• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.109-136
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• 2008

In this survey paper, we discuss biomedical ontologies and major text mining techniques applied to biomedicine and healthcare. Biomedical ontologies such as UMLS are currently being adopted in text mining approaches because they provide domain knowledge for text mining approaches. In addition, biomedical ontologies enable us to resolve many linguistic problems when text mining approaches handle biomedical literature. As the first example of text mining, document clustering is surveyed. Because a document set is normally multiple topic, text mining approaches use document clustering as a preprocessing step to group similar documents. Additionally, document clustering is able to inform the biomedical literature searches required for the practice of evidence-based medicine. We introduce Swanson's UnDiscovered Public Knowledge (UDPK) model to generate biomedical hypotheses from biomedical literature such as MEDLINE by discovering novel connections among logically-related biomedical concepts. Another important area of text mining is document classification. Document classification is a valuable tool for biomedical tasks that involve large amounts of text. We survey well-known classification techniques in biomedicine. As the last example of text mining in biomedicine and healthcare, we survey information extraction. Information extraction is the process of scanning text for information relevant to some interest, including extracting entities, relations, and events. We also address techniques and issues of evaluating text mining applications in biomedicine and healthcare.

• Journal of Korea Multimedia Society
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• v.13 no.9
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• pp.1263-1271
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• 2010

Ontologies, the important implementation tools for semantic web, are widely used in various areas such as search, reasoning, and knowledge representation. Developing well-defined ontologies, however, requires a lot of resources in terms of time and materials. There have been efforts to construct ontologies automatically to overcome these problems. In this paper, ontologies are automatically constructed from the natural languages sentences directly. To do this, the analysis of morphemes and a sentence structure is performed at first. then, the program finds predicates inside the sentence and the predicates are transformed to the corresponding ontology predicates. For matching the corresponding ontology predicate from a predicate in the sentence, we develop the "predicate ontology". An experimental comparison between human ontology engineer and the program shows that the proposed system outperforms the human engineer in an accuracy.

• Language and Information
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• v.14 no.1
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• pp.165-196
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• 2010

The primary goal of this paper is to propose methods of enriching two heterogeneous ontologies: Sejong Semantic Classes (SJSC) and KorLexNoun 1.5 (KLN). In order to achieve this goal, this study introduces the pros and cons of two ontologies, and analyzes the error patterns found during the fine-grained manual mapping processes between them. Error patterns can be classified into four types: (1) structural defectives involved in node branching, (2) errors in assigning the semantic classes, (3) deficiency in providing linguistic information, and (4) lack of the lexical units representing specific concepts. According to these error patterns, we propose different solutions in order to correct the node branching defectives and the semantic class assignment, to complement the deficiency of linguistic information, and to increase the number of lexical units suitably allotted to their corresponding concepts. Using the results of this study, we can obtain more enriched ontologies by correcting the defects and errors in each ontology, which will lead to the enhancement of practicality for syntactic and semantic analysis.

• The Journal of Society for e-Business Studies
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• v.9 no.4
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• pp.85-102
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• 2004

Currently, developed technologies for semantic web services discovery are based on ontologies. These ontologies are DAML-S(DARPA Agent Markup Language for Services) and Process Handbook Project of MIT. These technologies have some problems for intelligent web services discovery. So, in this paper we analyzed those ontologies and proposed TM-S, Topic Maps for Services. TM-S is the presentation model for semantic web services. And TM-S includes benefits and complements weaknesses of those ontologies. And we proposed TMS-QL, TM-S Query Language. TMS-QL is query language for intelligent web services discovery. At last, we designed and implemented intelligent web service discovery system that deals TM-S ontology and TMS-QL

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.437-448
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• 2004

This paper presents an approach to the development of bio-ontology using the Object-oriented Ontology Manager(OOM). OOM views a term of an ontology as an object which can be an instance or a concept. OOM facilitates the semi-automatic construction of ontologies by an intuitive interface and by inferencing with links among complicated and informative ontology terns. The main advantage of OOM is simple-to-use not compromising expressiveness so that ontologies in a complicated domain such as bioinformatics can be modeled intuitively. The ontologies constructed by OOM are easily exported to ontologies in other ontology languages without semantic loss because the structures of both the ontology by OOM and the ontologies in most of standard ontology languages are analogous. A translator to another standard ontology language is also provided by OOM so that the ontology can be combined with others to be applied to more complicated applications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.6
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• pp.1220-1228
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• 2009

