• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.179-186
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• 2021

We have been developed the smart tourism app and the Instagram and YouTube contents to provide personalized tourism information and travel product information to individual tourists. In this paper, we develop a rule-based smart tourism chatbot with the khaiii (Kakao Hangul Analyzer III) morphological analyzer and Neo4J graph database. In the proposed chatbot system, we use a morpheme analyzer, a proper noun dictionary including tourist destination names, and a general noun dictionary including containing frequently used words in tourist information search to understand the intention of the user's question. The tourism knowledge base built using the Neo4J graph database provides adequate answers to tourists' questions. In this paper, the nodes of Neo4J are Area based on tourist destination address, Contents with property of tourist information, and Service including service attribute data frequently used for search. A Neo4J query is created based on the result of analyzing the intention of a tourist's question with the property of nodes and relationships in Neo4J database. An answer to the question is made by searching in the tourism knowledge base. In this paper, we create the tourism knowledge base using more than 1300 Jeju tourism information used in the smart tourism app. We plan to develop a multilingual smart tour chatbot using the named entity recognition (NER), intention classification using conditional random field(CRF), and transfer learning using the pretrained language models.

• Asia pacific journal of information systems
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• v.7 no.2
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• pp.163-183
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• 1997

As object models and hybrid knowledge are increasingly used in current information systems development, Is-a structures need to be more formally defined, and subsequently subsumption related anomalies need to be detected with minimal declaration of meta knowledge. This paper extends a metagraph in the hybrid environments and demonstrates its utilities for detecting such anomalies that can occur from semantics and dynamics unique to the hybrid knowledge and data structure.

• The Mathematical Education
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• v.41 no.1
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• pp.101-108
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• 2002

The purpose of this study is to estimate teachers' knowledge of mathematics via the concept of function. For the purpose, a survey was done to measure their knowledge of mathematics. The result obtained from the survey was as follows With respect to the knowledge on concept of friction, they understood the function as ordered pairs and graph rather than as relation and expression. This study reached the following conclusions from the result : They have the more static cognition than the dynamic one on the concept of unction.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.9-21
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• 2020

With the proliferation of RDF knowledge graphs(KGs), there arose a need of a standardized schema representation of the graph model for effective data interchangeability and interoperability. The need resulted in the development of SHACL specification to describe and validate RDF graph's structure by W3C. Relational databases(RDBs) are one of major sources for acquiring structured knowledge. The standard for automatic generation of RDF KGs from RDBs is R2RML, which is also developed by W3C. Since R2RML is designed to generate only RDF data graphs from RDBs, additional manual tasks are required to create the schemas for the graphs. In this paper we propose an approach to automatically generate SHACL schemas for RDF KGs populated by R2RML mappings. The key of our approach is that the SHACL shemas are built only from R2RML documents. We describe an implementation of our appraoch. Then, we show the validity of our approach with R2RML test cases designed by W3C.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.281-289
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• 1999

The objective of this paper is to propose a brand new interface mechanism to provide more intelligent decision making support for EC problems. Its main virtue is based on a numerical process mechanism by using an Extended AND-OR Graph (EAOG)-based logic algebra. Using this mechanism, decision makers engaged in electronic commerce (EC) can effectively deal with complicated decision making problems. In the field of traditional expert systems research, AND-OR Graph approach has been suggested as a useful tool for representing the logic flowchart of the forward and/or backward chaining inference methods. However, the AND-OR Graph approach cannot be effectively used in the EC problems in which real-time problem-solving property should be highly required. In this sense, we propose the EAOG inference mechanism for EC problem-solving in which heurisric knowledge necessary for intelligent EC problem-solving can be represented in a form of matrix. Finally, we have proved the validity of our approach with several propositions and an illustrative EC example

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.9
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• pp.413-422
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• 2002

