• The Transactions of the Korea Information Processing Society
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• v.6 no.2
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• pp.357-366
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• 1999

Inheritance reasoning has been widely used in the area of common sense reasoning in artificial intelligence. Although many inheritance reasoners have been proposed in artificial intelligence literature, most previous reasoning systems are lack of clear semantics, thus sometimes provide anomalous conclusions. In this paper, we describe a set-oriented inheritance reasoner and propose a method of resolving conflicts with clear semantics of defeasible rules. The semantics of default rule is provided by statistical analysis of $\chi$ method, and likelihood of rule is computed based on the evidence in the past. Two basic rules, specificity and generality, are defined to resolve conflicts effectively in the process of reasoning. We show that the mutual tradeoff between specificity and generality 추 prevent many anomalous results from occurring in traditional inheritance reasoners. An algorithm is provided. and some typical examples are given to show how the specificity/generality rules resolve conflicts effectively in inheritance reasoning.

• The Journal of the Korea Contents Association
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• v.14 no.10
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• pp.438-453
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• 2014

This paper proposes a system to translate an RDB into an OWL ontology which enables the users to navigate the ontology in GUI. In order to accomplish the goals mentioned previously, the system overcame two difficulties. First, our system defines a new mapping algorithm to map between DB elements and ontology ones. Comparing with existing solutions, our algorithm is able to generate ontologies from more DB structures. Second, our system provides the same data generated by a reasoner to the users. Note that this operation does not load ABox ontology on a reasoner. In addition, Tableau-based reasoners have the tractability problem on a large ABox (e.g., large ABoxes translated from DBs practically cannot be served). To solve this, our system internally runs SQL queries to retrieve the same data as the one from a reasoner every time ABox elements are queried.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.655-666
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• 2007

Reasoners using typical Tableaux algorithm such as RacerPro, Pellet have a problem in Tableaux algorithm large ABox reasoning. Researches to solve these Problems are dealt with Instance Store of University of Manchester which uses Tableaux algorithm based reasoner and DBMS and KAON2 of University of Karlsruhe using Disjunctive Datalog approach. An evaluation experiment for present reasoners is the experiment of TBox reasoning in most of Tableaux algorithm based one. The most of benchmarking tests in reasoning systems haven't done with ABox reasoning based Tableaux Algorithm but done with TBox reasoning based Tableaux Algorithm. Especially, rarely reported benchmarking tests in reasoners have been issued nowadays. Therefore, this thesis evaluates systems with theory of each reasoners for large ABox reasoning that becomes issues recently with typical reasoners. The large AoBx reasoning engine will be analyzed using Instance Store and KAON2 of Manchester University for large ABox processing. At the analysing method, LUBM(Lehigh University BenchMark), benchmarking test method, and it's test system will be introduced. In conclusion, I recommend appropriate reasoner in various environment with experiment result and characteristic of algorithm used for each reasoner.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.3
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• pp.139-147
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• 2009

This paper proposed the context awareness reasoning system to provide the personalized services dynamically in a ubiquitous mobile environments. The proposed system is designed to provide the personalized services to mobile users and consists of the context aggregator and the knowledge manager. The context aggregator can collect information from networks through Open API Gateway as well as sensors in a various ubiquitous environment. And it can also extract the place types through the geocoding and the social address domain ontology. The knowledge manager is the core component to provide the personalized services, and consists of activity reasoner, user pattern learner and service recommender to provide the services predict by extracting the optimized service from user situations. Activity reasoner uses the ontology reasoning and user pattern learner learns with previous service usage history and contexts. And to design service recommender easy to flexibly apply in dynamic environments, service recommender recommends service in the only use of current accessible contexts. Finally, we evaluate the learner and recommender of proposed system by simulation.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.87-88
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• 2008

As an information service system based on semantic web technology, $OntoFrame^{(R)}$ takes aim at a framework for providing analysis and fusion services of academic information. It currently consists of three parts: ontologies representing knowledge schema derived from academic information, $OntoURI^{(R)}$ which makes academic information into knowledge, and $OntoReasoner^{(R)}$ which performs inference and search on the knowledge. Unlike existing search engines which provides simple search services, our system provides, based on semantic web technology, several semantic and analytic services such as year-based topic trends in academic information, related topics, topic-based researchers and institutes, researcher network, statistics and regional distribution of academic information.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.102-103
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• 2008

