• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.875-880
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• 2005

OWL(Web Ontology Language) is the Ontology Standard Language and the a lot of Ontologies are being constructed in OWL. But the research on the extension of OWL is also progressing because of the limit of representation power of in OWL language. The W3C suggests the SWRL(Semantic Web Rule Language) based on the combination of OWL and RuleML(Rule Markup Language), which is improved in the representation of rule. Thus, both OWL and SWRL are used for developing ontologies. However, research on inference of ontologies written in both languages is just begun. These day, for the inference of ontologies written in both languages, ontologies and divided in to two parts. The part written in OWL and written in SWRL. For the inference of the part written in OWL, Racer, a DL based inference engine, is used and for the other part Jess, a rule-based engine, is used. In this paper, we will propose three methods for integrated inference of the OWL part and the SWRL part of ontologies using Jess and some tools for ontology inference : OWLJessKB and SWRL Factory

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.213-216
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• 2005

W3C에서는 온톨로지의 표준 언어로 OWL(Web Ontology Language)을 발표하였고 이를 활용한 온톨로지가 다양한 곳에 적용되어 구축이 되고 있다. 하지만, DL(Description Logic)기반인 OWL언어가 표현할 수 있는 규칙의 한계로 인하여 이를 확장하기 위한 연구가 활발히 진행되고 있다. 이러한 연구를 통하여 W3C에서는 OWL과 RuleML(Rule Markup Language)을 통합하여 규칙(Rule)에 대한 표현력이 더욱 향상된 SWRL(Semantic Web Rule Language) 언어를 제안하였다. 현재 이러한 연구는 OWL, SWRL 온톨로지 언어를 활용하고 Racer, Jess와 같은 엔진을 통하여 추론을 하는 형태로 활성화 되어 가고 있다. 하지만 이러한 형태로 온톨로지를 구축하는데 있어서 Racer를 이용한 DL추론, Jess를 이용한 Rule-base추론이 병행되고 있다. 이에 따라 본 논문에서는 온톨로지를 추론하기 위한 엔진으로 Racer와 Jess의 병행이 아닌, Jess를 이용하여 DL기반언어인 OR온톨로지를 추론하는 것 뿐 만 아니라 SWRL언어의 규칙 또한 추론할 수 있도록 한다. 이러한 시스템을 구축하기 위해 OWL을 Jess언어를 이용하여 추론할 수 있도록 개발된 OWLJessKB라는 툴과 SWRL언어를 추론하기 위해 Jess언어로 변환하여 이를 추론하는 SWRL Factory, 그리고 이출 이용하여 통합 추론하기 위한 세가지 통합 추론 플랫폼을 제안한다.

• Journal of KIISE
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• v.45 no.2
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• pp.113-125
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• 2018

Recently, with the advent of the Big Data era, we have gained the capability of acquiring vast amounts of knowledge from various fields. The collected knowledge is expressed by well-formed formula and in particular, OWL, a standard language of ontology, is a typical form of well-formed formula. The symbolic reasoning is actively being studied using large amounts of ontology data for extracting intrinsic information. However, most studies of this reasoning support the restricted rule expression based on Description Logic and they have limited applicability to the real world. Moreover, knowledge management for inaccurate information is required, since knowledge inferred from the wrong information will also generate more incorrect information based on the dependencies between the inference rules. Therefore, this paper suggests that the SWAT, knowledge management system should be combined with the SWRL (Semantic Web Rule Language) reasoning based on ATMS (Assumption-based Truth Maintenance System). Moreover, this system was constructed by combining with SWRL reasoning and ATMS for managing large ontology data based on the distributed In-memory framework. Based on this, the ATMS monitoring system allows users to easily detect and correct wrong knowledge. We used the LUBM (Lehigh University Benchmark) dataset for evaluating the suggested method which is managing the knowledge through the retraction of the wrong SWRL inference data on large data.

• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.6-11
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• 2018

With the development of the Internet of Things technology, a system that ensures the self-adaptability of an environment that includes various IoT devices is attracting public attention. The rules for determining behavior rules in existing self-adaptation systems are based on the assumption of changes in system members and environment. However, in the IoT environment, flexibility is required to determine the behavior rules of various types of IoT devices that change in real time. In this paper, we propose a rule configuration in a self-adaptive system using SWRL based on OWL ontology. The self-adaptive system using the OWL - SWRL rule configuration has two advantages. The first is based on OWL ontology, so we can define the characteristics and behavior of various types of IoT devices as an integrated concept. The second is to define the concept of a rule as a specific language type, and to add, modify and delete a rule at any time as needed. Through the rule configuration in the adaptive system, we have shown that the rule defined in SWRL can provide flexibility and deeper concept expression function to adaptability to IoT environment.

• Journal of KIISE
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• v.45 no.3
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• pp.224-233
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• 2018

Recently, there are many of studies on SWRL reasoning engine based on user-defined rules in a distributed environment using a large-scale ontology. Unlike the schema based axiom rules, efficient inference orders cannot be defined in SWRL rules. There is also a large volumet of network shuffled data produced by unnecessary iterative processes. To solve these problems, in this study, we propose a method that uses Map-Reduce algorithm and distributed in-memory framework to deduce multiple rules simultaneously and minimizes the volume data shuffling occurring between distributed machines in the cluster. For the experiment, we use WiseKB ontology composed of 200 million triples and 36 user-defined rules. We found that the proposed reasoner makes inferences in 16 minutes and is 2.7 times faster than previous reasoning systems that used LUBM benchmark dataset.

• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.67-81
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• 2006

While the existing Web focuses on the interface with human users based on HTML, the next generation Web will focus on the interaction among software agents by using XML and XML-based standards and technologies. The inference engine, which will serve as brains of software agents in the next generation Web, should thoroughly understand the Semantic Web, the standard language of the next generation Web. As abasis for the service, the W3C (World Wide Web Consortium) has recommended SWRL (Semantic Web Rule Language) which had been made by compounding OWL (Web Ontology Language) and RuleML (Rule Markup Language). In this research, we develop a backward chaining inference engine SMART-B (SeMantic web Agent Reasoning Tools -Backward chaining inference engine), which uses SWRL and OWL to represent rules and facts respectively. We analyze the requirements for the SWRL-based backward chaining inference and design analgorithm for the backward chaining inference which reflects the traditional backward chaining inference algorithm and the requirements of the next generation Semantic Web. We also implement the backward chaining inference engine and the administrative tools for fact and rule bases into Java components to insure the independence and portability among different platforms under the environment of Ubiquitous Computing.

• Journal of KIISE
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• v.44 no.4
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• pp.383-391
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• 2017

As we enter a new era of Big Data, the amount of semantic data has rapidly increased. In order to derive meaningful information from this large semantic data, studies that utilize the SWRL(Semantic Web Rule Language) are being actively conducted. SWRL rules are based on data extracted from a user's empirical knowledge. However, conventional reasoning systems developed on single machines cannot process large scale data. Similarly, multi-node based reasoning systems have performance degradation problems due to network shuffling. Therefore, this paper overcomes the limitations of existing systems and proposes more efficient distributed inference methods. It also introduces data partitioning strategies to minimize network shuffling. In addition, it describes a method for optimizing the incremental reasoning process through data selection and determining the rule order. In order to evaluate the proposed methods, the experiments were conducted using WiseKB consisting of 200 million triples with 83 user defined rules and the overall reasoning task was completed in 32.7 minutes. Also, the experiment results using LUBM bench datasets showed that our approach could perform reasoning twice as fast as MapReduce based reasoning systems.

