraction based on Discourse Representation Theory and Linguistic Frames. A. ten Teije and J. Völker (eds.): Proceedings of the Conference on Knowledge Engineering and Knowledge Management (EKAW2012), LNCS, Springer, 2012 • Aldo Gangemi, Andrea Giovanni Nuzzolese, Valentina Presutti, Francesco Draicchio, Alberto Musetti and Paolo Ciancarini. Automatic Typing of DBpedia Entities. Proceedings of ISWC2012, the Tenth International Semantic Web Conference, LNCS, Springer, 2012 • Aldo Gangemi. A Comparison of Knowledge Extraction Tools for the Semantic Web. Proceedings of ESWC2013, LNCS, Springer, 2013 • Aldo Gangemi, Francesco Draicchio, Valentina Presutti, Andrea Giovanni Nuzzolese, Diego Reforgiato. A Machine Reader for the Semantic Web. Proceedings of ISWC2013, Springer, 2013 • Aldo Gangemi, Valentina Presutti, Diego Reforgiato Recupero. Frame-based detection of opinion holders and topics: a model and a tool. IEEE Computational Intelligence, 9(1), 2014 • Valentina Presutti, Sergio Consoli, Andrea Giovanni Nuzzolese, Diego Reforgiato Recupero, Aldo Gangemi, Ines Bannour, Haïfa Zargayouna. Uncovering the semantics of Wikipedia Pagelinks. Proceedings of the Conference on Knowledge Engineering and Knowledge Management (EKAW2014), Springer, Berlin, 2014 • Diego Reforgiato Recupero, Valentina Presutti, Sergio Consoli, Aldo Gangemi, Andrea Giovanni Nuzzolese. Sentilo: Frame-Based Sentiment Analysis. Cognitive Computation, http://dx.doi.org/10.1007/ s12559-014-9302-z, 2014