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Natural-language-based intelligent retrieval engine for BIM object database
- Source :
- Computers in Industry. 108:73-88
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- Rapid growth of building components in the BIM object database increases the difficulty of the efficient query of components that users require. Retrieval technology such as Autodesk Seek in America and BIMobject in Europe, which are widely used in BIM databases, are unable to understand what the search field truly means, causing a lack of completion and a low accuracy rate for results incapable of meeting the demands of users. To tackle such a problem, this paper puts forward a natural-language-based intelligent retrieval engine for the BIM object database and Revit modeling. First, a domain ontology is constructed for semantic understanding, and the BIM object database framework is established for testing our search engine. Second, “target keyword” and “restriction sequence” proposed are extracted from the natural sentences of users. Then, a final query is formed, combining concepts of “keyword” and “restriction sequence”, and its concepts are expanded through the semantic relationship in ontology. Finally, the results are presented after mapping from the final query to the BIM object database and ranking of results. Compared with traditional keyword-based methods, the experimental results demonstrate that our method outperforms the traditional methods.
- Subjects :
- 0209 industrial biotechnology
Sequence
General Computer Science
Database
Computer science
General Engineering
A domain
02 engineering and technology
Ontology (information science)
Object (computer science)
computer.software_genre
Field (computer science)
Ranking (information retrieval)
Search engine
020901 industrial engineering & automation
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
computer
Natural language
Subjects
Details
- ISSN :
- 01663615
- Volume :
- 108
- Database :
- OpenAIRE
- Journal :
- Computers in Industry
- Accession number :
- edsair.doi...........8b6c7392ff820464b47f2c898d1f234e