정보과학회 논문지 (Journal of KIISE)

  • 제45권2호
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  • Pages.113-125
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  • 2018
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  • 2383-630X(pISSN)
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  • 2383-6296(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
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SWAT: 분산 인-메모리 시스템 기반 SWRL과 ATMS의 효율적 결합 연구

SWAT: A Study on the Efficient Integration of SWRL and ATMS based on a Distributed In-Memory System

  • 투고 : 2017.04.20
  • 심사 : 2017.12.07
  • 발행 : 2018.02.15
⟨ 이전 논문 다음 논문 ⟩

초록

최근 빅데이터의 시대가 도래하여 다양한 분야로부터 다량의 지식을 얻을 수 있다. 수집된 지식은 정형화된 형태의 지식으로 가공하여 표현되며, 그 중 W3C의 온톨로지 표준 언어인 OWL이 대표적인 정형화 표현 형식이다. 이렇게 표현된 대용량의 온톨로지로부터 내재된 정보를 도출하기 위해 다양한 방법의 심볼릭 추론(Symbolic Reasoning) 연구가 활발하게 진행되고 있다. 그러나 대부분의 추론 연구들은 서술논리(Description Logic)표현 기반의 제한적인 규칙표현을 지원하며 실생활 기반의 서비스를 구축하기에는 많은 제약이 따른다. 또한 잘못된 지식으로부터 도출된 결과는 규칙들 사이의 종속관계에 따라 연쇄적으로 잘못된 지식이 생산될 수 있기 때문에 이러한 잘못된 지식에 대한 처리를 위한 지식관리가 필요하다. 따라서 본 논문에서는 해당 문제를 해결하기 위해 SWRL(Semantic Web Rule Language) 기반의 추론과 ATMS(Assumption-based Truth Maintenance System)간의 결합을 통해 새롭게 도출된 지식에 대한 관리를 할 수 있는 SWAT(SWRL + ATMS) 시스템을 제안한다. 또한 이 시스템은 대용량 데이터를 처리하기 위해 분산 인-메모리 프레임워크 기반의 SWRL추론과 ATMS를 병합 구축하였으며 이를 바탕으로 웹 형태의 ATMS 모니터링 시스템을 통하여 사용자가 손쉽게 잘못된 지식을 검색 및 수정할 수 있도록 한다. 본 논문에서 제안하는 방법에 대한 평가를 위해 LUBM(Lehigh University Benchmark)데이터 셋을 사용하였으며, 대용량 데이터에 대한 SWRL 추론과 잘못 추론된 정보에 대한 삭제를 통해 효율적인 추론과 관리가 가능한 결합 방법임을 증명한다.

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.

키워드

과제정보

연구 과제 주관 기관 : 한국연구재단

참고문헌

  1. J. Doyle, "A truth maintenance system," Artificial Intelligence, Vol. 12, No. 3, pp. 231-272, Nov. 1979. https://doi.org/10.1016/0004-3702(79)90008-0
  2. Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M., (2004, May 21). SWRL: A Semantic Web Rule Language Combining OWL and RuleML [Online], Available: https://www.w3.org/Submission/SWRL (downloaded 2017. April. 15)
  3. Jeffrey Dean, Sanfay Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM-50th anniversary issue: 1958-2008, Vol. 51, No. 1, Jan. 2008.
  4. Zaharia, Matei, et. al., "Spark: cluster computing with working sets," Proc. of the 2nd USENIX conference on Hot topics in cloud computing, pp. 10-10, Jun. 2010.
  5. Evren Sirin, Bijan P., Bernardo C. G., Aditya K., Yarden K., "Pellet: A practical OWL-DL reasoner," Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 5, No. 2, pp. 51-53, Jun. 2007. https://doi.org/10.1016/j.websem.2007.03.004
  6. Birte Glimm, Ian Horrocks, Boris Motik, Giorgos Stoilos, Zhe Wang, "HermiT: An OWL 2 Reasoner," Journal of Automated Reasoning, Vol. 53, No. 3, pp. 245-269, Oct. 2014. https://doi.org/10.1007/s10817-014-9305-1
  7. Dmitry Tsarkov, Ian Horrocks, "FaCT++ Description Logic Reasoner: System Description," International Joint Conference on Automated Reasoning: Automated Reasoning, pp. 292-297, Aug. 2006.
  8. Boris Motik, Yavor Nenov, Robert Piro, Ian Horrocks, Zhe Wu, Jay Banerjee, "RDFox: A Highly-Scalable RDF Store," International Semantic Web Conference, pp. 3-20, Oct. 2015.
  9. Volker Haarslev and Ralf Moller, "Racer system description," International Joint Conference on Automated Reasoning: Automated Reasoning, pp. 701-705, Jun. 2001.
  10. Philipp Cimiano, Johanna Volker, "Text2Onto: a framework for ontology learning and data-driven change discovery," Proc. of the 10th international conference on Natrual Language Processing and Information Systems, pp. 227-238, Jun. 2005.
  11. Min-Su Jang, Joo-Cahn Sohn, "Bossam: An Extended Rule Engine for OWL Inferencing," International Workshop on Rules and Markup Language for the Semantic Web, pp. 128-138, Nov. 2004.
  12. N. Hai, N. Alechina, B. Logan, "Axiom Pinpointing Using an Assumption-Based Truth Maintenance System," Proc. of the 25th International Wrokshop on Description Logics, pp. 290-300, Jun. 2012.
  13. Je-Min Kim, Young-Tack Park, "An Approach to Detect All Axioms Responsible for Unsatisfiable Concepts in Ontology," Journal of KIISE: Software and Applications, Vol. 39, No. 6, pp. 464-472, Jun. 2012.
  14. Michael Dixon, Johan de Kleer, "Massively Parallel Assumption-based Truth Maintenance," Proc. of the Seventh AAAI National Conference on Artificial Intelligence, pp. 199-204, Aug. 1988.
  15. Batselem Jagvaral, Young-Tack Park, "Distributed Assumption-Based Truth Maintenance System for Scalable Reasoning," Journal of KIISE: Software and Applications, Vol. 43, No. 10, pp. 1115-1123, Oct. 2016. https://doi.org/10.5626/JOK.2016.43.10.1115
  16. Batselem Jagvaral, Young-Tack Park, "Distributed scalable RDFS reasoning," Big Data and Smart Computing, pp. 31-34, Feb. 2015.
  17. Schatzle, Alexander, et al., "Sempala: Interactive SPARQL query processing on hadoop," International Semantic Web Conference, pp. 164-179, Oct. 2014.