Dangling-Aware Entity Alignment with Mixed High-Order Proximities (2022)

Abstract: We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of entities, a KG commonly contains some dangling entities that cannot find counterparts in other KGs. Therefore, dangling-aware entity alignment is more realistic than the conventional entity alignment where prior studies simply ignore dangling entities. We propose a framework using mixed high-order proximities on dangling-aware entity alignment. Our framework utilizes both the local high-order proximity in a nearest neighbor subgraph and the global high-order proximity in an embedding space for both dangling detection and entity alignment. Extensive experiments with two evaluation settings shows that our method more precisely detects dangling entities, and better aligns matchable entities. Further investigations demonstrate that our framework can mitigate the hubness problem on dangling-aware entity alignment.

## introduction knowledge graphs (kgs) have become the backbone of many intelligent applications @xcite . in spite of their importance, many kgs are independently created without considering the interrelated and interchangeable nature of individually created knowledge @xcite . to allow complementary knowledge to be automatically combined and migrated across individual kgs, entity alignment seeks to identify equivalent entities in distinct kgs @xcite . recent literature has focused on learning embedding representations of multiple kgs where identical entities are aligned based on their embedding similarity @xcite @xcite @xcite . aside from the surge of research effort on entity alignment @xcite , an unresolved but important challenge that existing methods face is the dangling entity problem. dangling entities are those unique entities in a kg that cannot find counterparts in another kg. considering that individually created kgs are unlikely to share the same set of entities, identifying dangling entities is undoubtedly an indispensable step of any practical solution to entity alignment. however, nearly all prior studies have neglected dangling entities and assume there must be one-to-one entity mapping from the source kg to the target one @xcite . this assumption prevents prior methods from practically supporting the alignment between kgs in real-world scenarios. to fill the gap, @xcite formally define a more practical problem setting where a model needs to both determine whether each given source entity is a matchable one, as well as retrieve counterparts for the predicted matchable entities. although some preliminary attempts have been made to implement dangling-aware entity alignment @xcite , the attempted methods still suffer from a major drawback, i.e., they only consider the first-order proximity (namely, pairwise cosine similarity) between source and target entities. however, to effectively discover dangling entities as outliers in the embedding representation, we argue that high-order proximity measures should also be involved. fig. 1a shows the intuition of using the high-order proximity for dangling entity detection. despite a fairly high cosine similarity, the source entity 4 is not the nearest neighbor of target entity b, indicating that 4 is likely to be dangling. in contrast, 1 and a are mutual nearest neighbors even with a relatively low similarity, indicating that 1 is more likely to be matchable. hence, in addition to the first-order proximity from source to target, the local high-order proximity (e.g., the second-order proximity foot_0 in the nearest neighbor subgraph) is also useful for detecting dangling entities. in alignment learning, the previous works neglect global information since they merely optimize the entity-level alignment loss on labeled alignments without considering entity embedding distributions as shown in fig. 1b . in fig. 1c , we show that a desirable dangling-aware model should align the global distributions of matchable source and target entities (i.e., global high-order proximity in an embedding distribution space), such that dangling entities in both kgs could appear as dissimilar outliers in both distributions. motivated by the above intuition, we propose a dangling-aware entity alignment framework based on mixed high-order proximities (mhp). mhp considers both local and global high-order proximities to foster both dangling entity detection and matchable entity alignment. we introduce the optimization of global high-order proximity measure as finding the optimal transport between matchable source entities and target entities. through this optimization process, to facilitate dangling detection, mhp also encourages a large distance between the dangling entity distribution and matchable entity distribution. additionally, to leverage the local high-order proximity, we propose a dangling entity classifier which takes into account the second-order proximity in the nearest neighbor subgraph. furthermore, with the similar principle of local high-order proximity, we adopt an nca (neighborhood component analysis) loss @xcite for alignment learning to mitigate the hubness problem foot_1 @xcite , which is severe in dangling-aware entity alignment as observed in our experiments. our main technical contributions to the studied problem are two-fold. first, the local high-order proximity (i.e., the second-order proximity in the nearest neighbor subgraph) is modeled to facilitate both dangling detection and alignment learning. second, we design the use of the global high-order proximity to align the distributions of matchable entities, therefore precisely separating the representations of dangling entities and matchable ones. in addition, the techniques are model-agnostic and can be incorporated with various alignment methods (e.g., mtranse @xcite or alinet @xcite ) and dangling detection methods (e.g., the marginal or background ranking @xcite ). extensive experiments on dbp2.0 demonstrate its effectiveness and adaptiveness. ## preliminary in this section, we provide the problem definition of dangling-aware entity alignment and briefly introduce previous methods for this problem. ## methodology in this section, we introduce the techniques in our framework which captures both local and global high-order proximities to collaboratively tackle dangling detection and entity alignment. ## experiments in this section, we report our experiments to show the effectiveness of mhp. we describe the evaluation settings in sec. 4.1, and present the results in two alignment settings separately in sec. 4.2 and 4.3. we conduct an ablation study and demonstrate that mhp can mitigate the hubness problem in sec. 4.4, followed by a case study to show the importance of local high-order proximity in sec. 4.5. ## conclusion in this paper, we propose a framework, mhp, with mixed high-order proximities for dangling-aware entity alignment. mhp captures the local highorder proximity via a dangling classifier based on both the first-and second-order proximities. additionally, we propose a optimal transport based method considering the global high-order proximity to facilitate both dangling detection and entity alignment. comprehensive experiments on two alignment settings show the effectiveness of utilizing mixed high-order proximities. furthermore, our extensive ablation study demonstrates the effectiveness of each technique.