Collective Entity Alignment via Adaptive Features
This addresses the challenge of integrating heterogeneous knowledge graphs for applications like KG construction, though it appears incremental by building on existing features and matching algorithms.
The paper tackles the problem of entity alignment in knowledge graphs by proposing a collective framework that considers interdependence between entities, achieving state-of-the-art results on cross-lingual and mono-lingual benchmarks.
Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration. When generating EA results, current solutions treat entities independently and fail to take into account the interdependence between entities. To fill this gap, we propose a collective EA framework. We first employ three representative features, i.e., structural, semantic and string signals, which are adapted to capture different aspects of the similarity between entities in heterogeneous KGs. In order to make collective EA decisions, we formulate EA as the classical stable matching problem, which is further effectively solved by deferred acceptance algorithm. Our proposal is evaluated on both cross-lingual and mono-lingual EA benchmarks against state-of-the-art solutions, and the empirical results verify its effectiveness and superiority.