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Spark based Scalable RDFS Ontology Reasoning over Big Triples with Confidence Values
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 1,  2016, pp.87-95
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.1.87
 Title & Authors
Spark based Scalable RDFS Ontology Reasoning over Big Triples with Confidence Values
Park, Hyun-Kyu; Lee, Wan-Gon; Jagvaral, Batselem; Park, Young-Tack;
 
 Abstract
Recently, due to the development of the Internet and electronic devices, there has been an enormous increase in the amount of available knowledge and information. As this growth has proceeded, studies on large-scale ontological reasoning have been actively carried out. In general, a machine learning program or knowledge engineer measures and provides a degree of confidence for each triple in a large ontology. Yet, the collected ontology data contains specific uncertainty and reasoning such data can cause vagueness in reasoning results. In order to solve the uncertainty issue, we propose an RDFS reasoning approach that utilizes confidence values indicating degrees of uncertainty in the collected data. Unlike conventional reasoning approaches that have not taken into account data uncertainty, by using the in-memory based cluster computing framework Spark, our approach computes confidence values in the data inferred through RDFS-based reasoning by applying methods for uncertainty estimating. As a result, the computed confidence values represent the uncertainty in the inferred data. To evaluate our approach, ontology reasoning was carried out over the LUBM standard benchmark data set with addition arbitrary confidence values to ontology triples. Experimental results indicated that the proposed system is capable of running over the largest data set LUBM3000 in 1179 seconds inferring 350K triples.
 Keywords
scalable ontology;RDFS reasoning;confidence value;spark;
 Language
Korean
 Cited by
 References
1.
Patric Hayes and Brian McBride, RDF Semantics, Technical report, W3C Recommendation, 2004.

2.
Brickley, Dan, and Ramanathan V. Guha, "{RDF vocabulary description language 1.0: RDF schema," 2004.

3.
M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, "Spark: Cluster Computing with Working Sets," Proc. of the HotCloud 2010, pp. 10-16, Jun. 2010.

4.
M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauley, M. J. Franklin, S. Shenker, and I. Stoica, "Resilient Distributed Datasets: A Fault- Tolerant Abstraction for In-Memory Cluster Computing," Proc. of the 9th USENIX conference on Networked Systems Design and Implementation, 2012.

5.
J. Urbani, "RDFS/OWL reasoning using the Map- Reduce framework," Master thesis. 2009.

6.
Urbani, Jacopo, et al., "WebPIE: a web-scale parallel inference engine," Third IEEE International Scalable Computing Challenge (SCALE2010), Held in Conjunction with the 10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2010), Melbourne, Australia, 2010.

7.
Liu, Chang, et al., "Fuzzy reasoning over RDF data using OWL vocabulary," Proc. of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology-Volume 01. IEEE Computer Society, 2011.

8.
Liu, Chang, et al., "Large scale fuzzy pd* reasoning using mapreduce," The Semantic Web-ISWC 2011, Springer Berlin Heidelberg, 405-420, 2011.

9.
Stoilos, Giorgos, and Giorgos Stamou, "Reasoning with fuzzy extensions of OWL and OWL 2," Knowledge and information systems 40.1 (2014): 205-242. crossref(new window)

10.
Mazzieri, Mauro, Aldo Franco Dragoni, and U. P. D. Marche, "A Fuzzy Semantics for Semantic Web Languages," ISWC-URSW. 2005.

11.
Adams, J. Barclay, "Probabilistic reasoning and certainty factors," Rule-Based Expert Systems (1984): 263-271.

12.
Heckerman, David E., and Edward H. Shortliffe, "From certainty factors to belief networks," Artificial Intelligence in Medicine 4.1 (1992): 35-52. crossref(new window)

13.
Y. Chai, L. Jia, Z. Zhang, "Mamdani Model based Adaptive Neural Fuzzy Inference System and its Application," International Journal of Information and Mathematical Sciences, pp. 22-29, 2009.

14.
Toth-Laufer, Edit, Marta Takacs, and Imre J. Rudas, "Conjunction and disjunction operators in neuro-fuzzy risk calculation model simplification," Computational Intelligence and Informatics (CINTI), 2012 IEEE 13th International Symposium on. IEEE, 2012.

15.
Jagvaral, Batselem, et al., "Scalable RDFS Reasoning using Logic Programming Approach in a Single Machine," Journal of KIISE 41.10 (2014): 762-773. (in Korean) crossref(new window)

16.
Jagvaral, Batselem, and Young-Tack Park. "Distributed scalable RDFS reasoning," Big Data and Smart Computing (BigComp), 2015 International Conference on. IEEE, 2015.