INK: knowledge graph embeddings for node classification

Deep learning techniques are increasingly being applied to solve various machine learning tasks that use Knowledge Graphs as input data. However, these techniques typically learn a latent representation for the entities of interest internally, which is then used to make decisions. This latent repres...

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Bibliographic Details
Published in:Data mining and knowledge discovery 2022-03, Vol.36 (2), p.620-667
Main Authors: Steenwinckel, Bram, Vandewiele, Gilles, Weyns, Michael, Agozzino, Terencio, Turck, Filip De, Ongenae, Femke
Format: Article
Language:English
Subjects:
Artificial Intelligence
Chemistry and Earth Sciences
Classification
Cognitive tasks
Computer Science
Data Mining and Knowledge Discovery
Deep learning
Information Storage and Retrieval
Knowledge representation
Machine learning
Nodes
Performance prediction
Physics
Special Issue of the Journal Track of ECML PKDD 2022
Statistics for Engineering
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Summary:Deep learning techniques are increasingly being applied to solve various machine learning tasks that use Knowledge Graphs as input data. However, these techniques typically learn a latent representation for the entities of interest internally, which is then used to make decisions. This latent representation is often not comprehensible to humans, which is why deep learning techniques are often considered to be black boxes. In this paper, we present INK: Instance Neighbouring by using Knowledge, a novel technique to learn binary feature-based representations, which are comprehensible to humans, for nodes of interest in a knowledge graph. We demonstrate the predictive power of the node representations obtained through INK by feeding them to classical machine learning techniques and comparing their predictive performances for the node classification task to the current state of the art: Graph Convolutional Networks (R-GCN) and RDF2Vec. We perform this comparison both on benchmark datasets and using a real-world use case.
ISSN:1384-5810
1573-756X
DOI:10.1007/s10618-021-00806-z