Ontologies, which are the core of the Semantic Web, are usually limited by specific domains or created by defining meanings and relationships that depend on the heuristic. However, the creation of an ontology is not only very difficult but also very time-consuming. In contrast with ontologies that are used in specific fields, an ontology for the Web entails an unlimited scope of knowledge and expression of information. Hence, it is hard to express information in the same way that is used to create ontologies in specific fields. Therefore, the automatic generation of an ontology takes very important role in the Semantic Web. In this paper, to make ontologies automatically, we suggest the methods to create and renew ontologies by expanding keywords related to the index-terms which are extracted from the search keywords which users input in the search engines by analyzing the morphemes.

• The KIPS Transactions:PartD
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• v.18D no.2
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• pp.133-142
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• 2011

To ensure the successful employment of semantic web services, it is essential that they rely on the use of high quality ontologies. However, building such ontologies is difficult and costly, thus hampering web service deployment. This study automatically builds ontologies from WSDL documents and their underlying semantics, and presents discovery methods of similar web service operations using these ontologies. The key ingredient is techniques that cluster parameters in the collection of web services into semantically meaningful concepts, and capture the hierarchical relationships between the words contained in the tag. We implement an operation retrieval system for web services. This system finds out a ranked set of similar operations using a novel similarity measurement method, and selects the most optimal operation which satisfies user's requirements. It can be directly used for the web services composition.

• Journal of Korean Medical classics
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• v.22 no.3
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• pp.97-105
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• 2009

We in this paper have constructed herb and formula ontologies. Herb instances and formula instances can be distinguished by nature, used part, effect, disease pattern, symptom, and formula and constituent herb, dosage, effect, disease pattern, symptom, and medical book, respectively. The knowledge for herbs and formulas in ontology is formalized with the distinguishable elements and their relations. Based on the herb and formula ontologies, we propose the three reasoning rules as follows: In herb ontology, the relation between herb and disease can be reasoned if there are the relation between herb and effect, and effect and disease. In formula ontology, there are two reasoning rules. First, if each constituent herb, dosage, effect, disease pattern, and symptom of two formulas is same, it can be reasoned that two formulas are same though the medical books of the formulas are different. Second, if each constituent herb and dosage is same in two formula, it can be reasoned that each formula has all of effects, disease patterns, and symptoms of formulas. In future study, we study other ontologies such as disease ontology with respect to Korean Medicine and define the reasoning rules about the ontologies.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.557-568
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• 2008

An ontology as the core element of Semantic Web is a formal specification of a conceptualization of shared domain knowledge. The use of well-defined ontologies can increase the quality of interoperable information systems in the area of Semantic Web. However, in practice, it is not easy to develop high-quality ontologies which have no errors. Therefore, with methodologies for ontology design, various methods or tools for ontology analysis supporting for error-detection might be very helpful for ontology developers. In this paper, we propose a novel approach for analyzing the core constructs of ontology based on the Formal Concept Analysis and develop a tool that supports error-checking ontologies. Our approach can serve not only as a guidance to modify the existing ontologies, but also as a valuable tool in developing high-quality ontologies.

• Journal of Computing Science and Engineering
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• v.3 no.2
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• pp.90-108
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• 2009

In this paper, we present an ontology mediation solution based on the methods frequently used in Formal Concept Analysis. Our approach of mediation is based on the existence of instances associated to two source ontologies, then we can generate concepts in a new ontology if and only if they share the same extent. Hence our approach creates a merged ontology which captures the knowledge of these two source ontologies. The main contributions of this work are (i) to enable the creation of concepts not originally in the source ontologies, (ii) to propose a solution to label these emerging concepts and finally (iii) to optimize the resulting ontology by eliminating redundant or non pertinent concepts. Another contribution of this work is to emphasize that several forms of mediated ontology can be defined based on the relaxation of certain criteria produced from our method. The solution that we propose for tackling these issues is an automatic solution, meaning that it does not require the intervention of the end-user, excepting for the definition of the common set of ontology instances.