The paper introduces KorLex, Korean WordNet, which uses Princeton WordNet (PWN) as a reference model as well as a pivot that provides KorLex with the multilingual interface. Sub-section 2.1 describes the background for which we intended to build KorLex by ourselves, and the motivations for which we selected PWN as a reference model among other wordnets. Sub-sections 2.2 and 2.3 deal with the semantic features that share both PWN and KorLex, and the linguistic features that we attach to KorLex in order to improve Korean Language Processing (KLP). Section 3 presents two representative applications of KorLex in the field of Natural Language Processing: (1) in KLP environment, <Korean Spell/Grammar Checker (KSGC)> that we have developed for the last 30 years, and for which we decided to elaborate KorLex version 1.5 and to attach the sophisticate information of Korean language to the version 2.0; (2) in multilingual environment, <Korean-Korean_Sign_Language Machine Translation (K2KSL MT)>, which would not be successfully developed without the hierarchical semantic structure of KorLex as well as PWN, since the hierarchy enables K2KSL MT to solve logically the notorious ‘data sparseness (= lack of lexicon)’ problem that KSL encounters during the translation process, like in other bilingual or multilingual MT systems.

In the game graphic design process, design results are based on ideas that drives major decisions. Many information and stimuli could help to determine an initial game graphic design concept which is called concept generation. In this study Related Search Term and WordNet stimuli were given to the game design students in the concept generation. This study seeks to clarify which kind of stimuli contributes more to the creativity of the game graphic design concept. As a result, significant difference was found in creativity scores between two conditions. This result showed that related search terms which are based on collective knowledge could assist as stimuli on concept generation more effectively than WordNet which offers semantic dictionary definitions.