• Knowledge Management Research
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• v.19 no.3
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• pp.47-61
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• 2018

A knowledge map, a taxonomy of knowledge repositories, must have capabilities supporting and enhancing knowledge user's activity to search and select proper knowledge for problem-solving. Conventional knowledge maps, however, have been hierarchically categorized, and could not support such activity that must coincide with the user's cognitive process for knowledge utilization. This paper, therefore, aims to verify and develop a methodology to build a networked knowledge map that can support user's activity to search and retrieve proper knowledge based on the referential navigation between content-relevant knowledge. This paper deploys keywords as the semantic information between knowledge, because they can represent the overall contents of a given document, and because they can play the role of semantic information on the link between related documents. By aggregating links between documents, a document network can be formulated: a keyword-based networked knowledge map can be finally built. Domain expert-based validation test was also conducted on a networked knowledge map of 50 research papers, which confirmed the performance of the proposed methodology to be outstanding with respect to the precision and recall.

• Journal of KIISE:Software and Applications
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• v.32 no.3
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• pp.202-214
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• 2005

The semantic web is the web paradigm that represents not general link of documents but semantics and relation of document. In addition it enables software agents to understand semantics of documents. We propose a semantic search based on inference with ontologies, which has the following characteristics. First, our search engine enables retrieval using explicit ontologies to reason though a search keyword is different from that of documents. Second, although the concept of two ontologies does not match exactly, can be found out similar results from a rule based translator and ontological reasoning. Third, our approach enables search engine to increase accuracy and precision by using explicit ontologies to reason about meanings of documents rather than guessing meanings of documents just by keyword. Fourth, domain ontology enables users to use more detailed queries based on ontology-based automated query generator that has search area and accuracy similar to NLP. Fifth, it enables agents to do automated search not only documents with keyword but also user-preferable information and knowledge from ontologies. It can perform search more accurately than current retrieval systems which use query to databases or keyword matching. We demonstrate our system, which use ontologies and inference based on explicit ontologies, can perform better than keyword matching approach .

• Journal of Information Processing Systems
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• v.5 no.3
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• pp.159-166
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• 2009

As shown in Wikipedia, tagging or cross-linking through major keywords in a document collection improves not only the readability of documents but also responsive and adaptive navigation among related documents. In recent years, the Semantic Web has increased the importance of social tagging as a key feature of the Web 2.0 and, as its crucial phenotype, Tag Cloud has emerged to the public. In this paper we provide an efficient method of automated in-text keyword tagging based on large-scale controlled term collection or keyword dictionary, where the computational complexity of O(mN) - if a pattern matching algorithm is used - can be reduced to O(mlogN) - if an Information Retrieval technique is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that automatic in-text tagging with keywords filtered by Information Retrieval speeds up to about 6 $\sim$ 40 times compared with the fastest pattern matching algorithm.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.119-134
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• 2018

A knowledge map as the taxonomy used in a knowledge repository should be structured to support and supplement knowledge activities of users who sequentially inquire and select knowledge for problem solving. The conventional knowledge map with a hierarchical structure has the advantage of systematically sorting out types and status of the knowledge to be managed, however it is not only irrelevant to knowledge user's process of cognition and utilization, but also incapable of supporting user's activity of querying and extracting knowledge. This study suggests a methodology for constructing a networked knowledge map that can support and reinforce the referential navigation, searching and selecting related and chained knowledge in term of contents, between knowledge. Regarding a keyword as the semantic information between knowledge, this research's networked knowledge map can be constructed by aggregating each set of knowledge links in an automated manner. Since a keyword has the meaning of representing contents of a document, documents with common keywords have a similarity in content, and therefore the keyword-based document networks plays the role of a map expressing interactions between related knowledge. In order to examine the feasibility of the proposed methodology, 50 research papers were randomly selected, and an exemplified networked knowledge map between them with content relevance was implemented using common keywords.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.509-516
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• 2004

Semantic interpretation of image is incomplete without some mechanism for understanding semantic content that is not directly visible. For this reason, human assisted content-annotation through natural language is an attachment of textual description to image. However, keyword-based retrieval is in the level of syntactic pattern matching. In other words, dissimilarity computation among terms is usually done by using string matching not concept matching. In this paper, we propose a method for computerized semantic similarity calculation In WordNet space. We consider the edge, depth, link type and density as well as existence of common ancestors. Also, we have introduced method that applied similarity measurement on semantic image retrieval. To combine wi#h the low level features, we use the spatial color distribution model. When tested on a image set of Microsoft's 'Design Gallery Line', proposed method outperforms other approach.

• Korean Association of Arts Management
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• no.49
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• pp.5-35
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• 2019

The main purpose of this study was to use semantic network analysis to examine the international trend of arts management and other studies pertinent to this field. The subject was based on 357 keywords listed on the abstract of 185 research papers in the International Journal of Arts Management. To examine the most current trends of arts management based studies the time frame was restricted from 2008 to 2017. To briefly summarize the result, first, 'museum' was the most frequently appeared keyword. This was followed by 'performing arts' and 'arts' with more than 20 appearances. 'Motion picture industry' and 'theater' were the next frequently appeared keywords. 'Customer behavior' and 'market strategy', keywords related to management, were also included in the high ranked group along with art related keywords. Second, yearly research trend shows that arts management has been regularly studied for past ten years with average of 19 research papers with about 53 keywords. Keywords such as 'museum' and 'performing arts' has been regularly studied for past ten years. 'Culture', 'theater' and 'motion pictures industry' does not regularly appear in the result of yearly research trend but nevertheless they have sparsely made an appearance along the past decade. 'Art gallery' has not been cited till 2011 but from 2012 it was regularly and continuously made an appearance in the yearly research trend. Overall, the yearly trend result shows that the trend of international arts management studies within IJAM, was at first centered on fine arts but as the time passed there has been diversified keywords related to management. Third, 'performing art' and 'art' has the highest link frequency(34). Fourth, density result was 0.039 which shows that the keyword density is not very high. Fifth, 'art', 'performing art', 'museum', 'theater' and 'brand' were positioned in the middle when looking at the visualized version of centrality result. This means that these five keywords has the highest centrality among other keywords.