# ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base

arXiv: https://arxiv.org/abs/2305.05994 · HTML: https://mlsanity.com/paper/2305.05994/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 5 of its 52 references that are in the map.
- First posted on arXiv 10 May 2023.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 5 of 5)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT](https://mlsanity.com/paper/2210.04186/) (2022)
- [Multimodal Analogical Reasoning over Knowledge Graphs](https://mlsanity.com/paper/2210.00312/) (2022)
- [Scientific and Creative Analogies in Pretrained Language Models](https://mlsanity.com/paper/2211.15268/) (2022)

## Built on by (most cited first, 16 of 16)

- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models](https://mlsanity.com/paper/2310.03965/) (2023)
- [AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies](https://mlsanity.com/paper/2402.12370/) (2024)
- [Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction](https://mlsanity.com/paper/2305.12660/) (2023)
- [Graph Agent: Explicit Reasoning Agent for Graphs](https://mlsanity.com/paper/2310.16421/) (2023)
- [ParallelPARC: A Scalable Pipeline for Generating Natural-Language Analogies](https://mlsanity.com/paper/2403.01139/) (2024)
- [DeFine: Decision-Making with Analogical Reasoning over Factor Profiles](https://mlsanity.com/paper/2410.01772/) (2024)
- [Boosting Scientific Concepts Understanding: Can Analogy from Teacher Models Empower Student Models?](https://mlsanity.com/paper/2406.11375/) (2024)
- [On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models](https://mlsanity.com/paper/2310.07818/) (2023)
- [Pairing Analogy-Augmented Generation with Procedural Memory for Procedural Q&A](https://mlsanity.com/paper/2409.01344/) (2024)
- [Past Meets Present: Creating Historical Analogy with Large Language Models](https://mlsanity.com/paper/2409.14820/) (2024)
- [KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting](https://mlsanity.com/paper/2412.00869/) (2024)
- [Can Large Language Models Generalize Procedures Across Representations?](https://mlsanity.com/paper/2602.03542/) (2026)
- [Solving Hard Analogy Questions with Relation Embedding Chains](https://mlsanity.com/paper/2310.12379/) (2023)
- [Teaching Through Analogies: A Modular Pipeline for Educational Analogy Generation](https://mlsanity.com/paper/2605.24211/) (2026)
- [Analogical Deep Research: Retrieving and Integrating Historical Analogies for Foresight Analysis](https://mlsanity.com/paper/2607.13602/) (2026)

## Abstract (arXiv)

Analogical reasoning is a fundamental cognitive ability of humans. However, current language models (LMs) still struggle to achieve human-like performance in analogical reasoning tasks due to a lack of resources for model training. In this work, we address this gap by proposing ANALOGYKB, a million-scale analogy knowledge base (KB) derived from existing knowledge graphs (KGs). ANALOGYKB identifies two types of analogies from the KGs: 1) analogies of the same relations, which can be directly extracted from the KGs, and 2) analogies of analogous relations, which are identified with a selection and filtering pipeline enabled by large language models (LLMs), followed by minor human efforts for data quality control. Evaluations on a series of datasets of two analogical reasoning tasks (analogy recognition and generation) demonstrate that ANALOGYKB successfully enables both smaller LMs and LLMs to gain better analogical reasoning capabilities.