온톨로지가 대용량화됨에 따라, 구축 과정에 많은 인력이 투입되고, 그 과정 역시 복잡해지고 있다. 따라서 온톨로지 구축과정에서 발생하는 여러 가지 논리적 오류를 찾아내어 수정하는 작업은 중요하다. 또한 온톨로지 기반의 검색이나 온톨로지들을 통합할 때 온톨로지를 구성하는 개념간의 관계를 추론하는 것 역시 매우 중요하다. 본 연구의 목표는 온톨로지 구축 시 논리적 오류를 갖는 개념들을 찾아주고, 개념들 간에 관계를 추론하는 엔진을 구축하는 것이다. 본 논문에서 제안하는 Minerva는 OWL로 작성한 온톨로지 중 논리적 오류를 갖는 개념들을 찾아내어, 온톨로지 개발자들이 효과적으로 온톨로지를 구축하는 것과, 개념간의 관계를 추론해 줌으로써 온톨로지 기반의 서비스 어플리케이션 구축에 도움을 준다.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.835-840
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• 2007

현재 이슈가 되고 있는 유비쿼터스 컴퓨팅 환경에서 서비스를 제공함에 있어 사용자의 만족도를 높여주기 위해 서비스의 지능화가 필요하다. 이러한 지능적인 서비스를 제공하기 위해 서비스에 필요한 지식을 논리적으로 표현하고, 체계적으로 추론할 수 있는 방법이 요구된다. 이를 위해 표현 범위가 넓고 유연한 일차 술어 논리(FOL)는 여러 분야에서 사용되었으며, 추론 시스템에 이용되고 있다. 그러나 풍부한 표현 범위는 유비쿼터스 컴퓨팅 환경에서의 오브젝트 관리에 있어 많은 계산비용이 소요된다. 서비스의 빠른 제공을 목표로 하고 있는 유비쿼터스 환경에서 이러한 계산비용은 서비스 제공 시간을 늦추는 요인이 된다. 이러한 문제를 극복하고 지식의 의미를 부여하는 방법으로 Description Logic과 온톨로지가 연구되고 있다. 특히 OWL(Web Ontology Language)은 풍부한 표현력을 제공하고 있으며, W3C에 의해 온톨로지 기술의 표준으로 제안되었다. 그러나 풍부한 표현 범위는 실제 컴퓨팅 환경에서 모두 사용되지 않고, 기술 및 추론의 복잡함으로 overhead가 발생한다. 본 논문에서는 이를 극복하고자 실제 유비쿼터스 환경에서 요구되는 표현 범위를 만족하는 SUNHI의 표현력을 갖는 EOL을 제안한다.

• Journal of Information Technology Services
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• v.7 no.3
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• pp.239-252
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• 2008

Since EA includes huge information of a company, it takes long time and high cost for company's employees to search for what they need. We try to make the foundation to solve this problem by using ontology technology based on semantic web. In this paper, we try to verify efficiency of EA ontology by developing ontology for Business Enterprise Architecture(BEA). The purpose of this paper is to develop BEA ontology to provide new information by reasoner and to discover new relations between matadata by using extracted information and data. The EA ontology we developed will provide the new way of access and use for companies. The experience of ontology development will help EA ontology development in various domains. In the future, the development of other EAs which has more information resources will help to solve problems for interoperability between different EAs.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.828-831
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• 2015

시맨틱 웹에서 실세계의 복잡한 사건들은 시간의 흐름에 따라 새로운 결과 또는 사실들이 생겨나기 때문에 시간이 포함된 지식에 대한 추론능력이 필수적이다. 본 논문에서는 대표적 병렬 분산 컴퓨팅 환경인 맵리듀스 프레임워크를 이용해, 새로운 시간 관계를 추론할 수 있는 효율적인 대용량 시간 추론 알고리즘을 제안한다. 또한, 맵리듀스 프레임워크로 구현한 대용량 시간 추론기의 성능을 분석하기 위해 샘플 시간 지식베이스를 이용한 실험들을 수행하고, 그 결과를 소개한다.