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Knowledge Authoring for Rules and Actions

Published online by Cambridge University Press:  12 July 2023

YUHENG WANG Open the ORCID record for YUHENG WANG [Opens in a new window] ,
PAUL FODOR Open the ORCID record for PAUL FODOR [Opens in a new window]  and
MICHAEL KIFER
YUHENG WANG
Affiliation:
Stony Brook University, Stony Brook, NY, USA (e-mails: yuhewang@cs.stonybrook.edu, pfodor@cs.stonybrook.edu, kifer@cs.stonybrook.edu)
PAUL FODOR
Affiliation:
Stony Brook University, Stony Brook, NY, USA (e-mails: yuhewang@cs.stonybrook.edu, pfodor@cs.stonybrook.edu, kifer@cs.stonybrook.edu)
MICHAEL KIFER
Affiliation:
Stony Brook University, Stony Brook, NY, USA (e-mails: yuhewang@cs.stonybrook.edu, pfodor@cs.stonybrook.edu, kifer@cs.stonybrook.edu)
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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, 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.

Keywords

knowledge authoringknowledge representation and reasoningnatural language understandingframe-based parsing

Information

Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 4: 2023 International Conference on Logic Programming , July 2023 , pp. 797 - 811
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Copyright
© The Author(s), 2023. Published by Cambridge University Press

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Footnotes

*

Research partially funded by NSF grant 1814457.

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