Generating partial civil information model views using a semantic information retrieval approach

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2020-01-01
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Jeong, H. David
Gilbert, Stephen
Chukharev-Hudilainen, Evgeny
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Chukharev-Hudilainen, Evgeny
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Gilbert, Stephen
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Psychology
The Department of Psychology may prepare students with a liberal study, or for work in academia or professional education for law or health-services. Graduates will be able to apply the scientific method to human behavior and mental processes, as well as have ample knowledge of psychological theory and method.
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Industrial and Manufacturing Systems Engineering
The Department of Industrial and Manufacturing Systems Engineering teaches the design, analysis, and improvement of the systems and processes in manufacturing, consulting, and service industries by application of the principles of engineering. The Department of General Engineering was formed in 1929. In 1956 its name changed to Department of Industrial Engineering. In 1989 its name changed to the Department of Industrial and Manufacturing Systems Engineering.
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Virtual Reality Applications CenterPsychologyEnglishIndustrial and Manufacturing Systems EngineeringPsychologyGerontology
Abstract

Open data standards (e.g. LandXML, TransXML, CityGML) are a key to addressing the interoperability issue in exchanging civil information modeling (CIM) data throughout the project life-cycle. Since these schemas include rich sets of data types covering a wide range of assets and disciplines, model view definitions (MVDs) which define subsets of a schema are required to specify what types of data to be shared in accordance with a specific exchange scenario. The traditional procedure for generating and implementing MVDs is time-consuming and laborious as entities and attributes relevant to a particular data exchange context are manually identified by domain experts. This paper presents a method that can locate relevant information from a source XML data schema for a specific domain based on the user's keyword. The study employs a semantic resource of civil engineering terms to understand the semantics of a keyword-based query. The study also introduces a novel context-based search technique for retrieving related entities and their referenced objects. The developed method was tested on a gold standard of several LandXML subschemas. The experiment results show that the semantic MVD retrieval algorithm achieves a mean average precision of nearly 90%. The research is original, being a novel method for extracting partial civil information models given a keyword from the end user. The method is expected to become a fundamental tool assisting professionals in extracting data from complex digital datasets.

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This article is published as Le, Tuyen, H. David Jeong, Stephen B. Gilbert, and Evgeny Chukharev-Hudilainend. "Generating partial civil information model views using a semantic information retrieval approach." Journal of Information Technology in Construction (ITcon) 25, no. 2 (2020): 41-54. DOI: 10.36680/j.itcon.2020.002. Posted with permission.

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Wed Jan 01 00:00:00 UTC 2020
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