• Journal of KIISE
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• v.42 no.9
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• pp.1109-1116
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• 2015

In this paper, we propose a query processing approach that uses the Spark functional programming and distributed memory system to solve the computational overhead of SPARQL. In the semantic web, RDF ontology data is produced at large scale, and the main challenge for the semantic web is to query and manipulate such a large ontology with a high throughput. The most existing studies on SPARQL have focused on deploying the Hadoop MapReduce framework, and although approaches based on Hadoop MapReduce have shown promising results, they achieve a low level of throughput due to the underlying distributed file processes. Therefore, in order to speed up the query processes, we suggest query- processing methods that are based on memory caching in distributed memory system. Our approach is also integrated with a clause unification method for propagating between the clauses that exploits Spark join, map and filter methods along with caching. In our experiments, we have achieved a high level of performance relative to other approaches. In particular, our performance was nearly similar to that of Sempala, which has been considered to be the fastest query processing system.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.408-411
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• 2006

유비쿼터스 컴퓨팅 환경에서 서비스를 제공함에 있어 지능적인 수행 능력은 사용자의 만족도를 높여주는 핵심 요소이다. 시스템의 지능을 부여하기 위해서는 지식을 관리, 처리, 활용하는 기능이 필요한데, 이들 기능은 그 지식이 어떻게 표현되어 있는지에 큰 영향을 받는다. 일차 술어 논리 기반 지식 표현 방법은 폭넓은 표현 범위와 유연한 지식 정의, 추론 방법으로 선호되고 있지만, 복잡한 계산 비용을 갖고 있기 때문에, 전문적인 지식 처리 시스템이 아닌 경우, 불필요한 계산 비용이 소요된다. Description Logic은 Frame기반 지식 표현 방식으로 일차 술어 논리를 사용하는 것보다 지식을 표현할 수 있는 범위는 제한적이지만, 빠른 추론 결과를 보장해 준다. 유비쿼터스 컴퓨팅 환경에서는 분산된 다양한 오브젝트들이 협력과정을 통해 사용자에게 지능적인 서비스를 제공하게 되고, 이들 개별적인 오브젝트들은 저사양의 계산능력을 갖고 있다고 가정된다. 그러므로, 저사양의 컴퓨팅 오브젝트들을 조합하여 지능적인 서비스를 성공적으로 제공하기 위해서, 각각의 오브젝트들은 제한된 지식을 효과적으로 관리할 수 있는 방법이 필요하다. 이를 위해 본 논문에서는 Frame 기반의 Description Logic을 기반으로 SUNHI의 표현 범위를 가진 인식론적 온톨로지 표현 언어를 제안하고, SUNHI의 표현 범위의 효율성을 증명하고자 한다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.273-279
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• 2005

월드와이드웹 (WWW) 기반의 전자상거래는 주로 데이터베이스를 기반으로 서비스를 제공하고 있다. 그러나 월드와이드웹 기반의 전자상거래는 단순 키워드 검색에만 의존하고 있다. 이러한 검색은 데이터베이스 자체로는 의미적인 정보를 효과적으로 처리하기에는 많은 문제점이 있다. 1999년 말에 의미적인 정보를 효과적으로 처리하기 할 수 있는 시맨틱 웹 이 제안되었다. 시맨틱 웹은 의미적인 정보를 담고 있는 지식베이스(Knowledge Bases)인 온톨로지를 기반으로 하고 있다. 그러나 온툴로지의 생성은 많은 부분을 휴리스틱에 의존하고 있기 때문에 많은 시간과 비용이 소비된다. 따라서 우리는 이와 같은 문제를 해결하기 위하여 데이터베이스에서 온톨로지를 생성하는 방법을 제안한다. 데이터베이스는 도메인을 잘 나타내고 있는 정보의 저장소이므로 데이터베이스로부터의 온톨로지 생성은 분석, 설계 등의 사전 작업이 필요하지 않아 시간과 비용의 소비를 줄 일 수 있는 장점이 있다. 우리는 데이터베이스에서 스키마를 추출, 뼈대그래프$^{1}$ 를 생성하고 개념그래프로 확장하여 도메인을 잘 나타낼 수 있는 온톨로지를 생성하는 알고리즘을 제안하고 제안된 알고리즘을 통하여 온톨로지를 생성을 함으로서 제안된 생성 방법을 검증한다. 제안한 방법으로 생성된 온톨로지는 단순 키워드 검색에서 의미적인 검색을 할 수 있는 시맨틱 웹 서비스의 기반이 되므로 의미적 검색이 가능한 전자상거래 서비스를 구축하는데 시간과 비용의 소비를 줄임으로 차세대 전자상거래의 초석이 된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.43-46
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• 2009

