Knowledge graph embedding for data mining vs. knowledge graph embedding for link prediction – two sides of the same coin?


Portisch, Jan ; Heist, Nicolas ; Paulheim, Heiko



DOI: https://doi.org/10.3233/SW-212892
URL: https://content.iospress.com/articles/semantic-web...
Document Type: Article
Year of publication: 2022
The title of a journal, publication series: Semantic Web
Volume: tba
Issue number: tba
Page range: 1-24
Place of publication: Amsterdam
Publishing house: IOS Press
ISSN: 1570-0844 , 2210-4968
Publication language: English
Institution: School of Business Informatics and Mathematics > Web Data Mining (Paulheim 2018-)
Subject: 004 Computer science, internet
Abstract: Knowledge Graph Embeddings, i.e., projections of entities and relations to lower dimensional spaces, have been proposed for two purposes: (1) providing an encoding for data mining tasks, and (2) predicting links in a knowledge graph. Both lines of research have been pursued rather in isolation from each other so far, each with their own benchmarks and evaluation methodologies. In this paper, we argue that both tasks are actually related, and we show that the first family of approaches can also be used for the second task and vice versa. In two series of experiments, we provide a comparison of both families of approaches on both tasks, which, to the best of our knowledge, has not been done so far. Furthermore, we discuss the differences in the similarity functions evoked by the different embedding approaches.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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