Knowledge Authoring for Rules and Actions

Abstract

Abstract Knowledge representation and reasoning (KRR) systems describe and reason with complex concepts and relations in the form of facts and rules. Unfortunately, wide deployment of KRR systems runs into the problem that domain experts have great difficulty constructing correct logical representations of their domain knowledge. Knowledge engineers can help with this construction process, but there is a deficit of such specialists. The earlier Knowledge Authoring Logic Machine (KALM) based on Controlled Natural Language (CNL) was shown to have very high accuracy for authoring facts and questions. More recently, KALM FL , a successor of KALM, replaced CNL with factual English, which is much less restrictive and requires very little training from users. However, KALM FL has limitations in representing certain types of knowledge, such as authoring rules for multi-step reasoning or understanding actions with timestamps. To address these limitations, we propose KALM RA to enable authoring of rules and actions. Our evaluation using the UTI guidelines benchmark shows that KALM RA achieves a high level of correctness (100%) on rule authoring. When used for authoring and reasoning with actions, KALM RA achieves more than 99.3% correctness on the bAbI benchmark, demonstrating its effectiveness in more sophisticated KRR jobs. Finally, we illustrate the logical reasoning capabilities of KALM RA by drawing attention to the problems faced by the recently made famous AI, ChatGPT.