Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/222313
Title: Enhanced Explicit Semantic Analysis for Product Model Retrieval in Construction Industry
Authors: Han Liu;Yu-Shen Liu;Pieter Pauwels;Hongling Guo;Ming Gu
Year: 2017
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/222313
volume: 13
issue: 6
More Information: 3361,
3369
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7934460.pdf990.17 kBAdobe PDF
Title: Enhanced Explicit Semantic Analysis for Product Model Retrieval in Construction Industry
Authors: Han Liu;Yu-Shen Liu;Pieter Pauwels;Hongling Guo;Ming Gu
Year: 2017
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/222313
volume: 13
issue: 6
More Information: 3361,
3369
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7934460.pdf990.17 kBAdobe PDF
Title: Enhanced Explicit Semantic Analysis for Product Model Retrieval in Construction Industry
Authors: Han Liu;Yu-Shen Liu;Pieter Pauwels;Hongling Guo;Ming Gu
Year: 2017
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/222313
volume: 13
issue: 6
More Information: 3361,
3369
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7934460.pdf990.17 kBAdobe PDF