Learning Logical Definitions of n-Ary Relations in Graph Databases

Göz, Furkan; Mutlu, Alev

doi:10.1007/978-3-319-92639-1_5

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Algorithm 1: Generic Concept Discovery Process1

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2019

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“…Such studies include crime analysis [21], searching semantically similar subgraphs [22], frequent itemset mining [23]. Applications of graph databases in concept discovery problem have been addressed in [24,25].…”

Section: Algorithm 1: Generic Concept Discovery Processmentioning

confidence: 99%

Guiding Search in Relational Pathfinding-based Concept Discovery via Bivariate Statistical Methods

Mutlu¹,

Göz²

2019

ijisae

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Relational pathfinding-based systems learn concept descriptors by extending candidate concept descriptors by one literal at a time. As such learning systems usually deal with large search spaces, choosing literals to extend candidate concept descriptors becomes an essential issue. In this study we empirically analyze applicability of three bivariate statistical methods namely, frequency ratio, hazard index, and weight of evidence, as heuristics to choose literals to extend candidate concept descriptors. 10-fold experiments conducted on three benchmark datasets showed that frequency ratio, hazard index, and weight of evidence were able to reduce the space and hence provided speedups when compared to extending candidate concept descriptors by a randomly chosen literal. Moreover, the heuristic-based settings provided improved predictive accuracy.

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