Machine reading for the Semantic Web
Transcript of Machine reading for the Semantic Web
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Machine reading for the Semantic Web
Aldo Gangemi and Valentina PresuttiSTLab, ISTC-CNR, Italy
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Semantic Technology LaboratoryInstitute of Cognitive Sciences and Technologies
Consiglio Nazionale delle Ricerche Italy
Misael Mongiovì
Daria Spampinato
Aldo GangemiHead of the Lab
Valentina Presutti
Andrea Nuzzolese
Sergio Consoli
Luigi Asprino
Giorgia Lodi
Diego ReforgiatoMartina SangiovanniPaolo Ciancarini
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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
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Open Knowledge Extraction
aka Machine Reading for the Semantic Web
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WWWInformation Resources
Data
Non-Information Resources
Agents
The Web as an integration hub
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WWWInformation Resources
Data
Non-Information Resources
Agents
Web 1.0
Web 2.0
Web 3.0aka
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
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WWWInformation 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
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• 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
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WWWInformation 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
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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.
?
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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
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Open Knowledge Extraction
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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
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WWWInformation 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
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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.
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• The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914
eventsnega(on
modality
par(cipants
more par(cipants
quality
coreference
need for “deep” machine reading
event rela(on
date
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Open Information Extractionpc5: 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 1610.2200632195721161 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
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FRED: A Machine Reader for the Semantic Web
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LOD and ODP design
Aligned to WordNet, VerbNet, FrameNet,
DOLCE+DnS,DBpedia, schema.org
http://wit.istc.cnr.it/stlab-tools/fred
“The Semantic Web will extremely love FRED’s reading”
RESTful, Python lib
Earmark, NIF
RDF, OWL
Apache Stanbol
DRT- and Frame-basedHigh EE and RE accuracy
FRED integrates NER, SenseTagging, WSD, Tax. Ind.,
Relation/Event/Role Extraction
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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 andEarmark/NIF text spans with semiotic relations to graph entities (denotes,
hasInterpretant)
events
quali(es
tense representa(on
WSD/alignment
event rela(ons
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FRED’s architecture
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<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
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FRED’s REST API
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FRED’s Python library to access FRED by “motif”
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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
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Evaluation against a motif-based gold standard“mo(fs”
text types
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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
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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
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• 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
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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
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Semantic Web Machine Reading
Miles Davis was an american jazz musician.
Ongoing research: temporal series of graphs,
motif-based evaluation
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Tìpalo: a FRED app http://wit.istc.cnr.it/stlab-tools/tipalo/
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Linking to WN supersenses
DBpedia resourceExtracted 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: [ʃɛzlɔŋ̃ɡ(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
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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
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Semantic Sentiment Analysis
Miles Davis hated Betty Mabry because of her radical promiscuity.
Open issues: better sentic resources,
contextual scoring, etc.
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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.
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• 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
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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
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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 ?xFILTER (?agent = vnrole:Agent || ?agent = boxer:agent || ?agent = vnrole:Experiencer || ?agent = vnrole:Actor || ?agent = vnrole:Actor1 || ?agent = vnrole:Actor2 || ?agent = vnrole:Cause)}
OPTIONAL {?e ?atheme ?xFILTER (?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 ?yFILTER (?patient = vnrole:Topic || ?patient = vnrole:Beneficiary || ?patient = vnrole:Patient || ?patient = vnrole:Patient1 || ?patient = vnrole:Patient2 || ?patient = boxer:patient || ?patient = boxing:declaration)}
OPTIONAL {?e ?ptheme ?yFILTER (?ptheme = vnrole:Theme || ?ptheme = vnrole:Theme1 || ?ptheme = vnrole:Theme2 || ?ptheme = boxer:theme){?e ?agent1 ?aFILTER (?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 ?modFILTER (?aspect = owl:sameAs || ?aspect = dul:hasQuality || ?aspect = boxing:hasModality || ?aspect = boxing:hasTruthValue)}
OPTIONAL {?e ?oblique ?zFILTER (?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}}}
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event subgraph
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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
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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
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• 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
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• 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
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• 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”
• …
• …
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Robotic machine reading challenges …
“my birthday is on Saturday”
“ok, then on March 10th you’ll be
one year older”
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“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 …
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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