Knowledge Authoring and Question Answering with KALM

Tiantian Gao
(Stony Brook University)

Knowledge representation and reasoning (KRR) is one of the key areas in artificial intelligence (AI) field. It is intended to represent the world knowledge in formal languages (e.g., Prolog, SPARQL) and then enhance the expert systems to perform querying and inference tasks. Currently, constructing large scale knowledge bases (KBs) with high quality is prohibited by the fact that the construction process requires many qualified knowledge engineers who not only understand the domain-specific knowledge but also have sufficient skills in knowledge representation. Unfortunately, qualified knowledge engineers are in short supply. Therefore, it would be very useful to build a tool that allows the user to construct and query the KB simply via text. Although there is a number of systems developed for knowledge extraction and question answering, they mainly fail in that these system don't achieve high enough accuracy whereas KRR is highly sensitive to erroneous data. In this thesis proposal, I will present Knowledge Authoring Logic Machine (KALM), a rule-based system which allows the user to author knowledge and query the KB in text. The experimental results show that KALM achieved superior accuracy in knowledge authoring and question answering as compared to the state-of-the-art systems.

In Bart Bogaerts, Esra Erdem, Paul Fodor, Andrea Formisano, Giovambattista Ianni, Daniela Inclezan, German Vidal, Alicia Villanueva, Marina De Vos and Fangkai Yang: Proceedings 35th International Conference on Logic Programming (Technical Communications) (ICLP 2019), Las Cruces, NM, USA, September 20-25, 2019, Electronic Proceedings in Theoretical Computer Science 306, pp. 389–395.
Published: 19th September 2019.

ArXived at: https://dx.doi.org/10.4204/EPTCS.306.52 bibtex PDF
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