• Journal of applied mathematics & informatics
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• v.10 no.1_2
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• pp.333-351
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• 2002

This paper introduces a search method based on conceptual graph. A hyperlink information is essential to construct conceptual graph in web. The information is very useful as it provides summary and further linkage to construct conceptual graph that has been provided by human. It also has a property which shows review, relation, hierarchy, generality, and visibility. Using this property, we extracted the keywords of web documents and made up of the conceptual graph among the keywords sampled from web pages. This paper extracts the keywords of web pages using anchor text one out of hyperlink information and makes hyperlink of web pages abstract as the link relation between keywords of each web page. 1 suggest this useful search method providing querying word extension or domain knowledge by conceptual graph of keywords. Domain knowledge was conceptualized knowledged as the conceptual graph. Then it is not listing web documents which is the defect of previous search system. And it gives the index of concept associating with querying word.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.285-292
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• 2014

Intelligent systems are systems that mainly use knowledge rather than data or information. Therefore, knowledge representation is an important factor for intelligent system construction. Conceptual graph is a logical knowledge representation language which has graphical form and it can represent knowledge efficiently. It is, however, cumbersome to use conceptual graphs directly for programming. Various tools were developed to overcome this difficulties. In this paper, we survey on functions and characteristics of conceptual graph tools that can be utilized for constructing intelligent systems by using conceptual graphs. The result of this survey will be very helpful to use conceptual graphs for development of intelligent systems.

• Journal of information and communication convergence engineering
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• v.7 no.4
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• pp.419-430
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• 2009

Reading comprehension (RC) systems are to understand a given text and return answers in response to questions about the text. Many previous studies extract sentences that are the most similar to questions as answers. However, texts for RC tests are generally short and facts about an event or entity are often expressed in multiple sentences. The answers for some questions might be indirectly presented in the sentences having few overlapping words with the questions. This paper proposes a conceptual graph matching method towards RC tests to extract answer strings. The method first represents the text and questions as conceptual graphs, and then extracts subgraphs for every candidate answer concept from the text graph. All candidate answer concepts will be scored and ranked according to the matching similarity between their sub-graphs and question graph. The top one will be returned as answer seed to form a concise answer string. Since the sub-graphs for candidate answer concepts are not restricted to only covering a single sentence, our approach improved the performance of answer extraction on the Remedia test data.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.123-129
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• 2007

On the Semantic Web, the content of the documents must be explicitly represented through metadata in order to enable contents-based inference. In this study, we propose a mechanism to convert the Conceptual Graph (CG) into Resource Description Framework (RDF). Quite a large number or representation languages for representing knowledge on the Web have been established over the last decade. Most of these researches are focused on design of independent knowledge description. On the Semantic Web, however, a knowledge conversion mechanism will be needed to exchange the knowledge used in independent devices. In this study, the CG could give an entire conceptual view of knowledge and RDF can represent that knowledge on the Semantic Web. Then the CG-based object oriented PROLOG could support the natural inference based on that knowledge. Therefore, our proposed knowledge conversion mechanism will be used in the designing of Semantic Web-based knowledge representation and inference systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.314-317
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• 2006

This study proposes an ontology design framework to support the cooperation among devices by using PROLOG, Conceptual Graph (CG), and Resource Description Framework (RDF). Quite a large number of representation languages for representing ontology on the Web have been established over the last decade. Most of these researches are focused on design of independent resources description. In Semantic Web, however, cooperation ontology will be needed. In this study, the CG could make an entire conceptual view of knowledge and RDF can represent that knowledge. Then the PROLOG could support the natural inference based on that knowledge. Therefore, our proposed ontology will be used in the designing of Semantic Web-based cooperation systems.

• The KIPS Transactions:PartC
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• v.13C no.1 s.104
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• pp.45-54
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• 2006

Nowadays, a lot of techniques have been applied for the detection of malicious behavior. However, the current techniques taken into practice are facing with the challenge of much variations of the original malicious behavior, and it is impossible to respond the new forms of behavior appropriately and timely. There are also some limitations can not be solved, such as the error affirmation (positive false) and mistaken obliquity (negative false). With the questions above, we suggest a new method here to improve the current situation. To detect the malicious code, we put forward dealing with the basic source code units through the conceptual graph. Basically, we use conceptual graph to define malicious behavior, and then we are able to compare the similarity relations of the malicious behavior by testing the formalized values which generated by the predefined graphs in the code. In this paper, we show how to make a conceptual graph and propose an efficient method for similarity measure to discern the malicious behavior. As a result of our experiment, we can get more efficient detection rate.

• Journal of Korea Entertainment Industry Association
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• v.14 no.4
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• pp.349-354
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• 2020

This paper, firstly, propose a new Clustering scheme Based on Conceptual graphs (CBC) that can describe objects freely and can perform clustering efficiently. The conceptual clustering is one of machine learning technique. The similarity among the objects in conceptual clustering are decided on the bases of concept membership, unlike the general clustering scheme which decide the similarity without considering the context or environment of the objects. A new conceptual clustering scheme, CBC, which can perform efficient conceptual clustering by describing various objects freely with conceptual graphs is introduced in this paper.

• Journal of Physiology & Pathology in Korean Medicine
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• v.26 no.6
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• pp.818-822
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• 2012

We in this paper propose a prognosis graph, analyzing prognoses of each pattern described in the Korean medicine literatures. This graph is represented as the integrated graphs about knowledge of patterns and their transitions in the prognoses, where a node becomes a pattern name and a edge becomes a transition between patterns, along with a condition with respect to cause or mechanism of the pattern. The knowledge of prognoses which a pattern is transit into another pattern can be identified at a glance by using this model. We also construct a upper-level prognosis graph, excluding five viscera and six entrails from the model. This upper-level prognosis graph contains the conceptual knowledge than clinical one so that it may be helpful to students and researchers in the Korean medicine fields.

• International Journal of Advanced Culture Technology
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• v.6 no.2
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• pp.117-122
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• 2018

As evidenced through rapidly growing digital repositories and web resources, automatic metadata generation is becoming ever more critical, especially considering the costly and complex operation of manual metadata creation. Also, automatic metadata generation is apt to consistent metadata application. In this sense, metadata quality and interoperability can be enhanced by utilizing a mechanism for automatic metadata generation. In this article, a mechanism of automatic metadata extraction called ExMETA is introduced in order to alleviate issues dealing with inconsistent metadata application and semantic interoperability across ever-growing digital collections. Conceptual graph, one of formal languages that represent the meanings of natural language sentences, is utilized for ExMETA as a mediation mechanism that enhances the metadata quality by disambiguating semantic ambiguities caused by isolation of a metadata element and its corresponding definition from the relevant context. Hence, automatic metadata generation by using ExMETA can be a good way of enhancing metadata quality and semantic interoperability.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.1-6
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• 2018

The following research questions are examined in this paper. What hinders quality metadata generation and metadata interoperability? What kind of semantic representation technique can be utilized in order to enhance metadata quality and semantic interoperability? This paper suggests a way of handling semantic ambiguity for metadata generation. The conceptual graph is utilized to disambiguate semantic ambiguities caused by isolation of a metadata element and its corresponding definition from the relevant context. The mechanism introduced in this paper has the potential to alleviate issues dealing with inconsistent metadata application and interoperability across digital collections.