Knowledge Authoring for Rules and Actions
Yuheng Wang Paul Fodor Michael Kifer
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, KALMFL, a
successor of KALM, replaced CNL with factual English, which is much less
restrictive and requires very little training from users. However, KALMFL 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 KALMRA to enable authoring of rules and actions.
Our evaluation using the UTI guidelines benchmark shows that KALMRA achieves a
high level of correctness (100%) on rule authoring. When used for authoring and
reasoning with actions, KALMRA 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 KALMRA by drawing
attention to the problems faced by the recently made famous AI, ChatGPT.