Abstract:
Knowledge representation and reasoning (KRR) systems represent knowledge as collections of facts and rules. Like databases, KRR systems contain information about domains of human activities like industrial enterprises, science, and business. KRRs can represent complex concepts and relations, and they can query and manipulate information in sophisticated ways. Unfortunately, the KRR technology has been hindered by the fact that specifying the requisite knowledge requires skills that most domain experts do not have, and professional knowledge engineers are hard to find. One solution could be to extract knowledge from English text, and a number of works have attempted to do so (OpenSesame, Google's Sling, etc.). Unfortunately, at present, extraction of logical facts from unrestricted natural language is still too inaccurate to be used for reasoning, while restricted grammars of the language (so-called controlled natural languages, or CNL) are hard for the users to learn and use. Nevertheless, some recent CNL-based approaches, such as the Knowledge Authoring Logic Machine (KALM), have shown to have very high accuracy compared to others, and a natural question is to what extent the CNL restrictions can be lifted. Besides the CNL restrictions, KALM has limitations in terms of the types of knowledge it can represent. For example, KALM users cannot author rules to support multi-step reasoning, nor can they author actions associated with occurrences of events, which hinders its ability to do time-related reasoning. Apart from the aforementioned shortcomings, the system's speed was insufficient to adequately support the overall knowledge authoring process.
To address these issues, we propose an extension of KALM called KALM for Factual Language (KALMFL). KALMFL uses a neural parser for natural language, mStanza, to parse what we call factual English sentences, which require little grammar training to use. Building upon KALMFL, we propose KALM for Rules and Actions (KALMRA), to represent and reason with rules and actions. Furthermore, we identify the reasons behind the slow speed of KALM and make optimizations to address this issue. Our evaluation using multiple benchmarks shows that our approaches achieve a high level of correctness on fact and query authoring (95%) and on rule authoring (100%). When used for authoring and reasoning with actions, our approach achieves more than 99.3% correctness, demonstrating its effectiveness in enabling more sophisticated knowledge representation and reasoning. We also illustrate the logical reasoning capabilities of our approach by drawing attention to the problems faced by the famous AI, ChatGPT. Finally, the evaluation of the newly proposed speed optimization points not only to a 68% runtime improvement but also yields better accuracy of the overall system.



















Note: Upon successful completion of the defense there will be a reception for Yuheng in front of NCS 220. All are welcome.