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Confidence Value based Large Scale OWL Horst Ontology Reasoning
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 5,  2016, pp.553-561
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.5.553
 Title & Authors
Confidence Value based Large Scale OWL Horst Ontology Reasoning
Lee, Wan-Gon; Park, Hyun-Kyu; Jagvaral, Batselem; Park, Young-Tack;
 
 Abstract
Several machine learning techniques are able to automatically populate ontology data from web sources. Also the interest for large scale ontology reasoning is increasing. However, there is a problem leading to the speculative result to imply uncertainties. Hence, there is a need to consider the reliability problems of various data obtained from the web. Currently, large scale ontology reasoning methods based on the trust value is required because the inference-based reliability of quantitative ontology is insufficient. In this study, we proposed a large scale OWL Horst reasoning method based on a confidence value using spark, a distributed in-memory framework. It describes a method for integrating the confidence value of duplicated data. In addition, it explains a distributed parallel heuristic algorithm to solve the problem of degrading the performance of the inference. In order to evaluate the performance of reasoning methods based on the confidence value, the experiment was conducted using LUBM3000. The experiment results showed that our approach could perform reasoning twice faster than existing reasoning systems like WebPIE.
 Keywords
Big Data;Ontology;Reasoning;Spark;Confidence Value;OWL Horst;
 Language
Korean
 Cited by
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