EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs
Xiaobin Tian, Zequn Sun, Guangyao Li, Wei Hu

TL;DR
This paper critically evaluates existing embedding-based entity alignment methods for event-centric knowledge graphs, revealing their limitations and introducing a new dataset and a time-aware literal encoder to improve performance.
Contribution
It identifies biases in current datasets, creates a more challenging heterogeneous dataset, and proposes a novel time-aware literal encoder for better entity alignment.
Findings
Existing methods perform poorly on the new dataset.
Biases in previous datasets favor certain techniques.
The proposed encoder shows improved results.
Abstract
Entity alignment is to find identical entities in different knowledge graphs (KGs) that refer to the same real-world object. Embedding-based entity alignment techniques have been drawing a lot of attention recently because they can help solve the issue of symbolic heterogeneity in different KGs. However, in this paper, we show that the progress made in the past was due to biased and unchallenging evaluation. We highlight two major flaws in existing datasets that favor embedding-based entity alignment techniques, i.e., the isomorphic graph structures in relation triples and the weak heterogeneity in attribute triples. Towards a critical evaluation of embedding-based entity alignment methods, we construct a new dataset with heterogeneous relations and attributes based on event-centric KGs. We conduct extensive experiments to evaluate existing popular methods, and find that they fail to…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Data Quality and Management · Topic Modeling
Methodsfail
