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Knowledge Authoring with Factual English, Rules, and Actions

Yuheng Wang

TL;DR

This work proposes KALM for Rules and Actions (KALMR), to represent and reason with rules and actions, and illustrates the logical reasoning capabilities of this approach by drawing attention to the problems faced by the famous AI, ChatGPT.

Abstract

Knowledge representation and reasoning systems represent knowledge as collections of facts and rules. 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. 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. To address these issues, we propose an extension of KALM called KALM for Factual Language (KALMF). KALMF uses a neural parser for natural language, MS, to parse what we call factual English sentences, which require little grammar training to use. Building upon KALMF, we propose KALM for Rules and Actions (KALMR), 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.

Knowledge Authoring with Factual English, Rules, and Actions

TL;DR

This work proposes KALM for Rules and Actions (KALMR), to represent and reason with rules and actions, and illustrates the logical reasoning capabilities of this approach by drawing attention to the problems faced by the famous AI, ChatGPT.

Abstract

Knowledge representation and reasoning systems represent knowledge as collections of facts and rules. 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. 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. To address these issues, we propose an extension of KALM called KALM for Factual Language (KALMF). KALMF uses a neural parser for natural language, MS, to parse what we call factual English sentences, which require little grammar training to use. Building upon KALMF, we propose KALM for Rules and Actions (KALMR), 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.

Paper Structure

This paper contains 83 sections, 12 equations, 13 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Motivation
  3. Thesis Contributions and Outline
  4. Background
  5. Knowledge Representation and Reasoning
  6. XSB Prolog
  7. Disjunctive Logic Programming
  8. Frame Reasoning
  9. The Event Calculus
  10. Linguistic Resources
  11. FrameNet
  12. BabelNet
  13. The Attempto Project
  14. Attempto Controlled English
  15. Discourse Representation Structure for Attempto Controlled English
  16. ...and 68 more sections

Figures (13)

  • Figure 1: Visualization for SEC-based reasoning
  • Figure 2: Underlying dependency information in the DRS of "Mary buys a beautiful car"
  • Figure 3: The frameworks of the KALM system
  • Figure 4: Dependency graph for DRS (\ref{['code:drs']})
  • Figure 5: $score^{R}$ computation for role-name/role-filler pair (Recipient, friend) from candidate parse (\ref{['code:parse-1']})
  • ...and 8 more figures

Theorems & Definitions (4)

  • definition 3.1
  • definition 3.2
  • definition 4.3
  • definition 4.4