Hyoung Kim-Joo

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Hyoung Kim-Joo is an author.

Publications

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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
FolksoViz: A subsumption-based folksonomy visualization using the wikipedia Journal of KISS: Computing Practices 2008 Folksonomy, which is created through the collaborative tagging from many users, is one of the driving factors of Web 2.0. Tags are said to be the web metadata describing a web document. If we are able to find the semantic subsumption relationships between tags created through the collaborative tagging, it can help users understand the metadata more intuitively. In this paper, targeting del.icio.us tag data, we propose a method named {FolksoViz} for deriving subsumption relationships between tags by using Wikipedia texts. For this purpose, we propose a statistical model for deriving subsumption relationships based on the frequency of each tag on the Wikipedia texts, and {TSD} {(Tag} Sense Disambiguation) method for mapping each tag to a corresponding Wikipedia text. The derived subsumption pairs are visualized effectively on the screen. The experiment shows that our proposed algorithm managed to find the correct subsumption pairs with high accuracy. 0 0
Schema and constraints-based matching and merging of Topic Maps Information Processing and Management 2007 In this paper, we propose a multi-strategic matching and merging approach to find correspondences between ontologies based on the syntactic or semantic characteristics and constraints of the Topic Maps. Our multi-strategic matching approach consists of a linguistic module and a Topic Map constraints-based module. A linguistic module computes similarities between concepts using morphological analysis, string normalization and tokenization and language-dependent heuristics. A Topic Map constraints-based module takes advantage of several Topic Maps-dependent techniques such as a topic property-based matching, a hierarchy-based matching, and an association-based matching. This is a composite matching procedure and need not generate a cross-pair of all topics from the ontologies because unmatched pairs of topics can be removed by characteristics and constraints of the Topic Maps. Merging between Topic Maps follows the matching operations. We set up the {MERGE} function to integrate two Topic Maps into a new Topic Map, which satisfies such merge requirements as entity preservation, property preservation, relation preservation, and conflict resolution. For our experiments, we used oriental philosophy ontologies, western philosophy ontologies, Yahoo western philosophy dictionary, and Wikipedia philosophy ontology as input ontologies. Our experiments show that the automatically generated matching results conform to the outputs generated manually by domain experts and can be of great benefit to the following merging operations. 2006. 0 0