Bibliographic Details
| Title: |
Construction of metadata database structured by conceptual elements of text structure and semantic search evaluation of Korean studies. |
| Authors: |
Ko, Young Man, Song, Min Sun, Lee, Seung Jun |
| Source: |
Electronic Library; 2021, Vol. 39 Issue 5, p678-694, 17p |
| Abstract: |
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. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
