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Authors: Daniel Obraczka and Erhard Rahm

Affiliation: ScaDS.AI/Database Group, Leipzig University, Germany

Keyword(s): Hubness Reduction, Nearest Neighbor Search, Knowledge Graph Embedding, Entity Alignment.

Abstract: The heterogeneity of Knowledge Graphs is problematic for conventional data integration frameworks. A possible solution to this issue is using Knowledge Graph Embeddings (KGEs) to encode entities into a lower-dimensional embedding space. However, recent findings suggest that KGEs suffer from the so-called hubness phenomenon. A dataset that suffers from hubness has a few popular entities that are nearest neighbors of a highly disproportionate amount of other entities. Because the calculation of nearest neighbors is an integral part of entity alignment with KGEs, hubness reduces the accuracy of the matching result. We therefore investigate a variety of hubness reduction techniques and utilize approximate nearest neighbor (ANN) approaches to offset the increase in time complexity stemming from the hubness reduction. Our results suggest, that hubness reduction in combination with ANN techniques improves the quality of nearest neighbor results significantly compared to using no hubness red uction and exact nearest neighbor approaches. Furthermore, this advantage comes without losing the speed advantage of ANNs on large datasets. (More)

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Paper citation in several formats:
Obraczka, D. and Rahm, E. (2021). An Evaluation of Hubness Reduction Methods for Entity Alignment with Knowledge Graph Embeddings. In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KEOD; ISBN 978-989-758-533-3; ISSN 2184-3228, SciTePress, pages 28-39. DOI: 10.5220/0010646400003064

@conference{keod21,
author={Daniel Obraczka. and Erhard Rahm.},
title={An Evaluation of Hubness Reduction Methods for Entity Alignment with Knowledge Graph Embeddings},
booktitle={Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KEOD},
year={2021},
pages={28-39},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010646400003064},
isbn={978-989-758-533-3},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KEOD
TI - An Evaluation of Hubness Reduction Methods for Entity Alignment with Knowledge Graph Embeddings
SN - 978-989-758-533-3
IS - 2184-3228
AU - Obraczka, D.
AU - Rahm, E.
PY - 2021
SP - 28
EP - 39
DO - 10.5220/0010646400003064
PB - SciTePress

- Science and Technology Publications, Lda.
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