ActiveEA: Active Learning for Neural Entity Alignment

TL;DR

ActiveEA introduces an active learning framework for neural entity alignment that reduces annotation costs by intelligently selecting highly informative seed alignments, leveraging entity dependencies and recognizing unmatched entities.

Contribution

This paper presents a novel structure-aware active learning strategy for neural entity alignment, addressing entity dependency exploitation and bachelor entity recognition, which improves efficiency and effectiveness.

Findings

Significantly improves sampling quality across datasets.

Reduces annotation costs for entity alignment.

Enhances model performance with fewer seed alignments.

Abstract

Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion. Current mainstream methods -- neural EA models -- rely on training with seed alignment, i.e., a set of pre-aligned entity pairs which are very costly to annotate. In this paper, we devise a novel Active Learning (AL) framework for neural EA, aiming to create highly informative seed alignment to obtain more effective EA models with less annotation cost. Our framework tackles two main challenges encountered when applying AL to EA: (1) How to exploit dependencies between entities within the AL strategy. Most AL strategies assume that the data instances to sample are independent and identically distributed. However, entities in KGs are related. To address this challenge, we propose a structure-aware uncertainty sampling strategy that can measure the uncertainty of each entity as well as its impact on its neighbour entities in the KG. (2) How to recognise entities that appear in one KG but not in the other KG (i.e., bachelors). Identifying bachelors would likely save annotation budget. To address this challenge, we devise a bachelor recognizer paying attention to alleviate the effect of sampling bias. Empirical results show that our proposed AL strategy can significantly improve sampling quality with good generality across different datasets, EA models and amount of bachelors.