• Journal of Korean Library and Information Science Society
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• v.48 no.1
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• pp.345-371
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• 2017

Traditionally, the construction industry field is one of the typical fields using facet analysis method. Internationally the facet analysis method is applied to the classification scheme "Uniclass" or thesaurus "Construction Industry Thesaurus(CIT)". In the case of Korea, the facet analysis method is being used in classification scheme "Construction Industry Classification System" but it is difficult to find studies on facet analysis method in thesaurus or building case study. This study aims to establish facet types by introducing building of thesaurus for information retrieval in construction field using facet analysis method and to suggest building plan of thesaurus in construction field using facet analysis method. In this paper, We establish the 11 top facets(agent/patient, artifacts, abstract, material, parts/component, works, attribute, media, process, space, time) and the 13 subfacet as rudimental facets and suggest building plan according to thesaurus building procedure which is suggested by International Standard(ISO 25964-1). The result of this study is expected to apply to facet based thesaurus. And it is also expected to reuse in Taxonomy or Ontology etc. and to use in interoperability with the classification scheme of Construction filed.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.375-382
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• 2024

Large Language Models (LLMs) still encounter challenges in comprehending domain-specific expressions within construction documents. Analogous to humans acquiring unfamiliar expressions from dictionaries, language models could assimilate domain-specific expressions through the use of a thesaurus. Numerous prior studies have developed construction thesauri; however, a practical issue arises in effectively leveraging these resources for instructing language models. Given that the thesaurus primarily outlines relationships between terms without indicating their relative importance, language models may struggle in discerning which terms to retain or replace. This research aims to establish a robust framework for guiding language models using the information from the thesaurus. For instance, a term would be associated with a list of similar terms while also being included in the lists of other related terms. The relative significance among terms could be ascertained by employing similarity scores normalized according to relevance ranks. Consequently, a term exhibiting a positive margin of normalized similarity scores (termed a pivot term) could semantically replace other related terms, thereby enabling LLMs to comprehend domain-specific terms through these pivotal terms. The outcome of this research presents a practical methodology for utilizing domain-specific thesauri to train LLMs and analyze construction documents. Ongoing evaluation involves validating the accuracy of the thesaurus-applied LLM (e.g., S-BERT) in identifying similarities within construction specification provisions. This outcome holds potential for the construction industry by enhancing LLMs' understanding of construction documents and subsequently improving text mining performance and project management efficiency.

• Journal of the Korean Society for information Management
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• v.12 no.1
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• pp.99-113
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• 1995

This study evaluates representability and comprehensivity of the Theasurus in theeconomics and industry fields of the "General Thesaurus of Newspaper Articles." The methodsused in the study were, first, indexing of the pages covering economics and industry articlesusing the Thesaurus and second, comparing the Thesaurus terms with the words collectedfrom the newspapers articles and glossaries. The study clarifies the following problems whichmight occur in the construction and use of newspaper thesaurus: specificity of the subjectconcepts, separation of component concept, preference relationship between descriptors andentry terms, the methods of recording of proper nouns and allocation of terms among thesubject areas concern.he subject areas concern.