# Knowledge Authoring for Rules and Actions

arXiv: https://arxiv.org/abs/2305.07763 · HTML: https://mlsanity.com/paper/2305.07763/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 7 of 7)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks](https://mlsanity.com/paper/1502.05698/) (2015)
- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold](https://mlsanity.com/paper/1706.09528/) (2017)
- [Self-Attentive Associative Memory](https://mlsanity.com/paper/2002.03519/) (2020)
- [SLING: A framework for frame semantic parsing](https://mlsanity.com/paper/1710.07032/) (2017)
- [Knowledge Authoring with Factual English](https://mlsanity.com/paper/2208.03094/) (2022)

## Built on by (most cited first, 2 of 2)

- [Fast and Robust Contextual Node Representation Learning over Dynamic Graphs](https://mlsanity.com/paper/2411.07123/) (2024)
- [LP-LM: No Hallucinations in Question Answering with Logic Programming](https://mlsanity.com/paper/2502.09212/) (2025)

## Abstract (arXiv)

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.