Semantically processing parallel colour descriptions

Shenghui Wang*, Jeff Z. Pan

*Corresponding author for this work

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

Abstract

Information integration and retrieval are useful tasks in many information systems. In these systems, it is far from an easy task to directly integrate information from natural language (NL) sources, because precisely capturing NL semantics is not a trivial issue in the first place. In this paper, we choose the botanical domain to investigate this issue. While most existing systems in this domain support only keyword-based search, this paper introduces an ontology-based approach to process parallel colour descriptions from botanical documents. Based on a semantic model, it takes advantage of ontologies so as to represent the semantics of colour descriptions precisely, to integrate parallel descriptions according to their semantic distances, and to answer colour-related species identification queries. To evaluate this approach, we implement a colour reasoner based on the FaCT-DG Description Logic reasoner, and present some results of our experiments on integrating parallel descriptions and species identification queries. From this highly specialised domain, we learn a set of more general methodological rules.

Original languageEnglish
Title of host publicationJournal on Data Semantics X
Pages212-236
Number of pages25
DOIs
Publication statusPublished - 27 Aug 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4900 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • colour space
  • description logic
  • colour term
  • colour description
  • parallel information

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Wang, S., & Pan, J. Z. (2008). Semantically processing parallel colour descriptions. In Journal on Data Semantics X (pp. 212-236). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4900 LNCS). https://doi.org/10.1007/978-3-540-77688-8_7