Eun-Kyong Kim

Bio: Eun-Kyong Kim is an academic researcher. The author has contributed to research in topics: Ontology Inference Layer & Process ontology. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

The Comparative Study on the Methodologies of Building Ontology toward Semantic Web

Abstract: This is the comparative study about analyzing the methodologies of building ontology with IEEE Standard 1074-1997. The methodologies are chosen to be analyzed. They are OTK, CommanKADS, ONIONS and Noy & McGuinness`s Ontology Development 101. On the basis of analyzing, the ontology development process is proposed after the strong points of four methodologies are accepted but the weak points of them are supplemented. The sixth development steps are following: 1) Conducting the feasibility study about ontology building as the pre-development process 2) Setting up the purpose of the ontology development as the starting point of the building 3) Considering the integration of the existing ontologies for the knowledge reuse 4) Constructing the ontology by defining the concepts and relations 5) Evaluating and testing the ontology for the completeness 6) Containing the ontology maintenance for the sustainable use.

Development of Manufacturing Ontology-based Quality Prediction Framework and System : Injection Molding Process

Abstract: Today, many manufacturing companies realize that collaboration is crucial for their survival. Especially, in the perspective of quality, the importance of collaboration is emphasized because economic loss increases exponentially while defective parts go through the process in supply chain. However, the manufacturing companies are facing two main difficulties in implementing collaborative relationships with their suppliers. First, it is difficult for the suppliers to produce reliable products due to their obsolete facilities. The problem gets worse for second- or third-tire vendors. Second, the companies experience the lack of universally understandable set of terminology and effective methodologies for knowledge representation. Ontology is one of the best approaches to expressing and processing a domain knowledge. In this paper, we propose the manufacturing ontology-based quality prediction framework to represent and share the knowledge of industrial environment and to predict product quality in manufacturing processes. In addition, we develop the ontology-based quality prediction system based on the proposed framework. We carried out a series of experiments for an injection molding process at an automotive part supplier. The experimental results demonstrated that the proposed framework and system can be successfully applicable in manufacturing industry.

Automatic Metadata Conversion System with Ontology

Abstract: Some information providers or information circulation centers are collecting various metadata to serve users. The collected information has various structures and expression types of contents as well as file types. For information service, these various types of metadata should be processed and converted into one unified form. Converting metadata has lots of difficulties and mapping between elements of metadata schema is most important and difficult. We are making an automatic metadata conversion system with ontology. This paper describes the conversion method with ontology and schema mapping tool in the system.

Implementation of a Geo-Semantic App by Combining Mobile User Contexts with Geographic Ontologies

Abstract: This paper describes a GIS framework for geo-semantic information retrieval in mobile computing environments. We built geographic ontologies of POI (point of interest) and weather information for use in the combination of semantic, spatial, and temporal functions in a fully integrated database. We also implemented a geo-semantic app for Android-based smartphones that can extract more appropriate POIs in terms of user contexts and geographic ontologies and can visualize the POIs using Google Maps API (application programming interface). The feasibility tests showed our geo-semantic app can provide pertinent POI information according to mobile user contexts such as location, time, schedule, and weather. We can discover a baking CVS (convenience store) in the test of bakery search and can find out a drive-in theater for a not rainy day, which are good examples of the geo-semantic query using semantic, spatial, and temporal functions. As future work, we should need ontology-based inference systems and the LOD (linked open data) of various ontologies for more advanced sharing of geographic knowledge.