INK: Knowledge graph representation for efficient and performant rule mining

Tracking #: 3495-4709

Bram Steenwinckel
Filip De Turck
Femke Ongenae

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Guest Editors NeSy 2022

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Full Paper
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 less 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 use INK, a KG representation based on neighbourhood nodes of interest to mine rules for improved decision support. By selecting one or two sets of nodes of interest, the rule miner created on top of the INK representation will either mine task-agnostic or task- specific rules. In both subfields, the INK miner is competitive to the currently state-of-the-art semantic rule miners on 14 different benchmark datasets within multiple domains.
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Anonymous submitted on 17/Jul/2023
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Review #2
Anonymous submitted on 18/Jul/2023
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The revisions you have implemented have significantly fortified the paper, resulting in enhanced clarity, improved methodology, and a more comprehensive evaluation. The refined manuscript now adeptly elucidates the underlying idea and effectively assesses its implications.