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In logic-based approaches to reasoning tasks such as Recognizing TextualEntailment (RTE), it is important for a system to have a large amount ofknowledge data. However, there is a tradeoff between adding more knowledge datafor improved RTE performance and maintaining an efficient RTE system, as such abig database is problematic in terms of the memory usage and computationalcomplexity. In this work, we show the processing time of a state-of-the-artlogic-based RTE system can be significantly reduced by replacing itssearch-based axiom injection (abduction) mechanism by that based on KnowledgeBase Completion (KBC). We integrate this mechanism in a Coq plugin thatprovides a proof automation tactic for natural language inference.Additionally, we show empirically that adding new knowledge data contributes tobetter RTE performance while not harming the processing speed in thisframework.

Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference