• The KIPS Transactions:PartD
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• v.14D no.7
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• pp.819-828
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• 2007

In this paper, we propose a topic maps matching and merging approach based on the syntactic or semantic characteristics and constraints of the topic maps. Previous schema matching approaches have been developed to enhance effectiveness and generality of matching techniques. However they are inefficient because the approaches should transform input ontologies into graphs and take into account all the nodes and edges of the graphs, which ended up requiring a great amount of processing time. Now, standard languages for developing ontologies are RDF/OWL and Topic Maps. In this paper, we propose an enhanced version of matching and merging technique based on topic partitioning, several matching operations and merging conflict detection.

• Journal of KIISE:Information Networking
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• v.32 no.2
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• pp.123-133
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• 2005

In recent years, service discovery is one of the major technologies of home networks which head for a pervasive computing environment. However, existing service discovery techniques are difficult to understand semantics, and they only provide syntactic level service matching. To solve these problems, we have designed and developed ontology for semantic service discovery. Our ontology could enrich the amount of devices and services representations with semantics, and the relation of devices and service could be efficiently described through primitive service. For representing context information of devices, we describe attributes of device including location information, device status and etc. To determine whether the developed ontology can be applied to service discovery systems, we have implemented a semantic service discovery system by extension of the existing Jini lookup service. Also, we have evaluated our ontology with associated software environment according to some experiment scenarios, and have proved the usefulness of our ontology-based semantic service discovery system.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.4
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• pp.219-228
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• 2006

Many biomedical research groups have been trying to share their outputs to increase the efficiency of research. As part of their efforts, a common ontology named Gene Ontology(GO), which comprises controlled vocabulary for the functions of genes, was built. However, data from many research groups are distributed and most systems don't support integrated semantic queries on them. Furthermore, the semantics of the associations between concepts from external classification systems and GO are still not clarified, which makes integrated semantic query infeasible. In this paper we present an ontology matching and integration system, called AutoGOA, which first resolves the semantics of the associations between concepts semi-automatically, and then constructs integrated ontology containing concepts from GO and external classification systems. Also we describe a web-based application, named GOGuide II, which allows the user to browse, query and visualize integrated data.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.121-123
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• 2003

본 연구에서는 현재 웹 환경에서의 단순한 string matching 검색에 대한 한계점을 해결하기 위해서 온톨로지 기반의 자원검색 방안을 논한다. 테스트 베드로서 제주도의 숙박, 관광정보에 따른 온톨로지를 DAML+OIL언어로 생성하고 Jena에서 지원하는 API를 이용하여 사용자가 원하는 정보검색을 수행할 수 있는 테스트 베드 구축 방안도 제시한다.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.265-268
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• 2004

Keyword matching technique which is used in most information retrieval systems is unfit for efficient processing of geometrically increasing information. The problem can be solved by using semantic information and an efficient method of semantic processing is introduced in this paper. The technique uses conceptual graph to represent the semantic information and apply it for information retrieval. The implemented system can perform exact matching and partial matching. Partial matching has two different types. One is syntactic partial matching and the other is semantic partial matching. The semantic semilaries are measured by the subclass relations in the ontology. The introduced technique can be used not only information retrieval but also in various applications such as an implementation of dynamic hyperlinks.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.8
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• pp.281-286
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• 2018

With recent increase in complexity and variety of information and massively available information, interest in and necessity of ontology has been on the rise as a method of extracting a meaningful search result from massive data. Although there have been proposed many methods of extracting the ontology from a given text of a natural language, the extraction based on most of the current methods is not consistent with the structure of the ontology. In this paper, we propose a method of automatically creating ontology by distinguishing a term needed for establishing the ontology from a text given in a specific domain and extracting various relationships between the terms based on the pattern-based method. To extract the relationship between the terms, there is proposed a method of reducing the size of a searching space by taking a matching set of patterns into account and connecting a join-set concept and a pattern array. The result is that this method reduces the size of the search space by 50-95% without removing any useful patterns from the search space.

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.17-21
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• 2017

In this paper, the service execution accuracy was compared by ontology based rule inference method and machine learning method, and the amount of data at the point when the service execution accuracy of the machine learning method becomes equal to the service execution accuracy of the rule inference was found. The rule inference, which measures service execution accuracy and service execution accuracy using accumulated data and pattern matching on service results. And then machine learning method measures service execution accuracy using cross validation data. After creating a confusion matrix and measuring the accuracy of each service execution, the inference algorithm can be selected from the results.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.34-46
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• 2023

A growing number of large scale knowledge graphs raises several issues how knowledge graph data can be organized, discovered, and integrated efficiently. We present a novel semantic-based mashup platform for contents convergence which consists of acquisition, RDF storage, ontology learning, and mashup subsystems. This platform servers a basis for developing other more sophisticated applications required in the area of knowledge big data. Moreover, this paper proposes an entity matching method using graph convolutional network techniques as a preliminary work for automatic classification and discovery on knowledge big data. Using real DBP15K and SRPRS datasets, the performance of our method is compared with some existing entity matching methods. The experimental results show that the proposed method outperforms existing methods due to its ability to increase accuracy and reduce training time.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.596-600
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• 2009

As the amount of available information increases rapidly, sometimes the efficient search method alone is not enough to obtain necessary information in timely manner. Therefore additional support is needed to share the burden of searching for and filtering information. In the area of ubiquitous computing, computer systems existing everywhere should be able to proactively provide information just in time. Resource matching is essential in order to develop a system searching and recommending information required for a user in a specific context. This paper describes the infrastructure and methodology of information providing including systematical organization representation, ontological resource demarcation, and resource matching in the environment of a research institute. A specific application was developed to illustrate the proposed approach.

• Journal of Korea Multimedia Society
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• v.7 no.9
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• pp.1195-1207
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• 2004

Schema matching is prerequisite to an automated transformation of XML documents. Because previous works about schema matching compute all semantically-possible matchings, they produce many-to-many matching relationships. Such imprecise matchings are inappropriate for an automated transformation of XML documents. This paper presents an efficient schema matching algorithm that computes precise one-to-one matchings between two schemas. The proposed algorithm consists of two steps: preliminary matching relationships between leaf nodes in the two schemas are computed and one-to-one matchings are finally extracted based on a proposed path similarity. Specifically, for a sophisticated schema matching, the proposed algorithm is based on a domain ontology as well as a lexical database that includes abbreviations and synonyms. Experimental results with real schemas from an e-commerce field show that the proposed method is superior to previous works, resulting in an accuracy of 97% in average.