Abstract
Given a set of facts and related background knowledge, it has always been a challenging task to learn theories that define the facts in terms of background knowledge. In this study, we focus on graph databases and propose a method to learn definitions of n-ary relations stored in such mediums. The proposed method distinguishes from state-of-the-art methods as it employs hypergraphs to represent relational data and follows substructure matching approach to discover concept descriptors. Moreover, the proposed method provides mechanisms to handle inexact substructure matching, incorporate numerical attributes into concept discovery process, avoid target instance ordering problem and concept descriptors suppress each other. Experiments conducted on two benchmark biochemical datasets show that the proposed method is capable of inducing concept descriptors that cover all the target instances and are similar to those induced by state-of-the-art methods.
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Goz, F., Mutlu, A. (2018). Learning Logical Definitions of n-Ary Relations in Graph Databases. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_5
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