Consistency and provenance in rule processing

  • Authors:
  • Eric Jui-Yi Kao

  • Affiliations:
  • Computer Science Department, Stanford University, Stanford, CA

  • Venue:
  • RuleML'11 Proceedings of the 5th international conference on Rule-based modeling and computing on the semantic web
  • Year:
  • 2011

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Abstract

Open collections of data and rules on the web are typically characterized by heterogeneous quality and imperfect consistency. In reasoning with data and rules on the web, it is important to know where an answer comes from (provenance) and whether the it is reasonable considering the inconsistencies (inconsistency-tolerance). In this paper, I draw attention to the idea that provenance and inconsistency-tolerance play mutually supporting roles under the theme of reasoning with imperfect information on the web. As a specific example, I make use of basic provenance information to avoid unreasonable answers in reasoning with rules and inconsistent data.