• KIPS Transactions on Software and Data Engineering
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• v.5 no.5
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• pp.231-242
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• 2016

In order to meet rapid changes of Web information, it is necessary to extend the current Web technologies to represent both the valid time and location of each fact and knowledge, and reason their relationships. Until recently, many researches on qualitative temporal reasoning have been conducted in laboratory-scale, dealing with small knowledge bases. However, in this paper, we propose the design and implementation of a parallel qualitative temporal reasoner, MRQUTER, which can make reasoning over Web-scale large knowledge bases. This parallel temporal reasoner was built on a Hadoop cluster system using the MapReduce parallel programming framework. It decomposes the entire qualitative temporal reasoning process into several MapReduce jobs such as the encoding and decoding job, the inverse and equal reasoning job, the transitive reasoning job, the refining job, and applies some optimization techniques into each component reasoning job implemented with a pair of Map and Reduce functions. Through experiments using large benchmarking temporal knowledge bases, MRQUTER shows high reasoning performance and scalability.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.99-106
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• 2014

Generation and analysis methods have been proposed in recent years, such as using a natural language and formal language processing, artificial intelligence algorithms based knowledge model is effective meaning. its semantic based knowledge model has been used effective decision making tree and problem solving about specific context. and it was based on static generation and regression analysis, trend analysis with behavioral model, simulation support for macroeconomic forecasting mode on especially in a variety of complex systems and social network analysis. In this study, in this sense, integrating knowledge-based models, This paper propose a text mining derived from the inter-Topic model Integrated formal methods and Algorithms. First, a method for converting automatically knowledge map is derived from text mining keyword map and integrate it into the semantic knowledge model for this purpose. This paper propose an algorithm to derive a method of projecting a significant topic map from the map and the keyword semantically equivalent model. Integrated semantic-based knowledge model is available.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.1
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• pp.1-6
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• 2013

In this paper, we review an outline as premise for environmental analysis of green IT which has basic stage through the world in order to make knowledge map of green IT. We can analyze genuine environment for green IT based on this premise, and we expect that we can also use to analyse power of competition based on intellectual property after performing these environmental analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.1
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• pp.7-12
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• 2013

In this paper, we provide a basic data to constitute knowledge map through analysis of competition situation such as analysis of patent activity for each nationality, analysis of patent activity for each applicant for a patent, analysis of patent activity for each technical area and analysis of competition status for power of security for market which consider qualitative level. In order to analysis LED data, we choose patent data of LED.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.609-610
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• 2006

This paper introduces a methodology for construction of ontology-based product knowledge Map. For CPC(Collaborative Product Commerce) environment, engineering application of ontology has been studied . However, there are no generic and comprehensive methodologies for ontology construction yet because of such problems: dependency on experience of ontologist and domain experts and insufficiency of detail stages or rules. To solve those problems, we propose a methodology to construct ontology from engineering documents in semi-automatic. We use middle-out strategy and term's axioms, sub-definitions, to build ontology map. 5-turple ontology structure, semantic network and First order logic (FOL) are used for ontology definition in this study.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.502-509
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• 2013

Even if the need for English education in Korea is on the rise, too various perspectives on English education and the government's lack of consistency in the English education policy are said to be a negative factor in improving the English communication skills. The purpose of this study, in line with the knowledge and information society, is to develop ontology modeling for knowledge map of English education methodology and to make a contribution to English education policy of the country.

• Journal of Smart Tourism
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• v.2 no.4
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• pp.11-23
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• 2022

The use of robots in tourism and hospitality contexts have drawn increasing scholarly and practical regard. Although the number of recent robotics related studies continue to grow, a general knowledge map, which is important to point out promising directions for future studies, remains to be made. To understand the application of robotics in tourism and hospitality, this study conducts descriptive and bibliometric analysis to present a holistic knowledge map of this specific field where research trend, key contributors, highly cited references, and popular themes were identified. Collaboration networks among institutes and regions were additionally illustrated. Collaboration across fields, industries, and perspectives were encouraged following the findings and both theoretical and practical implications are accordingly provided.

• International Journal of CAD/CAM
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• v.5 no.1
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• pp.99-109
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• 2005

This paper introduces an approach to an ontology-based multi-level knowledge framework for a knowledge management system for discrete-product development. Participants in a product life cycle want to share comprehensive product knowledge without any ambiguity and heterogeneity. However, previous knowledge management approaches are limited in providing those aspects: therefore, we suggest an ontology-based multi-level knowledge framework (OBMKF). The bottom level, the axiom, specifies the semantics of concepts and relations of knowledge so ambiguity can be alleviated. The middle level is a product development knowledge map; it defines the concepts and the relations of the product domain knowledge and guides the engineer to process their engineering decisions. The middle level is then classified further into more detailed levels, such as generic product level, specific product level, product version level, and manufactured item level, according to the various viewpoints. The top level is specialized knowledge for a specific domain that gives the solution of a specific task or problem. It is classified into three knowledge types: expert knowledge, engineering function knowledge, and data-analysis-based knowledge. This proposed framework is based on ontology to accommodate a comprehensive range of knowledge and is represented with first-order logic to maintain a uniform representation.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.1
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• pp.17-40
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• 1994

The objectives of this paper are to apply fuzzy cognitive map (FCM)- related techniques to (1) extract causal knowledge from a specific problem-domain and (2) perform a series of causal analysis in complicated decision making area. We propose a set operation-based augmentation (SOBA) algorithm to combine multiple FCMs developed by multiple experts. Based on the SOBA knowledge acquisition algorithm, we can obtain a causal knowledge base fairly representing multiple experts' knowledge about a problem domain. The causal knowledge base built by SOBA algorithm can be described as a matrix form, guaranteeing mathematically compact operation compared with a production (if-then) knowledge base. We applied out method to stock market analysis problem whichis a typical of highly unstructured problems in OR/MS fields.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.397-406
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• 2014

In order to answer the questions successfully on behalf of the human in DeepQA environments such as Jeopardy! of the American quiz show, the computer is required to have the capability of fast temporal and spatial reasoning on a large-scale commonsense knowledge base. In this paper, we present a scalable spatial reasoning algorithm for deriving efficiently new directional and topological relations using the MapReduce framework, one of well-known parallel distributed computing environments. The proposed reasoning algorithm assumes as input a large-scale spatial knowledge base including CSD-9 directional relations and RCC-8 topological relations. To infer new directional and topological relations from the given spatial knowledge base, it performs the cross-consistency checks as well as the path-consistency checks on the knowledge base. To maximize the parallelism of reasoning computations according to the principle of the MapReduce framework, we design the algorithm to partition effectively the large knowledge base into smaller ones and distribute them over multiple computing nodes at the map phase. And then, at the reduce phase, the algorithm infers the new knowledge from distributed spatial knowledge bases. Through experiments performed on the sample knowledge base with the MapReduce-based implementation of our algorithm, we proved the high performance of our large-scale spatial reasoner.