In a power plant, disturbance detection and diagnosis are massive and complex problems. Once a disturbance occurs, it can be either persistent, self cleared, cleared by the automatic controllers or propagated into another disturbance until it subsides in a new equilibrium or a stable state. In addition to the Physical complexity of the power plant structure itself, these dynamic behaviors of the disturbances further complicate the fault monitoring and diagnosis tasks. A data structure called a disturbance interrelation analysis graph(DIAG) is proposed in this paper, trying to capture, organize and better utilize the vast and interrelated knowledge required for power plant disturbance detection and diagnosis. The DIAG is a multi-layer directed AND/OR graph composed of 4 layers. Each layer includes vertices that represent components, disturbances, conditions and sensors respectively With the implementation of the DIAG, disturbances and their relationships can be conveniently represented and traced with modularized operations. All the cascaded disturbances following an initial triggering disturbance can be diagnosed in the context of that initial disturbance instead of diagnosing each of them as an individual disturbance. DIAG is applied to a typical cooling water system of a thermal power plant and its effectiveness is also demonstrated.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.04a
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• pp.242-250
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• 1999

The objective of this paper is to propose an Extended AND-OR Graph (EAOG)-driven inferential simulation mechanism with which decision makers engaged in electronic commerce (EC) can effectively deal with complicated decision making problem. In the field of traditional expect systems research, AND-OR Graph approach cannot be effectively used in the EC problems in which real-time problem-solving property should be highly required. In this sense, we propose the EAOG inference mechanism for EC problem-solving in which heurisric knowledge necessary for intelligent EC problem-solving can be represented in a form of matrix. The EAOG method possesses the following three characteristics. 1. Realtime inference: The EAOG inference mechanism is suitable for the real-time inference because its computational mechanism is based on matrix computation.2. Matrix operation: All the subjective knowledge is delineated in a matrix form, so that inference process can proceed based on the matrix operation which is computationally efficient.3. Bi-directional inference: Traditional inference method of expert systems is based on either forward chaining or based on either and computational efficiency. However, the proposed EAOG inference mechanism is generically bi-directional without loss of both speed and efficiency.We have proved the validity of our approach with several propositions and an illustrative EC example.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.450-456
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• 1990

In this paper, production system is implemented with the inferential neural network model using semantic network and directed graph. Production system can be implemented with the transform of knowledge representation in production system into semantic network and of semantic network into directed graph, because directed graphs can be expressed by neural matrices. A concept node should be defined by the state vector to calculated the concepts expressed by matrices. The expressional ability of neunal network depends on how the state vector is defined. In this study, state vector is overlapped and each overlapping part acts as a inheritant of concept.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.127-132
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• 2008

This study proposes a Natural Language Query Framework (NLQF) for the semantic web. It supports an intelligent inference at a semantic level. Most of previous researches focused on the knowledge representation on the semantic web. However, to revitalize the intelligent e-business on the semantic web, there is a need for semantic level inference to the web information. To satisfy the need, we will review the knowledge/resource representation on the semantic web such as RDF, Ontology and Conceptual Graph (CG), and then discuss about the natural language (NL) inference. The result of this research could support a natural interface for the semantic web. Furthermore, we expect that the NLQF can be used in the semantic web-based business communications.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.759-760
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• 2020

최근 사용자, 상품, 그리고 상품의 메타 정보 사이의 관계를 표현한 지식 그래프 (knowledge graph) 가 추천 시스템 분야에서 많은 관심을 받고 있으며 활발히 이용되고 있다. 하지만 기존의 지식 그래프는 각 노드 (사용자, 상품, 메타 정보 등) 사이의 단순한 사실 관계만을 표현하고 있으며, 이는 사용자의 선호도를 정확히 파악하는 데 한계가 있다. 본 논문에서는 지식 그래프의 정보 부족 문제를 보완하기 위해 각 상품에 남겨진 텍스트 리뷰를 감정 분석 (sentiment analysis) 하고, 이를 각 노드 간의 선호도 정보로 활용하여 지식 그래프를 구축하는 방법을 제안한다.