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Adaptando, Aprendendo e Integrando Modelos Semânticos.
- 1. 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
- 2. ALICE research group Adaptating,Adaptating, Learning and Integrating Conceptualizations Environment
- 3. Overall Scope Structured data (Databases) Learning Ontologies (OntoUML, FOL, DL) Business rules (declare)services Integrating Documents Social Data sources Adapting Business Process Models (BPMN, XPDL, KIPN) (declare) Resources Patterns AntiPatterns servicesSocial media Logs (provenance data) Stories Knowledge base Linguistic (wordnet) abcabcabcabc
- 4. Learning well-founded ontologies from texts Structured data (Databases) Learning Ontologies (OntoUML, FOL, DL) Business rules (declare)services Integrating Documents Social Data sources Adapting Business Process Models (BPMN, XPDL, KIPN) (declare) Resources Patterns AntiPatterns servicesSocial media Logs (provenance data) Stories Knowledge base Linguistic (wordnet) abcabcabcabc
- 5. 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 Paper Executes submission **
- 6. 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 Types UFO (OntoUML) Supersenses and Semantic Relations
- 7. 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
- 8. 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
- 9. Method Proposal 9 / 21
- 10. Example of an ontology learned
- 11. Pattern-Based Ontology Refinement Structured data (Databases) Learning Ontologies (OntoUML, FOL, DL) Business rules (declare)services Integrating Documents Social media Logs Data sources Adapting Business Process Models (BPMN, XPDL, KIPN) Resources Patterns AntiPatterns Logs (provenance data) Stories Knowledge base Linguistic (wordnet) abcabcabcabc
- 13. Proposal
- 14. Improving ontology alignment through correspondence antipatterns Structured data (Databases) Learning Ontologies (OntoUML, FOL, DL) Business rules (declare)services Integrating Documents Social Data sources Adapting Business Process Models (BPMN, XPDL, KIPN) (declare) Resources Patterns AntiPatterns servicesSocial media Logs (provenance data) Stories Knowledge base Linguistic (wordnet) abcabcabcabc
- 15. Proposal 15
- 16. Back to the overall scenario... Structured data (Databases) Learning Ontologies (OntoUML, FOL, DL) Business rules (declare)services Integrating Documents Social Data sources Adapting Business Process Models (BPMN, XPDL, KIPN) (declare) Resources Patterns AntiPatterns servicesSocial media Logs (provenance data) Stories Knowledge base Linguistic (wordnet) abcabcabcabc
- 17. 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