• Journal of Intelligence and Information Systems
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• v.13 no.3
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• pp.17-29
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• 2007

Inference engine that performs the brain of software agent in next generation's web with various standards based on standard language of the web, XML has to understand SWRL (Semantic Web Rule Language) that is a language to express the rule in the Semantic Web. In this research, we want to develop a forward inference engine, SMART-F (SeMantic web Agent Reasoning Tools-Forward chaining inference engine) that uses SWRL as a rule express method, and OWL as a fact express method. In the traditional inference field, the Rete algorithm that improves effectiveness of forward rule inference by converting if-then rules to network structure is often used for forward inference. To apply this to the Semantic Web, we analyze the required functions for the SWRL-based forward inference, and design the forward inference algorithm that reflects required functions of next generation's Semantic Web deducted by Rete algorithm. And then, to secure each platform's independence and portability in the ubiquitous environment and overcome the gap of performance, we developed management tool of fact and rule base and forward inference engine. This is compatible with fact and rule base of SMART-B that was developed. So, this maximizes a practical use of knowledge in the next generation's Web environment.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.372-377
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• 2006

유비쿼터스 컴퓨팅 시대의 도래와 시맨틱 웹에 대한 관심이 높아짐에 따라 관련 기술인 온톨로지와 이를 이용한 추론 기술에 대한 요구가 증가하고 있다. 따라서, 추론이 가능한 시맨틱 웹 기반의 모델링과 추론에 대한 연구가 필요하다. 모델링을 위해 사용되는 OWL-DL과 임의의 사용자 규칙을 표현하는 SWRL은 각각 W3C의 표준안으로서, 유비쿼터스 컴퓨팅 환경에 효율적으로 자동적인 개인화 서비스[1][2]를 제공하는데 있어서 적합하다. 그러나 OWL-DL과 SWRL의 단순한 결합은 질의응답(Query Answering)에 대한 처리가 비결정 가능한(undecidable) 문제를 야기한다. 본 논문에서는, 비결정가능성 문제의 원인인 무한반복의 가능성을 제거하기 위한 블록(blocking) 방법을 제안한다. OWL-DL이 지닌 서술논리(Description Logic)의 표현력을 유지하고, 그에 따른 추론의 질적인 성능을 유지하는 범위에서 블록방법을 사용하여 결정 가능한 질의응답을 수행하는데 궁극적인 목적을 두고 있다. OWL-DL의 TBox에 위치하는 존재 정량자(Existential Quantifier)를 대체하고 ABox에 삽입하여, 무한반복의 가능성을 없애는 해결 방법을 제시한다. 실험은 비결정가능성 문제를 DL-Safe 규칙을 통해 해결한 KAON2와 비교하여 진행한다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.488-496
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• 2005

현재의 웹이 HTML을 바탕으로 인간 사용자와의 인터페이스에 초점을 맞추고 있는데 비하여, 차세대 웹은 XML 및 XML 기반 각종 표준들을 바탕으로 소프트웨어 에이전트와의 인터페이스에 초점을 맞추어 나가고 있다. 차세대 웹에서 소프트웨어 에이전트의 두뇌 역할을 수행하기 위하여 추론엔진은 차세대 웹의 표준 언어인 시맨틱 웹(Semantic Web)을 충실히 이해할 수 있어야 한다. 이를 위한 기초 작업의 일환으로 OWL(Web Ontology Language)과 RuleML(Rule Markup Language)이 W3C에 제안된 바 있다. 본 연구에서는 SWRL을 규칙 표현 방법으로 사용하고, OWL을 사실 표현 방법으로 사용하는 역방향 추론엔진인 SMART-B(SeMantic web Agent Reasoning Tools - Backward chaining inference engine)을 개발하고자 한다. 이를 위하여 SWRL 기반 역방향 추론을 위한 요구 기능을 분석하고, 기존 역방향 추론 알고리즘에 차세대 시맨틱 웹을 요구 기능을 반영한 역방향 추론 알고리즘을 설계하였다. 또한, 유비쿼터스 환경에서의 각종 플랫폼의 독립성과 이식성을 확보하고 기기 간의 성능 차이를 극복할 수 있도록 사실 베이스 및 규칙 베이스의 관리도구와 역방향 추론 엔진 등을 Java 프로그래밍 언어를 이용하여 단위 컴포넌트의 형태로 개발 중에 있다.