INK: Knowledge graph representation for efficient and performant rule mining

Tracking #: 3147-4361

This paper is currently under review
Authors: 
Bram Steenwinckel
Filip De Turck
Femke Ongenae

Responsible editor: 
Guest Editors NeSy 2022

Submission type: 
Full Paper
Abstract: 
Semantic rule mining can be used for both deriving task-agnostic or task-specific information within a Knowledge Graph (KG). Underlying logical inferences to summarise the KG or fully interpretable binary classifiers predicting future events are common results of such a rule mining process. The current methods to perform task-agnostic or task-specific semantic rule mining operate, however, a completely different KG representation, making them not suitable to perform both tasks or incorporate each other's optimizations. This also results in the need to master multiple techniques for both exploring and mining rules within KGs, as well losing time and resources when converting one KG format into another. In this paper, we present INK, a KG representation based on neighbourhood nodes of interest for improved decision support. By selecting one or two sets of nodes of interest, the designed rule miner on top of this INK representation will either mine task-agnostic or task-specific rules. In both subfields, the INK miner outperforms the currently state-of-the-art semantic rule miners on 14 different benchmark datasets within multiple domains.
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Under Review