• 제목/요약/키워드: domain specific knowledge

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Fuzzy Causal Knowledge-Based Expert System

  • Lee, Kun-Chang;Kim, Hyun-Soo;Song, Yong-Uk
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.10a
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    • pp.461-467
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    • 1998
  • Although many methods of knowledge acquisition has been developed in the expert systems field, such a need for causal knowledge acquisition has not been stressed relatively. In this respect, this paper is aimed at suggesting a causal knowledge acquisition process, and then investigate the causal knowledge-based inference process. A vehicle for causal knowledge acquisition is FCM (Fuzzy Cognitive Map), a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain. Although FCM has a plenty of generic properties for causal knowledge acquisition, it needs some theoretical improvement for acquiring a more refined causal knowledge. In this sense, we refine fuzzy implications of FCM by proposing fuzzy implications of FCM by proposing fuzzy causal relationship and fuzzy partially causal relationship. To test the validity of our proposed approcach, we prototyped a causal knowledge-driven inference engine named CAKES and then experime ted with some illustrative examples.

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MULTI-LAYERED PRODUCT KNOWLEDGE MODEL (다중 레이어 기반 제품 지식 모델)

  • Lee J.H.;Suh H.W.
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2005.06a
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    • pp.65-70
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    • 2005
  • This paper introduces an approach to multi-layered product knowledge model for collaborative engineering environment. The participants in collaborative engineering want to share and reason product knowledge through internet without any heterogeneity and ambiguity. However the previous knowledge models are limited in providing those aspects. In this paper, the collaborative engineering domain is analyzed and then the product knowledge is organized into four levels such as product context model, product specific model, product design model and product manufacturing model. The four levels are represented by first-order logic in layered fashion. The concepts and the instances of a formal ontology are used for recursive representation of the four levels. The instances of the concepts of an upper level like product context model are considered as the concepts of an adjacent lower level like product specific model, and this mechanism is applied to the other levels. These logic representations are integrated with the schema and the instances of a relational database. OWL representation of the four levels is defined through the integration of the logic representation and OWL primitives. The four product knowledge models have their major representation according to the characteristics of each model. This approach enables engineer to share product knowledge through internet without any ambiguity and utilize it as basis for additional reasoning.

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Semi Automatic Ontology Generation about XML Documents

  • Gu Mi Sug;Hwang Jeong Hee;Ryu Keun Ho;Jung Doo Yeong;Lee Keum Woo
    • Proceedings of the KSRS Conference
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    • 2004.10a
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    • pp.730-733
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    • 2004
  • Recently XML (eXtensible Markup Language) is becoming the standard for exchanging the documents on the web. And as the amount of information is increasing because of the development of the technique in the Internet, semantic web is becoming to appear for more exact result of information retrieval than the existing one on the web. Ontology which is the basis of the semantic web provides the basic knowledge system to express a particular knowledge. So it can show the exact result of the information retrieval. Ontology defines the particular concepts and the relationships between the concepts about specific domain and it has the hierarchy similar to the taxonomy. In this paper, we propose the generation of semi-automatic ontology based on XML documents that are interesting to many researchers as the means of knowledge expression. To construct the ontology in a particular domain, we suggest the algorithm to determine the domain. So we determined that the domain of ontology is to extract the information of movie on the web. And we used the generalized association rules, one of data mining methods, to generate the ontology, using the tag and contents of XML documents. And XTM (XML Topic Maps), ISO Standard, is used to construct the ontology as an ontology language. The advantage of this method is that because we construct the ontology based on the terms frequently used documents related in the domain, it is useful to query and retrieve the related domain.

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Linear Programming Model Discovery from Databases (데이터베이스로부터의 선형계획모형 추출방법에 대한 연구)

  • 권오병;김윤호
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2000.04a
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    • pp.290-293
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    • 2000
  • Knowledge discovery refers to the overall process of discovering useful knowledge from data. The linear programming model is a special form of useful knowledge that is embedded in a database. Since formulating models from scratch requires knowledge-intensive efforts, knowledge-based formulation support systems have been proposed in the DSS area. However, they rely on the strict assumption that sufficient domain knowledge should already be captured as a specific knowledge representation form. Hence, the purpose of this paper is to propose a methodology that finds useful knowledge on building linear programming models from a database. The methodology consists of two parts. The first part is to find s first-cut model based on a data dictionary. To do so, we applied the GPS algorithm. The second part is to discover a second-cut model by applying neural network technique. An illustrative example is described to show the feasibility of the proposed methodology.

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The Relation of High School Students' Epistemological belief, Acceptance of Evolutionary Theory and Evolutionary Knowledge (고등학생의 인식론적 신념과 진화수용 및 진화지식과의 관련성)

  • Kim, Sun Young
    • Journal of The Korean Association For Science Education
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    • v.35 no.2
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    • pp.259-265
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    • 2015
  • This study examined high school students' acceptance of evolutionary theory, evolutionary knowledge, and epistemological belief. The Christian and non-Christian students' acceptance of evolutionary theory and evolution content knowledge were compared in relation to their 'scientific epistemological views' (domain-specific) and 'evolution in relation to nature of science' (context-specific). The Christian students' evolutionary knowledge was most predicted by the theory-laden exploration of science, while the non-Christian students' scores on evolutionary knowledge were most predicted by the scientific epistemological views. In addition, the Christian students' scores on scientific epistemological views and evolution in relation to evolution were not significantly related to each other, while the non-Christian students' scores on both variables were significantly related. Furthermore, 'evolution in relation to nature of science' is the strongest predictor of both Christian and non-Christian students' acceptance of evolution.

