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Title: Knowledge Authoring for Rules and Actions
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 withfactualEnglish, which is much less restrictive and requires very little training from users. However, KALMFLhas 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 KALMRAto enable authoring of rules and actions. Our evaluation using the UTI guidelines benchmark shows that KALMRAachieves a high level of correctness (100%) on rule authoring. When used for authoring and reasoning with actions, KALMRAachieves 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 KALMRAby drawing attention to the problems faced by the recently made famous AI, ChatGPT.  more » « less
Award ID(s):
1814457
PAR ID:
10471702
Author(s) / Creator(s):
; ;
Publisher / Repository:
Cambridge University Press
Date Published:
Journal Name:
Theory and Practice of Logic Programming
Volume:
23
Issue:
4
ISSN:
1471-0684
Page Range / eLocation ID:
797 to 811
Subject(s) / Keyword(s):
knowledge authoring knowledge representation and reasoning natural language understanding frame-based parsing
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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