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Article Dans Une Revue International Journal of Approximate Reasoning Année : 2023

Concepts of Neighbors and their Application to Instance-based Learning on Relational Data

Résumé

Knowledge graphs and other forms of relational data have become a widespread kind of data, and powerful methods to analyze and learn from them are needed. Formal Concept Analysis (FCA) is a mathematical framework for the analysis of symbolic datasets, which has been extended to graphs and relational data, like Graph-FCA. It encompasses various tasks such as pattern mining or machine learning, but its application generally relies on the computation of a concept lattice whose size can be exponential with the number of instances. We propose to follow an instance-based approach where the learning effort is delayed until a new instance comes in, and an inference task is set. This is the approach adopted in k-Nearest Neighbors, and this relies on a distance between instances. We define a conceptual distance based on FCA concepts, and from there the notion of concepts of neighbors, which can be used as a basis for instance-based reasoning. Those definitions are given for both classical FCA and Graph-FCA. We provide efficient algorithms for computing concepts of neighbors, and we demonstrate their inference capabilities by presenting three different applications: query relaxation, knowledge graph completion, and relation extraction.
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hal-04246864 , version 1 (17-10-2023)

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H. Ambre Ayats, Peggy Cellier, Sébastien Ferré. Concepts of Neighbors and their Application to Instance-based Learning on Relational Data. International Journal of Approximate Reasoning, 2023, 164, pp.1-49. ⟨10.1016/j.ijar.2023.109059⟩. ⟨hal-04246864⟩
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