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Ontology learning: state of the art and open issues

Ontology learning: state of the art and open issues. Lina Zhou Presenter : Taizhi Li. Overview. L earning-oriented model of ontology development F ramework for ontology learning Ontology learning approaches Classification schema of domains Open issues Conclusion. Question s ？ ？ ？.

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Ontology learning: state of the art and open issues

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  1. Ontologylearning:stateoftheartandopenissues LinaZhou Presenter:TaizhiLi

  2. Overview • Learning-oriented model of ontology development • Framework for ontology learning • Ontology learning approaches • Classification schema of domains • Open issues • Conclusion

  3. Questions？？？ • WhatisOntologyLearning？ • OntologyLearningreferstotheautomaticdiscoveryandcreationofontologicalknowledgeusing machine learning techniques. • Whatisadvantagesofontologylearningcomparedwithcrafting ontologies? • Alargerscaleandafasterpace. • Mitigatehuman-introducedbiasesandinconsistence.

  4. Major issues in ontology development • Ontology representation: adequacy and inference efficiency • Ontology acquisition （incomplete，subjective，outdated） eg.Dictionaries,webdocuments,databaseschema • Ontology evaluation(content,technology,methodology,application) completeness,consistency,correctness;learning • Ontology maintenance(organize,search,updateexistingontologies) eg.SWOOGLE

  5. Learning-oriented model RODvs.ConventionalOntologyDevelopment? Iterativeprocess Rapid ontology development (ROD)

  6. RapidOntologydevelopment

  7. Requirementsforintegratedtools Functions that can facilitate ontology development: • Knowledge elicitation • Ontology retrieval • Ontology editing • Ontology validation • Collaborative development • Ontology transformation and presentation

  8. Framework for ontology learning

  9. Ontology organizationsteps B A A+B • Clusteringsynonymoustermsandtheirrelations • Derivinginverserelations • Discoveringlocalcentersofconcepts • Buildinghigher-levelontologies C C  B A c* p j i

  10. Ontology learning approaches

  11. Taxonomyoflearningtechniques

  12. Classificationschemaofdomains Domains Poorquality Learningstrategies Approachesselection: • Established vs. under-developeddomains Top-downBottom-up Knowledge-richKnowledge-lean (eg.biology)(eg.communitydevelopment)

  13. Classificationschemaofdomains • Emergingvs. conventionaldomains Bottom-upTop-down Hybrid • Technology-heavyvs.technology-lightdomains (Hybrid) • Self-containedvs.interdisciplinarydomains (Top-level)

  14. Open issues • Human understandable vs. machine-understandable • Learning specific relations eg.Part-wholeforSequenceOntologyforgenomicannotation; Related_synonymforGeneOntology • Learning higher-degree relations eg.Trustrelation:whotrustswhomonwhat • Learning definitions eg.isdefinedas;isreferredtoas

  15. Open issues • Term filtering Suggestion: mutual information and traditional term weighting tech; contrast analysis; anaphora and co-reference resolution • Mapping to high-level ontology Suggestion: path analysis; treat the top-level ontologies as seeds • Evaluation benchmark Suggestion: benchmark corpora • Incremental ontology learning Suggestion: update existing ontologies incrementally

  16. Open issues • Levels of ontology learning eg. “Dell Notebook”canbeasubclassoraninstanceof“Notebook” • Multi-agent learning Suggestion:agent-basedparadigm • Learning beyond text Suggestion：content-based imageretrievaltechs

  17. Conclusion • Ontology operational within web and distributed system • Semanticweb • Ontologies:turnthecurrentwebsiteintoanetworkofknowledgeresourcesandservices

  18. Thankyou！

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