Knowledge Authoring and Question Answering via Controlled Natural Language

Abstract

Knowledge acquisition from text is the process of automatically acquiring, organizing and structuring knowledge from text which can be used to perform question answering or complex reasoning. However, current state-of-the-art systems are limited by the fact that they are not able to construct the knowledge base with high quality as knowledge representation and reasoning (KRR) has a keen requirement for the accuracy of data. Controlled Natural Languages (CNLs) emerged as a technology to author knowledge using a restricted subset of English. However, they still fail to do so as sentences that express the same information may be represented by different forms. Current CNL systems have limited power to standardize sentences that express the same meaning into the same logical form. We solved this problem by building the Knowledge Authoring Logic Machine (KALM), which is a technology for domain experts who are not familiar with logic to author knowledge using CNL. The system performs semantic analysis of English sentences and achieves superior accuracy of standardizing sentences that express the same meaning to the same logical representation. Besides, we developed the query part of KALM to perform question answering, which also achieves very high accuracy in query understanding.