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

Author(s):



Medium: journal article
Language(s): English
Published in: Journal of Information Technology in Construction, , v. 25
Page(s): 41-54
DOI: 10.36680/j.itcon.2020.002
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.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.36680/j.itcon.2020.002.
  • About this
    data sheet
  • Reference-ID
    10540517
  • Published on:
    03/01/2021
  • Last updated on:
    19/02/2021
 
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