Architecture of knowledge-Base and Management System for Grining Operations (연삭가공용 데이타베이스 설게와 활용(기존지식베이스에 관하여))

  • Kim, G.H.;Inasaki, I.;Lee, J.K.
    • Journal of the Korean Society for Precision Engineering
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    • v.11 no.1
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    • pp.211-218
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    • 1994
  • Grinding is considered as a very effective machining technology to attain high production rates and a good surface quality of hard and brittle components. However, the grinding operations till needs the skill and the experience of an operator because of a lack of scientific knowledge and engineering principles. This is the reason why grinding operations are not completley intergrated in CIMS(Computer Intergrated Manufacturing System. Recent develop- ment focus on expert system which deals with domain specific knowledge in order to solve this problem. Firstly, in this study, a basic strategy to develop the grinding knowledge-base for grinding is discussed. Next, the architecture of knowledge-base and management of the grinding knowledge-base(GKB) is described.

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Linear Programming Model Discovery from Databases Using GPS and Artificial Neural Networks (GPS와 인공신경망을 활용한 데이터베이스로부터의 선형계획모형 발견법)

  • 권오병;양진설
    • Journal of the Korean Operations Research and Management Science Society
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    • v.25 no.3
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    • pp.91-107
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    • 2000
  • The linear programming model is a special form of useful knowledge that is embedded in a database. Since formulating models from scratch requires knowledge-intensive efforts, knowledge-based formulation support systems have been proposed in the Decision Support Systems area. However, they rely on the assumption that sufficient domain knowledge should already be captured as a specific knowledge representation form. Hence, the purpose of this paper is to propose a methodology that finds useful knowledge on building linear programming models from a database. The methodology consists of two parts. The first part is to find s first-cut model based on a data dictionary. To do so, we applied the General Problem Solver(GPS) algorithm. The second part is to discover a second-cut model by applying neural network technique. An illustrative example is described to show the feasibility of the proposed methodology.

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The Implications of Content Schemata for Standardized Reading Tests

  • Lee, Jong-Hee
    • English Language & Literature Teaching
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    • v.9 no.spc
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    • pp.79-98
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    • 2003
  • This article reports that a testee's content schemata have detrimental effects on the validity of standardized reading comprehension tests. in case that the reading passages constitute his prior background knowledge framed by regular academic pursuits focused on a major field of study. The empirical research findings on such a reliability issue have shown that the Korean subjects' familiarity with the content domain of the reading materials may substantially enhance their test performance, operated as unfair advantages over the other participants at the same level of English proficiency who do not possess technical knowledge closely related to the contents of the test passages. Based on these results, it may be argued that the reading text should be composed of maximally neutral contents to every testee to avoid the possible overlapping between a test-taker's content schemata and the content domain of the materials. Therefore, this article claims that the tenets of schema theory with its long-standing foundation on culturally predetermined content and formal schemata need to be expanded to cover professional knowledge structures, like those acquired by higher education, as the hard-wired framework of an individual's original culture-specific background knowledge.

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Design and Implementation of a Framework for Context-Aware Preference Queries

  • Roocks, Patrick;Endres, Markus;Huhn, Alfons;KieBling, Werner;Mandl, Stefan
    • Journal of Computing Science and Engineering
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    • v.6 no.4
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    • pp.243-256
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    • 2012
  • In this paper we present a framework for a novel kind of context-aware preference query composition whereby queries for the Preference SQL system are created. We choose a commercial e-business platform for outdoor activities as a use case and develop a context model for this domain within our framework. The suggested model considers explicit user input, domain-specific knowledge, contextual knowledge and location-based sensor data in a comprehensive approach. Aside from the theoretical background of preferences, the optimization of preference queries and our novel generator based model we give special attention to the aspects of the implementation and the practical experiences. We provide a sketch of the implementation and summarize our user studies which have been done in a joint project with an industrial partner.

Safety and Efficiency Learning for Multi-Robot Manufacturing Logistics Tasks (다중 로봇 제조 물류 작업을 위한 안전성과 효율성 학습)

  • Minkyo Kang;Incheol Kim
    • The Journal of Korea Robotics Society
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    • v.18 no.2
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    • pp.225-232
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    • 2023
  • With the recent increase of multiple robots cooperating in smart manufacturing logistics environments, it has become very important how to predict the safety and efficiency of the individual tasks and dynamically assign them to the best one of available robots. In this paper, we propose a novel task policy learner based on deep relational reinforcement learning for predicting the safety and efficiency of tasks in a multi-robot manufacturing logistics environment. To reduce learning complexity, the proposed system divides the entire safety/efficiency prediction process into two distinct steps: the policy parameter estimation and the rule-based policy inference. It also makes full use of domain-specific knowledge for policy rule learning. Through experiments conducted with virtual dynamic manufacturing logistics environments using NVIDIA's Isaac simulator, we show the effectiveness and superiority of the proposed system.