• ETRI Journal
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• v.44 no.5
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• pp.780-793
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• 2022

Large-scale ontology management is one of the main issues when using ontology data practically. Although many approaches have been proposed in relational database management systems (RDBMSs) or object-oriented DBMSs (OODBMSs) to develop large-scale ontology management systems, they have several limitations because ontology data structures are intrinsically different from traditional data structures in RDBMSs or OODBMSs. In addition, users have difficulty using ontology data because many terminologies (ontology nodes) in large-scale ontology data match with a given string keyword. Therefore, in this study, we propose a (graph database-based ontology management system (GOMS) to efficiently manage large-scale ontology data. GOMS uses a graph DBMS and provides new query templates to help users find key concepts or instances. Furthermore, to run queries with multiple joins and path conditions efficiently, we propose GOMS encoding as a filtering tool and develop hash-based join processing algorithms in the graph DBMS. Finally, we experimentally show that GOMS can process various types of queries efficiently.

• Journal of Information Technology Applications and Management
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• v.26 no.1
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• pp.65-75
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• 2019

As the use of semantic web based on XML increases in the field of data management, a lot of studies to extract useful information from the data stored in ontology have been tried based on association rule mining. Ontology data is advantageous in that data can be freely expressed because it has a flexible and scalable structure unlike a conventional database having a predefined structure. On the contrary, it is difficult to find frequent patterns in a uniformized analysis method. The goal of this study is to provide a basis for extracting useful knowledge from ontology by searching for frequently occurring subgraph patterns by applying transaction-based graph mining techniques to ontology schema graph data and instance graph data constituting ontology. In order to overcome the structural limitations of the existing ontology mining, the frequent pattern search methodology in this study uses the methodology used in graph mining to apply the frequent pattern in the graph data structure to the ontology by applying iterative node chunking method. Our suggested methodology will play an important role in knowledge extraction.

• Journal of Korea Multimedia Society
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• v.10 no.6
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• pp.754-759
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• 2007

Methodology of ontology building based on Web resources will not only reduce significantly the ontology construction period, but also enhance the quality of the ontology. Remarkable progress has been achieved in this regard, but they encounter similar difficulties, such as the Web data extraction and knowledge acquisition. This paper researches on the characteristics of ontology construction data, including dynamics, largeness, variation and openness and other features, and the fundamental issue of ontology construction - formalized representation method. Then, the key technologies used in and the difficulties with ontology construction are summarized. A software Model-OntoMaker (Ontology Maker) is designed. The model is innovative in two regards: (1) the improvement of generality: the meta learning machine will dynamically pick appropriate ontology learning methodologies for data of different domains, thus optimizing the results; (2) the merged processing of (semi-) structural and non-structural data. In addition, as known to all wetland researchers, information sharing is vital to wetland exploitation and protection, while wetland ontology construction is the basic task for information sharing. OntoMaker constructs the wetland ontologies, and the model in this work can also be referred to other environmental domains.

• Korean Journal of Computational Design and Engineering
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• v.13 no.3
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• pp.163-174
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• 2008

In this paper, for integrating of data on car parts we model information of parts that PDM system manages. Ontology of car parts applies existing ontology mapping research to integrate into car ontology. We propose a method for semantic integration of PLM object of MEMPHIS based on the integrated ontology. Through our method, we introduce C# ontology model to apply existing C# applications with ontology. We also classify ontology integration into three through examples and explain them. While semantically integrating PLM objects based on the integrated ontology, we explain the need for change of PLM object type and describe the process of change for PLM object type by examples.

• The Journal of Society for e-Business Studies
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• v.9 no.3
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• pp.79-94
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• 2004

W3C has developed the RDF standard for utilizing ontology in Web applications. This paper presents extracting, storing, and applying ontology on product data. The management and document information included in DWG files is focused as an example. By analyzing the relationship among the drawing data, the RDF schema is designed frist. Based on the schema ontology is extracted and stored in XML files. As an application of the stored ontology, we the schema ontology is extracted and stored in XML files. As an application of the stored ontology, we can reconfigure the sitemap of drawing data repositories. In this example, the users can select the view that he or she is interested in (e.g., designer, document, project). With such various views of an drawing data repository, the users can access the specific data more effectively.

• Journal of the Korea Safety Management & Science
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• v.6 no.1
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• pp.201-217
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• 2004

In this research, OIL (Ontology Inference Layer), one of the ontology language, is applied for classifying product data systematically, defining concepts, and establishing relationship between concepts. By transforming steel product data into XML documentation and managing them, knowledge management based on the logical structure of documents is possible.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.3
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• pp.39-48
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• 2013

Ontology has an important role in semantic web to construct and query semantic data. When system make changes to ontologies, questions arise about versioning of these changes. Applying this changes on a dynamic environment is even more important. To apply these changes, change specification method is needed. Early studies show RDF-based syntax for the operations between old and new ontologies. When several ontology versions can be used for some query, if possible, using possible newest version ontology with prospective use is best way to process the query. Prospective use of ontology means using a newer version of an ontology with a data source that conforms to a more recent ontology. In this paper, for prospective use of ontology version, structure of change specification of class and property through several ontology versions is proposed. From this, efficient adaptive ontology version selection for a query can be possible. Algorithm for structure of version transition representation is proposed and simulation results show possible newest version number for queries.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.261-276
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• 2020

Digital contents services are one of the topics that have been intensively studied in the media industry, where various semantic and ontology techniques are applied. However, query execution for ontology data is still inefficient, lack of sufficient extensible definitions for node relationships, and there is no specific semantic method fit for media data representation. In order to make the machine understand digital contents (DCs) data well, we analyze DCs data, including static data and dynamic data, and use ontology to specify and classify objects and the events of the particular objects. Then the formal representation method is proposed which not only redefines DCs data based on the technology of OWL/RDF, but is also combined with media segmentation methods. At the same time, to speed up the access mechanism of DCs data stored under the persistent database, an ontology-based DCs query solution is proposed, which uses the specified distance vector associated to a surveillance of semantic label (annotation) to detect and track a moving or static object.

• Journal of Digital Convergence
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• v.3 no.1
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• pp.183-194
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• 2005

This study is aimed at building up an Ontology to solve the discrepancy of terminologies between teachers and students by showing, through Ontology, the knowledge for diagnosis of underachieving students. Also this study makes it possible to infer the diagnosis based on information of these underachieving students. In addition, while a general Underachieving Students diagnosis system shows special diagnosis, this Ontology system helps users obtain correct concepts through this knowledge based system, and suggest building an Ontology to extend unclear conceptual knowledge to clearer ones.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.299-308
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• 2006

As recently XML is becoming the standard of exchanging web documents and public documentations, XML data are increasing in many areas. To retrieve the information about XML documents efficiently, the semantic web based on the ontology is appearing. The existing ontology has been constructed manually and it was time and cost consuming. Therefore in this paper, we propose the semi-automatic ontology generation technique using the data mining technique, the association rules. The proposed method solves what type and how many conceptual relationships and determines the ontology domain level for the automatic ontology generation, using the data mining algorithm. Appying the association rules to the XML documents, we intend to find out the conceptual relationships to construct the ontology, finding the frequent patterns of XML tags in the XML documents. Using the conceptual ontology domain level extracted from the data mining, we implemented the semantic web based on the ontology by XML Topic Maps (XTM) and the topic map engine, TM4J.