%0 Journal Article
%T Concepts of Neighbors and their Application to Instance-based Learning on Relational Data
%+ Large Scale Collaborative Data Mining (LACODAM)
%+ Institut National des Sciences Appliquées - Rennes (INSA Rennes)
%A Ayats, H. Ambre
%A Cellier, Peggy
%A Ferré, Sébastien
%< avec comité de lecture
%@ 0888-613X
%J International Journal of Approximate Reasoning
%I Elsevier
%V 164
%P 1-49
%8 2023-10
%D 2023
%R 10.1016/j.ijar.2023.109059
%K Relational Data
%K Knowledge Graph
%K Instance-Based Learning
%K Formal Concept Analysis
%K Graph-FCA
%K Concepts of Neighbors
%Z Computer Science [cs]/Artificial Intelligence [cs.AI]Journal articles
%X Knowledge graphs and other forms of relational data have become awidespread kind of data, and powerful methods to analyze and learn fromthem are needed. Formal Concept Analysis (FCA) is a mathematical frameworkfor the analysis of symbolic datasets, which has been extended to graphsand relational data, like Graph-FCA. It encompasses various tasks such aspattern mining or machine learning, but its application generally relies onthe computation of a concept lattice whose size can be exponential withthe number of instances. We propose to follow an instance-based approachwhere the learning effort is delayed until a new instance comes in, and aninference task is set. This is the approach adopted in k-Nearest Neighbors,and this relies on a distance between instances. We define a conceptualdistance based on FCA concepts, and from there the notion of concepts ofneighbors, which can be used as a basis for instance-based reasoning. Thosedefinitions 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.
%G English
%2 https://inria.hal.science/hal-04246864v1/document
%2 https://inria.hal.science/hal-04246864v1/file/IJAR___Concepts_of_Neighbors-1.pdf
%L hal-04246864
%U https://inria.hal.science/hal-04246864
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