DOI QR코드

DOI QR Code

Distributed In-Memory based Large Scale RDFS Reasoning and Query Processing Engine for the Population of Temporal/Spatial Information of Media Ontology

미디어 온톨로지의 시공간 정보 확장을 위한 분산 인메모리 기반의 대용량 RDFS 추론 및 질의 처리 엔진

  • Received : 2015.11.16
  • Accepted : 2016.07.05
  • Published : 2016.09.15

Abstract

Providing a semantic knowledge system using media ontologies requires not only conventional axiom reasoning but also knowledge extension based on various types of reasoning. In particular, spatio-temporal information can be used in a variety of artificial intelligence applications and the importance of spatio-temporal reasoning and expression is continuously increasing. In this paper, we append the LOD data related to the public address system to large-scale media ontologies in order to utilize spatial inference in reasoning. We propose an RDFS/Spatial inference system by utilizing distributed memory-based framework for reasoning about large-scale ontologies annotated with spatial information. In addition, we describe a distributed spatio-temporal SPARQL parallel query processing method designed for large scale ontology data annotated with spatio-temporal information. In order to evaluate the performance of our system, we conducted experiments using LUBM and BSBM data sets for ontology reasoning and query processing benchmark.

대용량 미디어 온톨로지를 이용하여 의미 있는 지능형 서비스를 제공하기 위해 기존의 Axiom 추론뿐만 아니라 다양한 추론을 활용하는 지식 확장이 요구되고 있다. 특히 시공간 정보는 인공지능 응용분야에서 중요하게 활용될 수 있고, 시공간 정보의 표현과 추론에 대한 중요도는 지속적으로 증가하고 있다. 따라서 본 논문에서는 공간 정보를 추론에 활용하기 위해서 공공 주소체계에 대한 LOD를 대용량 미디어 온톨로지에 추가하고, 이러한 대용량 데이터 처리를 위해 인메모리 기반의 분산 처리 프레임워크를 활용하는 공간 추론을 포함하는 RDFS 추론 시스템을 제안한다. 또한 추론을 통해 확장된 데이터를 포함하는 대용량 온톨로지 데이터를 대상으로 하는 분산 병렬 시공간 SPARQL 질의 처리 방법에 대해서 설명한다. 제안하는 시스템의 성능을 측정하기 온톨로지 추론과 질의 처리 벤치 마킹을 위한 LUBM과 BSBM 데이터셋을 대상으로 실험을 진행했다.

Keywords

Acknowledgement

Grant : 현장전문가의 경험지식 획득 및 활용을 위한 경험지식플랫폼 개발 연구

Supported by : 한국산업기술평가관리원

References

  1. Allen, James F., "Maintaining knowledge about temporal intervals," Communications of the ACM 26.11, pp. 832-843, 1983. https://doi.org/10.1145/182.358434
  2. Zaharia, Matei, et al., "Spark: cluster computing with working sets," Proc. of the 2nd USENIX conference on Hot topics in cloud computing, Vol. 10, 2010.
  3. Armbrust, Michael, et al., "Spark SQL: Relational data processing in Spark," Proc. of the 2015 ACM SIGMOD International Conference on Management of Data. ACM, 2015.
  4. Urbani, Jacopo, et al., "WebPIE: A Web-scale parallel inference engine using MapReduce," Web Semantics: Science, Services and Agents on the World Wide Web 10, pp. 59-75, 2012. https://doi.org/10.1016/j.websem.2011.05.004
  5. Peuquet, Donna J., and Zhan Ci-Xiang, "An algorithm to determine the directional relationship between arbitrarily-shaped polygons in the plane," Pattern recognition 20.1, pp. 65-74, 1987. https://doi.org/10.1016/0031-3203(87)90018-5
  6. Renz, Jochen, "Maximal tractable fragments of the region connection calculus: A complete analysis," IJCAI, 1999.
  7. Schatzle, Alexander, et al., "PigSPARQL: A SPARQL Query Processing Baseline for Big Data," International Semantic Web Conference (Posters & Demos), 2013.
  8. Kornacker, Marcel, et al., "Impala: A modern, open-source SQL engine for Hadoop," Proc. of the Conference on Innovative Data Systems Research (CIDR'15), 2015.
  9. Schatzle, Alexander, et al., "Sempala: Interactive SPARQL Query Processing on Hadoop," The Semantic Web-ISWC 2014. Springer International Publishing, pp. 164-179, 2014.
  10. Zaharia, Matei, et al., "Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing," Proc. of the 9th USENIX conference on Networked Systems Design and Implementation, USENIX Association, 2012.
  11. Carroll, Jeremy J., et al., "Jena: implementing the semantic web recommendations," Proc. of the 13th international World Wide Web conference on Alternate track papers & posters. ACM, 2004.
  12. Bizer, Christian, and Andreas Schultz, "The berlin sparql benchmark," 2009.
  13. Guo, Yuanbo, Zhengxiang Pan, and Jeff Heflin, "LUBM: A benchmark for OWL knowledge base systems," Web Semantics: Science, Services and Agents on the World Wide Web 3.2, pp. 158-182, 2005. https://doi.org/10.1016/j.websem.2005.06.005