• The Journal of Korean Association of Computer Education
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• v.9 no.6
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• pp.29-39
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• 2006

The Web is evolving quantitatively into an explosive development. However, users usually have heavy burden of searching information because of the absence of contextual meaning on the Web. Due to an enormous amount of information, users have to endure for finding strong cohesive keywords by themselves and read each of the documents with enduring effort. This paper proposes an efficient method of searching more relative documents than current KEM-based searching systems on the Web by using contextual meaning. We designed a domain ontology on computer hardware, and a searching system which was searching those e-Learning objects. Owing to the Ontology-applied search system, information such as educational materials and related multimedia can be easily provided to the users. Further, learners could be informed of relationship of knowledge, e.g., class hierarchy, properties and values, and so on. The request results are semantically related to users' needs, and thus the system provides a learner-centered searching.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.493-501
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• 2022

This paper is a study on context-sensitive spelling error correction and uses the Korean WordNet (KorLex)[1] that defines the relationship between words as a graph to improve the performance of the correction[2] based on the vector information of the word embedded in the correction technique. The Korean WordNet replaced WordNet[3] developed at Princeton University in the United States and was additionally constructed for Korean. In order to learn a semantic network in graph form or to use it for learned vector information, it is necessary to transform it into a vector form by embedding learning. For transformation, we list the nodes (limited number) in a line format like a sentence in a graph in the form of a network before the training input. One of the learning techniques that use this strategy is Deepwalk[4]. DeepWalk is used to learn graphs between words in the Korean WordNet. The graph embedding information is used in concatenation with the word vector information of the learned language model for correction, and the final correction word is determined by the cosine distance value between the vectors. In this paper, In order to test whether the information of graph embedding affects the improvement of the performance of context- sensitive spelling error correction, a confused word pair was constructed and tested from the perspective of Word Sense Disambiguation(WSD). In the experimental results, the average correction performance of all confused word pairs was improved by 2.24% compared to the baseline correction performance.