Enhanced explicit semantic analysis for product model retrieval in construction industry

Han Liu, Yu-Shen Liu, Pieter Pauwels, Hongling Guo, Ming Gu

Research output: Contribution to journalArticleAcademicpeer-review

16 Citations (Scopus)

Abstract

With the rapidly growing number of online product models in construction industry, there is an urgent need for developing effective domain-specific information retrieval methods. Explicit semantic analysis (ESA) is a method that automatically extracts concept-based features from human knowledge repositories for semantic retrieval. This avoids the requirement of constructing and maintaining an explicitly formalized ontology. However, since domain-specific knowledge repositories are relatively small, the available terminologies are insufficient and concepts have coarse granularity. In this paper, we propose an enhanced ESA method for product model retrieval in construction industry. The major enhancements for the original ESA method consist of two parts. First, a novel concept expansion algorithm is proposed to solve the problem caused by insufficient terminologies. Second, a reranking algorithm is developed to solve the problem caused by coarse granularity of concepts. Experimental results show that our method significantly improves the performance of product model retrieval and outperforms the state-of-the-art methods. Our method is also applicable to product retrieval in other engineering domain if a specific knowledge repository is provided in that domain.
Original languageEnglish
Article number7934460
Pages (from-to)3361-3369
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Volume13
Issue number6
DOIs
Publication statusPublished - Dec 2017
Externally publishedYes

Bibliographical note

wos:id 000418128400058 ugent:id 8541974 ugent:classification A1

Keywords

  • Building information modeling (BIM)
  • domain knowledge
  • explicit semantic analysis (ESA)
  • industry foundation classes (IFC)
  • information retrieval (IR)

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