Trust and Hybrid Reasoning for Ontological Knowledge Bases

Projects such as Libra and Cimple have built systems to capture knowledge in a research community and to respond to semantic queries. However, they lack the support for a knowledge base that can evolve over time while responding to queries requiring reasoning. We consider a semantic web that covers linked data about science research that are being harvested from the Web and are supplemented and edited by community members. We use ontologies to incorporate semantics to detect conflicts and resolve inconsistencies, and to infer new relations or proof statements with a reasoning engine. We consider a semantic web subject to changes in the knowledge base, the underlying ontology or the rule set that governs the reasoning. In this paper we explore the idea of trust where each change to the knowledge base is analyzed as to what subset of the knowledge base can still be trusted. We present algorithms that adapt the reasoner such that, when proving a goal, it does a simple retrieval when it encounters trusted items and backward chaining over untrusted items. We provide an evaluation of our proposed modifications that show that our algorithm is conservative and that it provides significant gains in performance for certain queries.