A query expansion method for retrieving online BIM resources based on Industry Foundation Classes

• Develop a prototype semantic search engine for retrieving online BIM resources
• Construct an IFC IR Ontology
• By combining the ontology and LCA, present an automatic query expansion method for improving retrieval performance
• Test our method on the benchmark dataset of Autodesk Seek

With the rapid popularity of Building Information Modeling (BIM) technology, BIM resources such as building product libraries are growing rapidly on the World Wide Web. As a result, this also increases the difficulty for quickly finding useful BIM resources that are sufficiently close to user's specific needs. Keyword-based search methods have been widely used due to their ease of use, but their search accuracy is often not satisfactory because of the semantic ambiguity of terminologies in BIM-specific documents and queries. To address this issue, we develop a prototype semantic search engine, named BIMSeek, for retrieving online BIM resources. The central work consists of two parts as follows. Firstly, based on Industry Foundation Classes (IFC) which is a major standard for BIM, a domain ontology is constructed for encoding BIM-specific knowledge into the search engine. Using the ontology, term`inologies in BIM documents can be disambiguated and indexed. Secondly, by combining the ontology and local context analysis technique, an automatic query expansion method is presented for improving retrieval performance. Compared with traditional keyword-based methods and WordNet-based query expansion methods, the experimental results demonstrate that our method outperforms them. The search engine is available at http://cgcad.thss.tsinghua.edu.cn/liuyushen/ifcqe/.

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