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Stay on Topic, Please: Aligning User Comments to the Content of a News Article Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Active Learning for Entity Alignment

verfasst von : Max Berrendorf, Evgeniy Faerman, Volker Tresp

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

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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. In the spirit of reproducible research, we make our code available at https://​github.​com/​mberr/​ea_​active_​learning.

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Vorheriges Kapitel FedeRank: User Controlled Feedback with Federated Recommender Systems
Nächstes Kapitel Exploring Classic and Neural Lexical Translation Models for Information Retrieval: Interpretability, Effectiveness, and Efficiency Benefits
Fußnoten
1
Note that the frequently used DBP15k dataset is not suitable for our experiments due to its construction. Exclusive nodes in DBP15K are exactly those having a degree of one and are therefore trivial to identify.
 
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Metadaten
Titel
Active Learning for Entity Alignment
verfasst von
Max Berrendorf
Evgeniy Faerman
Volker Tresp
Copyright-Jahr
2021
Buch
Advances in Information Retrieval

Print ISBN: 978-3-030-72112-1

Electronic ISBN: 978-3-030-72113-8

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-72113-8_4

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