Consistent Knowledge Discovery from Evolving Ontologies

Freddy Lecue, Jeff Z Pan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Deductive reasoning and inductive learning are the most common approaches for deriving knowledge. In real world applications when data is dynamic and incomplete, especially those exposed by sensors, reasoning is limited by dynamics of data while learning is biased by data incompleteness. Therefore discovering consistent knowledge from incomplete and dynamic data is a challenging open problem. In our approach the semantics of data is captured through ontologies to empower learning (mining) with (Description Logics) reasoning. Consistent knowledge discovery is achieved by applying generic, significative, representative association semantic rules. The experiments have shown scalable, accurate and consistent knowledge discovery with data from Dublin.
Original languageEnglish
Title of host publicationAAAI'15 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Pages189-195
Number of pages7
ISBN (Print)0-262-51129-0
Publication statusPublished - 2015
EventProceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence - Austin, United States
Duration: 25 Jan 201530 Jan 2015
http://www.aaai.org/Conferences/AAAI/aaai15.php

Conference

ConferenceProceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI'15
CountryUnited States
CityAustin
Period25/01/1530/01/15
Internet address

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Keywords

  • Semantic web
  • evolving ontology
  • dynamic ontology
  • dynamic reasoning
  • temporal reasoning

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Lecue, F., & Pan, J. Z. (2015). Consistent Knowledge Discovery from Evolving Ontologies. In AAAI'15 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (pp. 189-195). AAAI Press.