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Concept Discovery in Graph Databases

A Case Study with Neo4j
  • Furkan Goz
  • Alev MutluEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10334)

Abstract

Concept discovery is one of the most commonly addressed tasks of multi-relational data mining and is concerned with inducing logical definitions of a relation in terms of other relations provided. The problem has long been studied from Inductive Logic Programming and graph-oriented perspectives. In this study, we investigate the problem from graph databases perspective and propose a pathfinding-based method for concept discovery in graph databases. More specifically, we introduce a method that employs Neo4j graph database technology to store data and find the concept descriptors that define the target relation and implements several techniques to further improve the post processing steps of concept discovery process. The experimental results show that the proposed method is superior to state-of-the art concept discovery systems in terms of rule induction time, discovers shorter concept descriptors with high coverage and F1 score, and scales well.

Keywords

Concept discovery Path finding Graph database Neo4j 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer EngineeringKocaeli UniversityKocaeliTurkey

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