• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.317-326
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• 2010

To improve the diagnosis and prescription for military personnel, it is required to adopt Clinical Decision Support System (CDSS) in armed forces hospitals. The objective of this paper is to suggest a CDSS for armed forces hospitals using semantic web technologies. To this end, we designed military medical ontologies and military medical rules which consist of the various concepts and rules for supporting medical activities. We developed a semantic web-based CDSS to demonstrate the use of the ontologies and rules for treating military patients. We also showed the process of semantic search for the medical records which are created from the semantic web-based CDSS.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.585-592
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• 2005

The Semantic Web is being studied as the next step in the evolution of the web. In the environment of the Semantic Web, the information must be understandable computers as well as a just human. So we use ontologies for describing the contents of the web resources. Among such ontologies, OWL is proposed as a recommendation by W3C. OWL data is represented as graph structure and the query is evaluated by traversing each node of the graph. In this paper, we propose the optimization technique based on signature to efficiently process the OWL data. Our approach minimizes traversing each node of the graph in query processing.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.834-851
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• 2017

The amount of sources of information available on the web using ontologies as support continues to increase and is often heterogeneous and distributed. Ontology alignment is the solution to ensure semantic interoperability. In this paper, we describe a new ontology alignment approach, which consists of combining structure-based and reasoning-based approaches in order to discover new semantic correspondences between entities of different ontologies. We used the biblio test of the benchmark series and anatomy series of the Ontology Alignment Evaluation Initiative (OAEI) 2012 evaluation campaign to evaluate the performance of our approach. We compared our approach successively with LogMap and YAM++ systems. We also analyzed the contribution of our method compared to structural and semantic methods. The results obtained show that our performance provides good performance. Indeed, these results are better than those of the LogMap system in terms of precision, recall, and F-measure. Our approach has also been proven to be more relevant than YAM++ for certain types of ontologies and significantly improves the structure-based and reasoningbased methods.

• Journal of KIISE
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• v.42 no.3
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• pp.307-319
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• 2015

Current ontology studies use the Hadoop distributed storage framework to perform map-reduce algorithm-based reasoning for scalable ontologies. In this paper, however, we propose a novel approach for scalable Web Ontology Language (OWL) Horst Lite ontology reasoning, based on distributed cluster memories. Rule-based reasoning, which is frequently used for scalable ontologies, iteratively executes triple-format ontology rules, until the inferred data no longer exists. Therefore, when the scalable ontology reasoning is performed on computer hard drives, the ontology reasoner suffers from performance limitations. In order to overcome this drawback, we propose an approach that loads the ontologies into distributed cluster memories, using Spark (a memory-based distributed computing framework), which executes the ontology reasoning. In order to implement an appropriate OWL Horst Lite ontology reasoning system on Spark, our method divides the scalable ontologies into blocks, loads each block into the cluster nodes, and subsequently handles the data in the distributed memories. We used the Lehigh University Benchmark, which is used to evaluate ontology inference and search speed, to experimentally evaluate the methods suggested in this paper, which we applied to LUBM8000 (1.1 billion triples, 155 gigabytes). When compared with WebPIE, a representative mapreduce algorithm-based scalable ontology reasoner, the proposed approach showed a throughput improvement of 320% (62k/s) over WebPIE (19k/s).

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.3
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• pp.11-23
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• 2011

Recently, Open APIs are getting attention with the advent of Web 2.0. Open APIs are used to combine services and generate new services by Mashup. However, the growing number of available Open APIs raises a challenging issue how to locate the desired APIs. We automatically build ontologies from WSDL, WADL, HTML, and their underlying semantics. The key ingredient of our method is a technique that clusters input/output parameters in the collection of API methods into semantically meaningful concepts, and captures the hierarchical relationships between the terms contained in a parameter. These semantic ontologies allow search engines to support a similarity search for Open APIs based on various protocols such as SOAP, REST, JavaScript, and XML-RPC, and significantly improve the quality of APIs matching by the clustering and hierarchical relationships mechanism.

• Journal of the Korean Society for information Management
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• v.26 no.4
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• pp.319-336
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• 2009

Semantic web technology is the evolution of current World Wide Web including a machine-understandable knowledge database, ontology, it may be enable machine and people to work together. However, problems arise when we try to communicate with different data, which are annotated by different ontologies created by different people with different concepts. Thus, to communicate between ontologies, it needs to align between heterogeneous ontologies. When it is aligned between concept nodes of heterogeneous ontologies, one of main problems is a misalignment situation caused by false negative of automatic ontology mapping. So, in this paper, we present a new method to minimize the false negative error in the process of aligning concept nodes of different ontology.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.9
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• pp.904-917
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• 2010

Much research on ontology has been done during the last decade in order to represent knowledge and connect data semantically in AI and Semantic Web areas. However, ontologies might be represented and defined in different ways depending on knowledge and intention of users. It causes heterogeneity problem that the same concept can be differently expressed. This paper introduces a XOB (XMDR-based Ontology Builder) system based on XMDR to resolve the problem. XOB creates ontologies by reusing classes and relations defined in XMDR. XOB therefore is able to either solve or minimize the heterogeneity problem among ontologies. This paper introduces the conceptual model and overall architecture of the proposed system XOB. This paper defines the process, algorithm, ontology generation rule that is required to create ontologies by using concepts registered in XMDR. Our proposal supports higher standardization than the previous approaches, and it provides many advantages such as consistent concept usage, easy semantic exchange, and so on. Therefore, XOB enables high-quality ontology creation and reduces cost for ontology integration and system development.

• 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.

• The Journal of Society for e-Business Studies
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• v.10 no.3
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• pp.1-19
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• 2005

Ontology-based approaches for automated information integration are being widely investigated. The existing approaches explicitly represent the semantics of information sources in ontologies and let the computer system, through aligning or merging the source ontologies, provide a global view of information sources. The problem of aligning or merging different ontologies is a well known problem, and the inter-ontology mappings play an essential role in information integration. To enable simple and well-founded mappings , the ontologies of information sources should be modeled with the same world view and with the same manner of representation. This paper introduces an ontology modeling framework for component libraries, which is developed based on the Guarino's theory of upper ontology . This paper discusses the results of modeling ontologies of mold and die component libraries based on the framework. A Web-based implementation automatically merges the source ontologies.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.93-99
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• 2006

Ontology is an indispensable component in intelligent and semantic processing of knowledge and information, such as in semantic web. Ontology is considered to be constructed generally on the basis of taxonomy of human concepts about the world. However. as human concepts are unstructured and obscure, ontology construction based on the taxonomy of human concepts cannot be realized systematically furthermore automatically. So, we try to do this from the relation among linguistic symbols regarded representing human concepts, in short, words. We show the similarity between taxonomy of human concepts and relation among words. And we propose a methodology to construct and generate automatically ontologies from these relations mon words and a series of algorithm to convert these relations into ontologies. This paper presents the process and concrete application of this methodology.