• International journal of advanced smart convergence
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• v.12 no.3
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• pp.75-88
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• 2023

In a specific domain, experts have different understanding of domain knowledge or different purpose of constructing ontology. These will lead to multiple different ontologies in the domain. This phenomenon is called the ontology heterogeneity. For research fields that require cross-ontology operations such as knowledge fusion and knowledge reasoning, the ontology heterogeneity has caused certain difficulties for research. In this paper, we propose a novel ontology matching model that combines word embedding and a concatenated continuous bag-of-words model. Our goal is to improve word vectors and distinguish the semantic similarity and descriptive associations. Moreover, we make the most of textual and structural information from the ontology and external resources. We represent the ontology as a graph and use the SimRank algorithm to calculate the structural similarity. Our approach employs a similarity queue to achieve one-to-many matching results which provide a wider range of insights for subsequent mining and analysis. This enhances and refines the methodology used in ontology matching.

• 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 Internet Computing and Services
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• v.4 no.4
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• pp.1-8
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• 2003

As the multimedia information retrieval system is advanced, the study of multimedia information retrieval is changing the method of low-level content based image retrieval to the semantical concept based retrieval. in this paper, we apply the theory of ontology to overcome the conceptual heterogeneity in the annotation based image retrieval. And we solve the some problems that happen when the ontology apply. As a result of our study, we try to apply the domain ontology to settle the conceptual heterogenity. In the experimental result, we knew that the semantic distance among the words is pretty dose when we apply the domain ontology than the wordnet. And in this paper, we show the possibility of the semantic image retrieval as we apply the domain ontology in the annotation based image retrieval.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.19-30
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• 2008

Semantic heterogeneity should be overcome to support automated information sharing process between information systems in ontology-based distributed environments. To do so, traditional approaches have been based on explicit mapping between ontologies from human experts of the domain. However, the manual tasks are very expensive, so that it is difficult to obtain ontology mappings between all possible pairs of information systems. Thereby, in this paper, we propose a system to make the existing mapping information sharable and exchangeable. It means that the proposed system can collect the existing mapping information and aggregate them. Consequently, we can estimate the ontology mappings in an indirect manner. In particular, this paper focuses on query propagation on the distributed networks. Once we have the indirect mapping between systems, the queries can be efficiently transformed to automatically exchange knowledge between heterogeneous information systems.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.96-97
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• 2011

Heterogeneity in different Health Information Systems (HIS) processes persists to be the most demanded problem to be resolved in healthcare domain. The only way to resolve this problem is to practice health standards. One of such standards is Health Level Seven (HL7), used for the communication of medical information between healthcare systems. HL7 V3 has the aim to support all healthcare workflows. It defines series of electronic messages called interactions to support healthcare workflows. These interactions are part of the Interaction Model. Different healthcare organizations can conform to different process workflows based on their requirements. The heterogeneity in workflows results in communication blockade between sender and receiver healthcare organizations. We propose Interaction Ontology in order to cater the heterogeneity in workflows and provide process interoperability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.2
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• pp.422-430
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• 2008

Data integration is techniques to combine heterogeneous data from different sources, and to allow users to transparently access all data from multiple sources via a single view. The difficulty with data integration is data heterogeneity (i.e. schema heterogeneity, semantic heterogeneity). Richer semantics of data is a major factor in resolving conflicts among heterogeneous data sources. As UML class model represents only schema-based semantics of data, alternative methods such as ontology is useful for representing additional semantics. This paper proposes a method for integrating two data sources with UML class models by using an analysis of their ontologies. In our framework, ontology will be applied to describe semantics of data in each source. Then the ontologies are analysed and compared to determine their similarities and differences. The result of the comparison is used to devise an integrated ontology that will enable querying on the integrated information.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.375-378
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• 2016

The difficulty of "information sharing", "information reusing" issues happening in Wireless Sensor Network is due to the heterogeneity of the application environment, data processing, communication protocol etc. Based on the introduction of the Ontology theory, though analyzing the sensor characteristic a general type of sensor ontology contains the definition of concept, frame structure and OWL design was proposed from the standpoint of sensor observation. The paper expounded a system framework of the domain ontology through the expansion of knowledge base on the general sensor could achieve the information sharing and reuse by semantic communication between the general sensor ontology and user. The research of this method would bring new idea to the semantic sensor network construction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.5
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• pp.921-929
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• 2008

Recently, many researches have been conducted to solve data heterogeneity as a way for data integration. The elements of the system that we suggest are an XMDR wrapper and XMDR Repository. XMDR wrapper solves the heterogeneity of the existing system by creating the interface based on the standard information of XMDR, and performing the inter-conversion between global XMDR query and local query using mapping data on standard information and local schema. XMDR Repository are composed of XMDR which manages the mapping data on standard information and local schema, and of Proxy DB which saves the accomplished results. With XMDR wrapper and XMDR Repository, users can use the same interface, and they need not conduct repeated queries since XMDR wrapper not only solves the heterogeneity of the schema using the meta-semantic ontology of XMDR, but also considers the heterogeneity accompanying the meaning of the value through instance semantic ontology. Therefore, in this paper we suggest the grid wrapper for the solution of data heterogeneity and efficient data integration.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.97-99
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• 2011

As interoperability between systems in distributed environment has been important, it has been possible for various organizations to share resources and exchange relevant information. However, semantic heterogeneity between the systems and organizations causes the problem of making their interoperability impossible. Thereby, in this paper, we propose an ontology matching-based knowledge management system which can automatically discover semantic correspondences between ontologies. Moreover, even though there have been many existing ontology matchers, it is still difficult to directly apply them to the proposed system. To deal with the problems, we want to discover matching patterns (MP) which they discover from two given ontologies.