Active Learning for Entity Alignment

MCML Authors

Max Berrendorf

Dr.

* Former Member

→ Group Volker Tresp
 Database Systems, Data Mining and AI

Evgeny Faerman

Dr.

* Former Member

→ Group Matthias Schubert
 Spatial Artificial Intelligence

Volker Tresp

Prof. Dr.

Core PI

Database Systems, Data Mining and AI

Abstract

In this work, we propose a novel framework for the labeling of entity alignments in knowledge graph datasets. Different strategies to select informative instances for the human labeler build the core of our framework. We illustrate how the labeling of entity alignments is different from assigning class labels to single instances and how these differences affect the labeling efficiency. Based on these considerations we propose and evaluate different active and passive learning strategies. One of our main findings is that passive learning approaches, which can be efficiently precomputed and deployed more easily, achieve performance comparable to the active learning strategies.

inproceedings BFT20b

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DL4G @WWW 2020

5th International Workshop on Deep Learning for Graphs at the International World Wide Web Conference. Taipeh, Taiwan, Apr 21, 2020.

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Authors

M. Berrendorf • E. Faerman • V. Tresp

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Links

arXiv

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In Collaboration

 Siemens AG

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Research Area

 A3 | Computational Models