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2018 | OriginalPaper | Chapter

Fine-Grained Evaluation of Rule- and Embedding-Based Systems for Knowledge Graph Completion

Authors : Christian Meilicke, Manuel Fink, Yanjie Wang, Daniel Ruffinelli, Rainer Gemulla, Heiner Stuckenschmidt

Published in: The Semantic Web – ISWC 2018

Publisher: Springer International Publishing

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Abstract

Over the recent years, embedding methods have attracted increasing focus as a means for knowledge graph completion. Similarly, rule-based systems have been studied for this task in the past. What is missing so far is a common evaluation that includes more than one type of method. We close this gap by comparing representatives of both types of systems in a frequently used evaluation protocol. Leveraging the explanatory qualities of rule-based systems, we present a fine-grained evaluation that gives insight into characteristics of the most popular datasets and points out the different strengths and shortcomings of the examined approaches. Our results show that models such as TransE, RESCAL or HolE have problems in solving certain types of completion tasks that can be solved by a rule-based approach with high precision. At the same time, there are other completion tasks that are difficult for rule-based systems. Motivated by these insights, we combine both families of approaches via ensemble learning. The results support our assumption that the two methods complement each other in a beneficial way.

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next chapter Aligning Knowledge Base and Document Embedding Models Using Regularized Multi-Task Learning
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Metadata
Title
Fine-Grained Evaluation of Rule- and Embedding-Based Systems for Knowledge Graph Completion
Authors
Christian Meilicke
Manuel Fink
Yanjie Wang
Daniel Ruffinelli
Rainer Gemulla
Heiner Stuckenschmidt
Copyright Year
2018
Book
The Semantic Web – ISWC 2018

Print ISBN: 978-3-030-00670-9

Electronic ISBN: 978-3-030-00671-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00671-6_1

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