A Study on Conversion Methods for Generating RDF Ontology from Structural Terminology Net (STNet) based on RDB

Ko, Young Man; Lee, Seung Jun; Song, Malin

doi:10.3743/kosim.2015.32.2.131

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Third, after verifying any logical errors in ontology structure, we converted the STNet RDB data into RDF data. We used a "D2RQ" RDF ontology converter that has been found suitable for dynamic RDBs, in which relationships between data changes or new data are added frequently (Ko et al, 2015). We converted RDB data into RDF data, using an SQL script to retain class structures generated in the second process (Bumans, 2010).…”

Section: Process and Methodologymentioning

confidence: 99%

Construction of the structural definition-based terminology ontology system and semantic search evaluation

Song²,

Lee

2016

LHT

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During the production of your article we came across the following queries listed in the table below. Please review the queries and insert your reply or correction at the corresponding line in the PDF proof of the article which follows this query page. No. Queries Q1 Please check the change in reference from Dong (2013) to Dong et al. (2013) as per the reference list in the sentence "They are roughly classified…analysis of relational schema" is correct. Else provide complete publication details for Dong (2013). Q2 Please provide name of editor(s) in references: Astrova (2004), Corcho et al. (2005). Q3 Please provide the names of editor(s) and city location of publisher in reference: Astrova (2009). Q4 Please provide the names of editor(s), name of publisher and its city location in references Astrova and Stantic (2005), López-Pellicer et al. (2008).

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Section: Process and Methodologymentioning

confidence: 99%

Construction of the structural definition-based terminology ontology system and semantic search evaluation

Song²,

Lee

2016

LHT

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Construction of metadata database structured by conceptual elements of text structure and semantic search evaluation of Korean studies

Ko¹,

Song²,

Lee³

2021

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Purpose This study aims to develop metadata of conceptual elements based on the text structure of research articles on Korean studies, to propose a search algorithm that reflects the combination of semantically relevant data in accordance with the search intention of research paper and to examine the algorithm whether there is a difference in the intention-based search results. Design/methodology/approach This study constructed a metadata database of 5,007 research articles on Korean studies arranged by conceptual elements of text structure and developed F1(w)-score weighted to conceptual elements based on the F1-score and the number of data points from each element. This study evaluated the algorithm by comparing search results of the F1(w)-score algorithm with those of the Term Frequency- Inverse Document Frequency (TF-IDF) algorithm and simple keyword search. Findings The authors find that the higher the F1(w)-score, the closer the semantic relevance of search intention. Furthermore, F1(w)-score generated search results were more closely related to the search intention than those of TF-IDF and simple keyword search. Research limitations/implications Even though the F1(w)-score was developed in this study to evaluate the search results of metadata database structured by conceptual elements of text structure of Korean studies, the algorithm can be used as a tool for searching the database which is a tuning process of weighting required. Practical implications A metadata database based on text structure and a search method based on weights of metadata elements – F1(w)-score – can be useful for interdisciplinary studies, especially for semantic search in regional studies. Originality/value This paper presents a methodology for supporting IR using F1(w)-score—a novel model for weighting metadata elements based on text structure. The F1(w)-score-based search results show the combination of semantically relevant data, which are otherwise difficult to search for using similarity of search words.

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A Study on Conversion Methods for Generating RDF Ontology from Structural Terminology Net (STNet) based on RDB

Cited by 2 publications

References 6 publications

Construction of the structural definition-based terminology ontology system and semantic search evaluation

Construction of the structural definition-based terminology ontology system and semantic search evaluation

Construction of metadata database structured by conceptual elements of text structure and semantic search evaluation of Korean studies

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