# Analogical Dissimilarity: Definition, Algorithms and Two Experiments in Machine Learning

arXiv: https://arxiv.org/abs/1401.3427 · HTML: https://mlsanity.com/paper/1401.3427/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2014.

## Built on by (most cited first, 15 of 15)

- [BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?](https://mlsanity.com/paper/2105.04949/) (2021)
- [Distilling Relation Embeddings from Pre-trained Language Models](https://mlsanity.com/paper/2110.15705/) (2021)
- [Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning](https://mlsanity.com/paper/2306.16296/) (2023)
- [A Neural Approach for Detecting Morphological Analogies](https://mlsanity.com/paper/2108.03945/) (2021)
- [Galois theory for analogical classifiers](https://mlsanity.com/paper/2205.04593/) (2022)
- [On the Transferability of Neural Models of Morphological Analogies](https://mlsanity.com/paper/2108.03938/) (2021)
- [Solving morphological analogies: from retrieval to generation](https://mlsanity.com/paper/2303.18062/) (2023)
- [Tackling Morphological Analogies Using Deep Learning -- Extended Version](https://mlsanity.com/paper/2111.05147/) (2021)
- [Analogical proportions in monounary algebras](https://mlsanity.com/paper/2208.06829/) (2022)
- [Generalizing Analogical Inference from Boolean to Continuous Domains](https://mlsanity.com/paper/2511.10416/) (2025)
- [Some recent advances in reasoning based on analogical proportions](https://mlsanity.com/paper/2212.11717/) (2022)
- [Analogical Proportions and Creativity: A Preliminary Study](https://mlsanity.com/paper/2310.13500/) (2023)
- [KGPrune: a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning](https://mlsanity.com/paper/2408.14658/) (2024)
- [From numerical proportions to analogical proportions between probabilities](https://mlsanity.com/paper/2606.23029/) (2026)
- [Proportional Analogies on Probability Distributions via Bayesian Updating](https://mlsanity.com/paper/2608.11724/) (2026)

## Abstract (arXiv)

This paper defines the notion of analogical dissimilarity between four objects, with a special focus on objects structured as sequences. Firstly, it studies the case where the four objects have a null analogical dissimilarity, i.e. are in analogical proportion. Secondly, when one of these objects is unknown, it gives algorithms to compute it. Thirdly, it tackles the problem of defining analogical dissimilarity, which is a measure of how far four objects are from being in analogical proportion. In particular, when objects are sequences, it gives a definition and an algorithm based on an optimal alignment of the four sequences. It gives also learning algorithms, i.e. methods to find the triple of objects in a learning sample which has the least analogical dissimilarity with a given object. Two practical experiments are described: the first is a classification problem on benchmarks of binary and nominal data, the second shows how the generation of sequences by solving analogical equations enables a handwritten character recognition system to rapidly be adapted to a new writer.