• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.226-231
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• 2017

Word representation, an important area in natural language processing(NLP) used machine learning, is a method that represents a word not by text but by distinguishable symbol. Existing word embedding employed a large number of corpora to ensure that words are positioned nearby within text. However corpus-based word embedding needs several corpora because of the frequency of word occurrence and increased number of words. In this paper word embedding is done using dictionary definitions and semantic relationship information(hypernyms and antonyms). Words are trained using the feature mirror model(FMM), a modified Skip-Gram(Word2Vec). Sense similar words have similar vector. Furthermore, it was possible to distinguish vectors of antonym words.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.9-15
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• 2007

As the information of the internet increased, importance of sematic web for collecting and integration of these informations to support decision making of some group or ordinary people are growing as well. Basis structure that composes semantic web is ontology and languages like XML, RDF/RDF schema and OWL are basis means that compose ontology schema. When composes and manages Ontology schema, one of the important consideration point is that schema is changed as times go by. Therefore, change of domain of schema, change of data concept or change of relation between resource etc. are reflected in the ontology system. In this study, we suggest semantic web schema management skill in terms of version management. We categorized version change forms and created version graph for checking of version transition. With created version graph, we define transitivity rule and propose schema tag for detail application which enables extending of applicable version schema.