• DocumentCode
    3588326
  • Title

    Semantic enrichment in similarity combination for ontology

  • Author

    Kuan-Hao Huang ; Liu, Alan ; Jhih-Jhao Wang

  • Author_Institution
    Dept. of Electr. Eng., Nat. Chung Cheng Univ., Chiayi, Taiwan
  • fYear
    2014
  • Firstpage
    232
  • Lastpage
    237
  • Abstract
    Ontology can be seen as a knowledge sharing tool to communicate between different fields of knowledge. Each expert has their own way to design their ontologies using their own structures. This introduces a challenging problem of how to establish communication channels between these diversities. Ontology mapping is a good method to solve these problems by building relations between these ontologies. The core of ontology mapping is to compute the similarities between these concept pairs. In early years, researchers considered only single similarity for their ontology mapping. Recently, the scale of ontology has become more complicated. More researchers try to filter out all the possible similarities to combine concepts together to get a better result of mapping. One of the important similarity is the semantic similarity. In semantic similarity, how to get the attributes or properties to improve the scope of searching is an important part. This paper presents a method to improve the system by enriching the semantic similarity to expand the scope of the searching area. Constituting semantic similarity with other relations make the system have a better result in finding the correct similarity combinations and produce high possibility of successful mapping.
  • Keywords
    ontologies (artificial intelligence); semantic networks; knowledge sharing tool; ontology mapping; semantic enrichment; semantic similarity; similarity combination; Benchmark testing; Electrical engineering; Filtering; Mathematical model; Ontologies; Semantics; Speech; Ontology; Ontology Mapping; Semantic Enrichment; Similarity combination;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Automatic Control Conference (CACS), 2014 CACS International
  • Print_ISBN
    978-1-4799-4586-3
  • Type

    conf

  • DOI
    10.1109/CACS.2014.7097193
  • Filename
    7097193