의료 분야의 정보화 움직임으로 인해 단순한 정보 저장과 검색 시스템에서 벗어나 지능화된 서비스를 제공 해주는 시멘틱 웹 기반의 의료 시스템이 요구되고 있다. 이에 한의학 분야도 한의사의 진단을 보조할 수 있는 지식 기반 시스템에 대한 요구가 증대되고 있으며 관련 시스템들이 개발되어지고 있다. 온톨로지는 시멘틱 웹의 핵심적인 지식 체계로 지식의 처리와 추론이 가능하므로 질의 및 논리 추론을 통하여 진단을 내리는 한의학 지식 데이터베이스 구축에 적합하다. 본 연구에서는 한의학 분야의 지식을 보다 의미적이고 체계적으로 표현하고 온톨로지를 이용한 검색 결과의 이점을 보여주기 위해 추론 기술을 접목시켜 한의학 온톨로지를 개발하였다.

• Journal of KIISE
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• v.41 no.10
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• pp.762-773
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• 2014

As the web of data is increasingly producing large RDFS datasets, it becomes essential in building scalable reasoning engines over large triples. There have been many researches used expensive distributed framework, such as Hadoop, to reason over large RDFS triples. However, in many cases we are required to handle millions of triples. In such cases, it is not necessary to deploy expensive distributed systems because logic program based reasoners in a single machine can produce similar reasoning performances with that of distributed reasoner using Hadoop. In this paper, we propose a scalable RDFS reasoner using logical programming methods in a single machine and compare our empirical results with that of distributed systems. We show that our logic programming based reasoner using a single machine performs as similar as expensive distributed reasoner does up to 200 million RDFS triples. In addition, we designed a meta data structure by decomposing the ontology triples into separate sectors. Instead of loading all the triples into a single model, we selected an appropriate subset of the triples for each ontology reasoning rule. Unification makes it easy to handle conjunctive queries for RDFS schema reasoning, therefore, we have designed and implemented RDFS axioms using logic programming unifications and efficient conjunctive query handling mechanisms. The throughputs of our approach reached to 166K Triples/sec over LUBM1500 with 200 million triples. It is comparable to that of WebPIE, distributed reasoner using Hadoop and Map Reduce, which performs 185K Triples/sec. We show that it is unnecessary to use the distributed system up to 200 million triples and the performance of logic programming based reasoner in a single machine becomes comparable with that of expensive distributed reasoner which employs Hadoop framework.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.671-678
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• 2019

The semantic information provided through the semantic-based IoT system will produce new high value-added products that are completely different from what we have known and experienced. From this point of view, the key issue of current IoT technology and applications is the development of an intelligent IoT platform architecture. The proposed system collects the IoT data of the sensors from the cloud computer, converts them into RDF, and annotates them with semantics. The converted semantic data is shared and utilized through the ontology repository. We use KT's IoTMakers as a cloud computing environment, and the ontology repository uses Jena's Fuseki server to express SPARQL query results on the web using Daum Map API and Highcharts API. This gives people the opportunity to access the semantic IoT mash-up service easily and has various application possibilities.

• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.63-70
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• 2019

We extract the symptom of panic disorder from the context awareness environment. It extracts body context information through natural movement that exists in everyday life and uses a component of panic disorder. The ontology theory can be used to provide information on the degree of symptoms of panic disorder through inference process. For the components of panic disorder to the information processing based on ontology are defined as Classes. Panic disorder index is expressed through ontology modeling so that the condition of panic disorder can be known. The derivation of panic disorder component and panic disorder index will enable context awareness based information service for panic disorder. The context information is periodically synchronized with the context awareness on based device. Panic disorder can be used to improve the lifestyle of panic disorder.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.282-296
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• 2010

OWL is an ontology language for the Semantic Web, and suited to modelling the knowledge of a specific domain in the real-world. Ontology also can infer new implicit knowledge from the explicit knowledge. However, the modeled knowledge cannot be complete as the whole of the common-sense of the human cannot be represented totally. Ontology do not concern handling nonmonotonic reasoning to detect incomplete modeling such as the integrity constraints and exceptions. A default rule can handle the exception about a specific class in ontology. Integrity constraint can be clear that restrictions on class define which and how many relationships the instances of that class must hold. In this paper, we propose a practical reasoning system for open and closed-world reasoning that supports a novel hybrid integration of ontology based on open world assumption (OWA) and non-monotonic rule based on closed-world assumption (CWA). The system utilizes a method to solve the problem which occurs when dealing with the incomplete knowledge under the OWA. The method uses the answer set programming (ASP) to find a solution. ASP is a logic-program, which can be seen as the computational embodiment of non-monotonic reasoning, and enables a query based on CWA to knowledge base (KB) of description logic. Our system not only finds practical cases from examples by the Protege, which require non-monotonic reasoning, but also estimates novel reasoning results for the cases based on KB which realizes a transparent integration of rules and ontologies supported by some well-known projects.

• Journal of KIISE
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• v.42 no.3
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• pp.307-319
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• 2015

Current ontology studies use the Hadoop distributed storage framework to perform map-reduce algorithm-based reasoning for scalable ontologies. In this paper, however, we propose a novel approach for scalable Web Ontology Language (OWL) Horst Lite ontology reasoning, based on distributed cluster memories. Rule-based reasoning, which is frequently used for scalable ontologies, iteratively executes triple-format ontology rules, until the inferred data no longer exists. Therefore, when the scalable ontology reasoning is performed on computer hard drives, the ontology reasoner suffers from performance limitations. In order to overcome this drawback, we propose an approach that loads the ontologies into distributed cluster memories, using Spark (a memory-based distributed computing framework), which executes the ontology reasoning. In order to implement an appropriate OWL Horst Lite ontology reasoning system on Spark, our method divides the scalable ontologies into blocks, loads each block into the cluster nodes, and subsequently handles the data in the distributed memories. We used the Lehigh University Benchmark, which is used to evaluate ontology inference and search speed, to experimentally evaluate the methods suggested in this paper, which we applied to LUBM8000 (1.1 billion triples, 155 gigabytes). When compared with WebPIE, a representative mapreduce algorithm-based scalable ontology reasoner, the proposed approach showed a throughput improvement of 320% (62k/s) over WebPIE (19k/s).

• The KIPS Transactions:PartC
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• v.13C no.1 s.104
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• pp.129-136
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• 2006

Information Retrieval System aims towards providing fast and accurate information to users. However, current search systems are based on plain svntactic analysis which makes it difficult for the user to find the exact required information. This paper proposes the SW-IRS (Semantic Web-based Information Retrieval System) using an Ontology Server. The proposed system is purposed to maximize efficiency and accuracy of information retrieval of unstructured and semi-structured documents by using an agent-based automatic classification technology and semantic web based information retrieval methods. For interoperability and easy integration, RDF based repository system is supported, and the newly developed ranking algorithm was applied to rank search results and provide more accurate and reliable information. Finally, a new ranking algorithm is suggested to be used to evaluate performance and verify the efficiency and accuracy of the proposed retrieval system.