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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Learning Logical Definitions of n-Ary Relations in Graph Databases

verfasst von : Furkan Goz, Alev Mutlu

Erschienen in: Hybrid Artificial Intelligent Systems

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Inferring User Expertise from Social Tagging in Music Recommender Systems for Streaming Services
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Metadaten
Titel
Learning Logical Definitions of n-Ary Relations in Graph Databases
verfasst von
Furkan Goz
Alev Mutlu
Copyright-Jahr
2018
Buch
Hybrid Artificial Intelligent Systems

Print ISBN: 978-3-319-92638-4

Electronic ISBN: 978-3-319-92639-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-92639-1_5

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