Building the concept semantic space for large text database

Xiao Wei, Daniel Dajun Zeng, Wei Wu, Yeming Dai

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

To overcome such shortcomings of keyword-based systems on the large text database as low efficiency and recall of searching, this paper proposes a novel concept semantic space to describe the large scale of text database efficiently. The proposed concept semantic space describes the text database from multiple semantic granularities (i.e. keyword, concept, etc.) and multiple semantic dimensions (i.e. association relations, similar relations, etc.), which provides a macroscopic and dynamic view of the text database. With the support of concept semantic space, some novel systems can be constructed on the text database to provide novel, efficient and flexible services. In this paper, take association relation for example, the main steps of building such a concept semantic space on text database are discussed in detail. In the end, both the experimental results and a prototype system, named as Knowle, show that the proposed concept semantic space is efficient in organizing the text database.

Original languageEnglish (US)
Pages (from-to)421-429
Number of pages9
JournalComputer Systems Science and Engineering
Volume30
Issue number5
StatePublished - Sep 2015
Externally publishedYes

Keywords

  • Concept extraction
  • Concept semantic space
  • Multiple semantic dimensions
  • Multiple semantic granularities
  • Semantic link network
  • Text database

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science(all)

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