Human Evaluation of Procedural Knowledge Graph Extraction from Text with Large Language Models
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Abstract (arXiv)
Procedural Knowledge is the know-how expressed in the form of sequences of steps needed to perform some tasks. Procedures are usually described by means of natural language texts, such as recipes or maintenance manuals, possibly spread across different documents and systems, and their interpretation and subsequent execution is often left to the reader. Representing such procedures in a Knowledge Graph (KG) can be the basis to build digital tools to support those users who need to apply or execute them. In this paper, we leverage Large Language Model (LLM) capabilities and propose a prompt engineering approach to extract steps, actions, objects, equipment and temporal information from a textual procedure, in order to populate a Procedural KG according to a pre-defined ontology. We evaluate the KG extraction results by means of a user study, in order to qualitatively and quantitatively assess the perceived quality and usefulness of the LLM-extracted procedural knowledge. We show that LLMs can produce outputs of acceptable quality and we assess the subjective perception of AI by human evaluators.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 28 of its 57 references that are in the map. First posted on arXiv 27 Nov 2024.
Builds on
28 of 57 references.
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16 more
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Built on by
Citing papers per year: 2025: 2; 2026: 2.
4 papers.
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- 2026Learning from Research: Toward Lifelong Agent Harness Evolution30 Sep 2026
- 2026Procedural Knowledge Extraction from Industrial Troubleshooting Guides Using Vision Language Models30 Jan 2026
- 2025Matching Game Preferences Through Dialogical Large Language Models: A Perspective26 Jul 2025
- 2025Mutual Understanding between People and Systems via Neurosymbolic AI and Knowledge Graphs15 Apr 2025