• 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.

• The KIPS Transactions:PartD
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• v.15D no.1
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• pp.47-60
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• 2008

Success of a semantic web application, currently base on web technology, depend on web ontology construction that provided rule and inference function about knowledge. For, this study compared the ontology construction methods that were already proposed, and analyzed, and investigated characteristics of semantic web and web ontology, investigated characteristics of semantic web and web ontology, and defined characteristics of web ontology as-based technology of a semantic web application and knowledge representation steps, and studied a technical element that related currently web technology, and proposed a web ontology construction method for a semantic web application with bases to these. Established web ontologies of various knowledge fields as applied the construction method that proposed. Also evaluate performance of web ontology through inference verification of web ontologies established, web ontologies evaluated performance of web ontology as used inference verification. According to this, we confirmed that proposed construction method that can establish the ontology suitable for semantic web application.

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.333-342
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• 2010

Building ontology is the key factor to construct semantic web. However, this is time-consuming process. Hence, there are several approaches which automatically generate the ontologies from relational databases. Current studies on the automatic generation of the ontologies from relational database are focused on generating the ontology by analyzing the database schema and stored data. These studies generate the ontology by analyzing only tables and constraints in the schema and ignore view definitions. However, view definitions are defined by a database designer considering the domain of the database. Hence, by considering view definitions, additional classes and hierarchical relationships can be generated. And these are useful in answering queries and integration of ontologies. In this paper, we formalize the generation of classes and hierarchical relationships by analyzing existing methods, and we propose the method which generates additional classes and hierarchical relationships by analyzing view definitions. Finally, we analyze the generated ontology by applying our method to synthetic data and real-world data. We show that our method generates meaningful classes and hierarchical relationships using view definitions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2632-2637
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• 2010

Information in UbiComp Environment are transformed to knowledge by relationship in a spatio-temporal location, and then became intelligent contents with task procedures or application models. The entities in U-City has lots of relationships. It is important in U-City contents to provide intelligent and personalized response to meet the intention of users. We extend the spatial ontology model of SPIRIT to other domain. Domain ontologies are consist of type, relation, and instance ontologies. When the relationship model by shared concepts are not defined, we used the spatio-temporal events to find relationships. So we proposed the methods to recommend semantically related terms, not syntactically.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.139-148
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• 2006

In the collaborative product design environment, the communication between designers is important to capture design intents and to share a common view among the different but semantically similar terms. The Semantic Web supports integrated and uniform access to information sources and services as well as intelligent applications by the explicit representation of the semantics buried in ontology. Ontologies provide a source of shared and precisely defined terms that can be used to describe web resources and improve their accessibility to automated processes. Therefore, employing ontologies on assembly modeling makes assembly knowledge accurate and machine interpretable. In this paper, we propose a framework of semantic assembly modeling using ontologies to share design information. An assembly modeling ontology plays as a formal, explicit specification of a shared conceptualization of assembly design modeling. In this paper, implicit assembly constraints are explicitly represented using OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language). The assembly ontology also captures design rationale including joint intent and spatial relationships.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.91-104
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• 2007

Ontology matching technique is one of the most important techniques in the Semantic Web as well as in other areas. Ontology matching algorithm takes two ontologies as input, and finds out the matching relations between the two ontologies by using some parameters in the matching process. Ontology matching is very useful in various areas such as the integration of large-scale ontologies, the implementation of intelligent unified search, and the share of domain knowledge for various applications. In general cases, the performance of ontology matching is estimated by measuring the matching results such as precision and recall regardless of the requirements that came from the matching environment. Therefore, most research focuses on controlling parameters for the optimization of precision and recall separately. In this paper, we focused on the harmony of precision and recall rather than independent performance of each. The purpose of this paper is to propose a methodology that determines parameters for the desired ratio of precision and recall that is appropriate for the requirements of the matching environment.

• Journal of Information Science Theory and Practice
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• v.12 no.2
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• pp.22-35
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• 2024

There has been a growing interest in halal-related ontology research in recent years, as ontology has gained recognition in the halal industry. This paper discusses the development of a flavouring ontology that will assist halal management auditors in predicting the halal status of flavours in order to process food producers' applications for halal certification. The development of a flavouring ontology is based on multiple references, because the auditors of halal management divisions must consult a variety of sources independently in order to determine the halal status of flavourings. The process includes 1) determining the ontology goal and scope, 2) building ontologies, and 3) evaluating the ontologies. The researcher used Protégé to design the ontologies, and Phyton was used to develop a prototype based on flavouring ontology. The developed ontology consists of four classes, nine sub-classes, and 11 relationships. The evaluation of the ontology using the prototype revealed that the majority of experts were satisfied with the information generated by the ontology in the prototype, particularly in relation to synonyms and the hierarchical structure of a flavour. However, the experts suggest improvements in terms of flavour metadata, especially on raw materials and natural occurrence data, so that the flavour information retrieved is comprehensive and accurate.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.571-576
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• 2024

Ontologies are created by domain experts, but the same content may be expressed differently by each expert due to different understandings of domain knowledge. Since the ontology standardization is still lacking, multiple ontologies can be exist within the same domain, resulting in a phenomenon called the ontology heterogeneity. Therefore, we propose a novel ontology matching method that combines SCBOW(: Siames Continuois Bag Of Words) and BERT(: Bidirectional Encoder Representations from Transformers) models to solve the ontology heterogeneity issue. Ontologies are expressed as a graph and the SimRank algorithm is used to solve the one-to-many problem that can occur in ontology matching problems. Experimental results showed that our approach improves performance by about 8% over traditional matching algorithm. Proposed method can enhance and refine the alignment technology used in ontology matching.

• 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 the Institute of Electronics Engineers of Korea CI
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• v.41 no.4
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• pp.23-30
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• 2004

Ontologies play an important role in the Semantic Web by providing well-defined meaning to ontology consumers. But as the ontologies are authored in a bottom-up distributed mimer, a large number of overlapping ontologies are created and used for the similar domains. Ontology sharing and reuse have become a distinguished topic, and ontology merging and alignment are the solutions for the problem. Ontology merging and alignment algorithms previously proposed detect conflicts between concepts by making use of only local syntactic information of concept names. And they depend only on a semi-automatic approach, which makes ontology engineers tedious. Consequently, the quality of merging and alignment tends to be unsatisfying. To remedy the defects of the previous algorithms, we propose a new algorithm for ontology merging and alignment which uses local and global semantic set of a concept. We evaluated our algorithm with several pairs of ontologies written in OWL, and achieved around 91% of precision in merging and alignment. We expect that, with the widespread use of web ontology, the need for ontology sharing and reuse ill become higher, and our proposed algorithm can significantly reduce the time required for ontology development. And also, our algorithm can easily be applied to various fields such as ontology mapping where semantic information exchange is a requirement.