Search results

The purpose of this paper is to construct a structural definition-based terminology ontology system that defines the meanings of academic terms on the basis of properties and links terms with properties that are structured by conceptual categories (classes). This study also aims to test the possibility of semantic searches by generating inference rules and setting very complicated search scenarios.

For the study, 55,236 keywords from the articles of the “Korea Citation Index” were structurally defined and relationships among terms and properties were built. Then, the authors converted the RDB data into RDF and designed ontologies using the ontology developing tool Protégé. The authors also tested the designed ontology with the inference engine of the Protégé editor. The generated reference rules were tested by TBox and SPARQL queries.

The authors generated inference control rules targeting high-input-ratio data in the properties of classes by calculating the input ratio of real input data in the system, and then the authors executed a semantic search by SPARQL query by setting very complicated search scenarios, for which it would be difficult to deduce results via a simple keyword search. As a result, it was confirmed that the search results show the logical combination of semantically related term data.

The proposed terminology ontology system was constructed with the author keywords from research papers, it will be useful in searching the research papers which include the keywords as search results by the complex combination of semantic relation. And the Structural Terminology Net database could be utilized as an index database in retrieval services and the mining of informal big data through the application of well-defined semantic concepts to each term.

This paper presented a methodology for supporting IR using expanded queries based on a novel model of structural terminology-based ontology. The user who wants to access the specific topic can create query that brings the semantically relevant information. The search results show the logical combination of semantically related term data, which would be difficult to deduce results via traditional IR systems.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

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.

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.

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.

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.

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.

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.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.