Towards Utilizing Knowledge Graph Embedding Models for Conceptual Clustering

Mohamed H. Gad-Elrab, Vinh Thinh Ho, Evgeny Levinkov, Trung-Kien Tran, Daria Stepanova
2020 International Semantic Web Conference  
We propose a framework to utilize Knowledge Graph (KG) embedding models for conceptual clustering, i.e., the task of clustering a given set of entities in a KG based on the quality of the resulting descriptions for the clusters. Specifically, prominent regions in the embedding space are detected using Multicut clustering algorithm, and then the queries describing/covering the entities within these regions are obtained by rule learning. Finally, we evaluate these queries using different metrics.
more » ... In our preliminary experiments, we compare the suitability of well-known KG embedding models for conceptual clustering. The reported results provide insights for the capability of these embeddings to capture graph topology and their applicability for data mining tasks beyond link prediction. Recent advances in (deep) representation learning on KGs have proved to be effective for specialized tasks such as KG completion [16] and conjunctive query (CQ) answering [8, 13, 6] . In particular, in [13] queries are embedded as boxes/hyper-rectangles in the embedding space, where interior points of these boxes correspond to the set of query's answers.
dblp:conf/semweb/Gad-ElrabHLTS20 fatcat:3idrwcvctrgrvmf5f3xmwaxrvm