• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.69-87
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• 2008

The Web is evolving toward the Semantic Web. Ontologies are considered as a crucial component of the Semantic Web since it is the backbone of knowledge representation for this Web. However, most of these ontologies are still built manually. Manual building of an ontology is time-consuming activity which requires many resources. Consequently, the need for automatic ontology extraction tools has been increased for the last decade, and many tools have been developed for this purpose. Yet, there is no comprehensive framework for evaluating such tools. In this paper, we proposed a set of criteria for evaluating ontology extraction tools and carried out an experiment on four popular ontology extraction tools (i.e., OntoLT, Text-To-Onto, TERMINAE, and OntoBuilder) using our proposed evaluation framework. The proposed framework can be applied as a useful benchmark when developers want to develop ontology extraction tools.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.171-177
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• 2011

The decision making process is based on accurate and timely available information. To obtain precise information from the internet is becoming more difficult due to the continuous increase in vagueness and uncertainty from online information resources. This also poses a problem for blind people who desire the full use from online resources available to other users for decision making in their daily life. Ontology is considered as one of the emerging technology of knowledge representation and information sharing today. Fuzzy logic is a very popular technique of artificial intelligence which deals with imprecision and uncertainty. The classical ontology can deal ideally with crisp data but cannot give sufficient support to handle the imprecise data or information. In this paper, we incorporate fuzzy logic with heavyweight ontology to solve the imprecise information extraction problem from heterogeneous misty sources. Fuzzy ontology consists of fuzzy rules, fuzzy classes and their properties with axioms. We use Fuzzy OWL plug-in of Protege to model the fuzzy ontology. A prototype is developed which is based on OWL-2 (Web Ontology Language-2), PAL (Protege Axiom Language), and fuzzy logic in order to examine the effectiveness of the proposed system.

• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.17-26
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• 2009

In national defense, established information systems are mainly based on simple information processing, such as mass data query. They have thus lacked intelligent ability of information and knowledge representation ability. We therefore need the research about the construction of military ontology which is the main topic for knowledge construction. Military ontology can help us develop the intelligent national defense information system which can search and manage information efficiently. In this paper, we present the six-step bottom-up approach for military ontology extraction, then we apply this approach to one of military domain, called national defense educational training, and finally implement it using $Prot\acute{e}g\acute{e}$ which is one of the most useful ontology development tool.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.61-67
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• 2009

The proliferation of the Internet grows, according to electronic documents, along with increasing importance of technology in information retrieval. This research is possible to build a more efficient and accurate knowledge-base with unstructured text documents from the Web using to extract knowledge of the core meaning of LGG (Local Grammar Graph). We have built a ontology based on OWL(Web Ontology Language) using the areas of particular stocks up/down patterns created by the extraction and grammar patterns. It is possible for the user can search for meaning and quality of information about the user wants.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.317-322
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• 2021

The modern technological era has brought about the Semantic Web. Ontologies are essential to achieve the vision of the Semantic Web. Ontologies enable machines to understand data. The Arabic Language currently does not have a significant presence on the Web. To achieve a comparable level of Arabic access to other important languages, further work is needed to build Arabic ontologies. A goal is to design and create a robust Arabic ontology that represents the concepts from a large and significant subset of Arabic. We use a source of Hadiths (prophet saying and deeds) from Riyadh As-Saliheen. Preliminary results are very promising.

• Proceedings of the CALSEC Conference
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• 2005.03a
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• pp.55-61
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• 2005

We will introduce a new web-based knowledge management system in progress, in which XML-based web information extraction and our structuring knowledge technologies are combined using ontology-based natural language processing. Our aim is to provide efficient access to heterogeneous information on the web, enabling users to use a wide range of textual and non textual resources, such as newspapers and databases, effortlessly to accelerate knowledge acquisition from such knowledge sources. In order to achieve the efficient knowledge management, we propose at first an XML-based Web information extraction which contains a sophisticated control language to extract data from Web pages. With using standard XML Technologies in the system, our approach can make extracting information easy because of a) detaching rules from processing, b) restricting target for processing, c) Interactive operations for developing extracting rules. Then we propose a structuring knowledge system which includes, 1) automatic term recognition, 2) domain oriented automatic term clustering, 3) similarity-based document retrieval, 4) real-time document clustering, and 5) visualization. The system supports integrating different types of databases (textual and non textual) and retrieving different types of information simultaneously. Through further explanation to the specification and the implementation technique of the system, we will demonstrate how the system can accelerate knowledge acquisition on the Web even for novice users of the field.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2002.06a
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• pp.25-33
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• 2002

Recent developments in literature data mining for biology call for the design of a common framework that can be used to assess the performance of the reported systems in a fair and objective way. In this talk, we present an on-going effort to make it possible, in the form of challenges in the extraction of biological pathways and in the ontology construction. We are currently making this effort jointly with Lynette Hirschman (MITRE), Junichi Tsujii (University of Tokyo), Limsoon Wong (KRDL), and Cathy Wu (Georgetown University)

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.291-296
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• 2007

The ontology has been used widely in recent years with its aim to accumulate knowledge that machine can comprehend. We believe that machine can manage and analyze information on its own using the ontology. In this paper, we propose an algorithm that allows us to extract properties of ontology instances from structured information already existing in web documents. In particular, by stratification of the domain knowledge that is composed of property information, we were able to make the algorithm better and improve the quality of extraction results. In our experiments with 20 thousands targeted documents, we were able to extract property information with 83% confidence.

• 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.

• 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.