Towards Provenance in Heterogeneous Knowledge Bases

LOGIC PROGRAMMING AND NONMONOTONIC REASONING, LPNMR 2022(2022)

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摘要
A rapidly increasing amount of data, information and knowledge is becoming available on the Web, often written in different formats and languages, adhering to standardizations driven by the World Wide Web Consortium initiative. Taking advantage of all this heterogeneous knowledge requires its integration for more sophisticated reasoning services and applications. To fully leverage the potential of such systems, their inferences should be accompanied by justifications that allow a user to understand a proposed decision/recommendation, in particular for critical systems (healthcare, law, finances, etc.). However, determining such justifications has commonly only been considered for a single formalism, such as relational databases, description logic ontologies, or declarative rule languages. In this paper, we present the first approach for providing provenance for heterogeneous knowledge bases building on the general framework of multi-context systems, as an abstract, but very expressive formalism to represent knowledge bases written in different formalisms and the flow of information between them. We also show under which conditions and how provenance information in this formalism can be computed.
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关键词
Provenance,Heterogeneous knowledge bases,Multi-context systems
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