adaptando, aprendendo e integrando modelos semânticos
TRANSCRIPT
Adaptando, Aprendendo e Integrando
Modelos Semânticos
Kate Revoredo
Department of Applied Informatics
Informatics Graduate Program
Federal University of the State of Rio de Janeiro (UNIRIO)
Primeiro Encontro em Computação Semântica@ UFRJ, Março 2015
ALICE research group
Adaptating,Adaptating,Learning andIntegratingConceptualizationsEnvironment
Overall Scope
Structured data
(Databases) Learning Ontologies
(OntoUML,
FOL, DL)
Business rules
(declare)servicesIntegrating
Documents
Social
Data sources
Adapting Business Process Models
(BPMN, XPDL, KIPN)
(declare)
Resources
Patterns
AntiPatterns
servicesSocial
media
Logs
(provenance data)
StoriesKnowledge base
Linguistic
(wordnet)
abcabcabcabc
Learning well-founded ontologies from texts
Structured data
(Databases) Learning Ontologies
(OntoUML,
FOL, DL)
Business rules
(declare)servicesIntegrating
Documents
Social
Data sources
Adapting Business Process Models
(BPMN, XPDL, KIPN)
(declare)
Resources
Patterns
AntiPatterns
servicesSocial
media
Logs
(provenance data)
StoriesKnowledge base
Linguistic
(wordnet)
abcabcabcabc
Motivation
•Wordnet is Semantically restricted•Expresses some relationships, but not philosophical meta-properties.
• “Does it have an identity principle?” (sortal)
• “Is certain property always required?” (Rigidity)
• “Does it depend on other things to exist?” (Relational Dependency)
•Useful information for Computational Linguistics•Useful information for Computational Linguistics•Understanding of the concept under analysis
•Example:• “Author executes submission of paper”
• Author depends on Paper
• Paper only exists if an author submits it
Author PaperExecutes submission
**
Challenges
How to…•identify the best meaning (sense) for a word given its context?
•determine the most adequate construct of the well-founded modeling
language to represent this sense?
Linguistic approach with semantic focus
WordNet(synsets database)
Semantic TypesUFO
(OntoUML)
Supersenses and
Semantic Relations
From Wordnet synsets to Semantic Types
•Simple Mappings•Simple correspondence between Supersense and Semantic Type
• Equal or similar names and definitions
•Plant � Flora•Plant: “plants”
Semantic TypesWordNet Synsets
7 / 40
•Plant: “plants”
•Flora: “things of the natural world which instances belong to the plant
kingdom”
•Exemplo: Tree, flower, grass
Semantically Expanding WordNet through Semantic Types and UFO
From Wordnet synsets to Semantic Types
•Complex Mappings•More information then definitions needed
• Hypernyms and Hyponyms, Holonyms and Meronyms, etc.• Syntactical derivation• Morfological characteristics of sinonyms.
Semantic TypesWordNet Synsets
8 / 40
• Morfological characteristics of sinonyms.
• It is necessary to use more than one Semantic Type to complete the meaning
•Artifact � Artefact and Parts•Artifact: “Man-made objects” (different from Object)•Artefact: “Things made by humans, i.e. not existing in nature”•Parts: “things that can be seen as parts of other things or beings ”
Semantically Expanding WordNet through Semantic Types and UFO
Method Proposal
9 / 21
Example of an ontology learned
Pattern-Based Ontology Refinement
Structured data
(Databases) Learning Ontologies
(OntoUML,
FOL, DL)
Business rules
(declare)servicesIntegrating
Documents
Social
media
Logs
Data sources
Adapting Business Process Models
(BPMN, XPDL, KIPN)
Resources
Patterns
AntiPatterns
Logs
(provenance data)
StoriesKnowledge base
Linguistic
(wordnet)
abcabcabcabc
Ontology Design Patterns
Proposal
Improving ontology alignment through
correspondence antipatterns
Structured data
(Databases) Learning Ontologies
(OntoUML,
FOL, DL)
Business rules
(declare)servicesIntegrating
Documents
Social
Data sources
Adapting Business Process Models
(BPMN, XPDL, KIPN)
(declare)
Resources
Patterns
AntiPatterns
servicesSocial
media
Logs
(provenance data)
StoriesKnowledge base
Linguistic
(wordnet)
abcabcabcabc
Proposal
15
Back to the overall scenario...
Structured data
(Databases) Learning Ontologies
(OntoUML,
FOL, DL)
Business rules
(declare)servicesIntegrating
Documents
Social
Data sources
Adapting Business Process Models
(BPMN, XPDL, KIPN)
(declare)
Resources
Patterns
AntiPatterns
servicesSocial
media
Logs
(provenance data)
StoriesKnowledge base
Linguistic
(wordnet)
abcabcabcabc
Some challenges...
•Probabilistic formalisms
•Big data
•Refinement in general.
•Incoherence alignments
•E-science Support Infrastructure•For learning, refinement and integration•For learning, refinement and integration
ThankThankThankThank youyouyouyou............
Adaptating,
Contact
Adaptating,Learning andIntegratingConceptualizationsEnvironment