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 labeling 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 BFT21

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ECIR 2021

43rd European Conference on Information Retrieval. Virtual, Mar 28-Apr 01, 2021.

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Authors

M. Berrendorf • E. Faerman • V. Tresp

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Links

DOI GitHub

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In Collaboration

 Siemens AG

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Research Area

 A3 | Computational Models