Document Type
Article
Publication Date
3-21-2026
Abstract
Analogical reasoning is an increasingly popular, lightweight solution to enable large language model (LLM)-level reasoning without computational complexity. Still, it has yet to be adopted due to its reliance on strictly hand-formatted data. Therefore, we propose Analogy2KG (“Analogy to Knowledge Graph”), as an automatic pipeline that transforms text into a KG format via a fine-tuned version of information extraction (IE) algorithms for long-text analogies. The need to verify that the complex underlying analogical structure of the data is maintained was done via paired samples tests in the creation and validation of this pipeline. Graph density was used to evaluate the structural quality of the resulting KGs. Lastly, causal relationships were optionally detected using a novel, question-and-answer-based method. Analogy2KG was validated on the Rattermann and Wharton long-text datasets, which suggested that the proposed methodology maintains analogical structure when transforming from text to KGs. The resulting RattermannKG and WhartonKG datasets were introduced to the literature, which is the first instance of a conversion of long-text analogy dataset into a KG format in the literature. Finally, Analogy2KG had superior performance among three LLM-enabled information extraction algorithms: ChatIE, Code4UIE, and InstructUIE for maintaining analogical structure, despite operating without the need for an LLM backend and a pre-defined relation extractor list; thus, making it an ideal lightweight solution.
Source Publication
Knowledge-Based Systems (ISSN 0950-7051 | eISSN 1872-7409)
Recommended Citation
Combs, K., Champagne, L., Cox, B., Schubert Kabban, C., Bihl, T., & Lemming, G. (2026). Analogy2kg: An automatic pipeline for deriving knowledge graphs from long-text analogies. Knowledge-Based Systems, 341, 115772. https://doi.org/10.1016/j.knosys.2026.115772
Comments
© 2026 The Authors.
This article is published by Elsevier, licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Sourced from the published version of record, as cited below.
Co-author K. Combs was completing a AFIT doctoral program at the time of this article's publication date. (AFIT-ENS-DS-26-M-093, March 2026).