Knowing the No-match: Entity Alignment with Dangling Cases (2021)

Abstract: This paper studies a new problem setting of entity alignment for knowledge graphs (KGs). Since KGs possess different sets of entities, there could be entities that cannot find alignment across them, leading to the problem of dangling entities. As the first attempt to this problem, we construct a new dataset and design a multi-task learning framework for both entity alignment and dangling entity detection. The framework can opt to abstain from predicting alignment for the detected dangling entities. We propose three techniques for dangling entity detection that are based on the distribution of nearest-neighbor distances, i.e., nearest neighbor classification, marginal ranking and background ranking. After detecting and removing dangling entities, an incorporated entity alignment model in our framework can provide more robust alignment for remaining entities. Comprehensive experiments and analyses demonstrate the effectiveness of our framework. We further discover that the dangling entity detection module can, in turn, improve alignment learning and the final performance. The contributed resource is publicly available to foster further research.

## introduction knowledge graphs (kgs) have evolved to be the building blocks of many intelligent systems @xcite . despite the importance, kgs are usually costly to construct @xcite and naturally suffer from incompleteness @xcite . hence, merging multiple kgs through entity alignment can lead to mutual enrichment of their knowledge @xcite , and provide downstream applications with more comprehensive knowledge representations @xcite . entity alignment seeks to discover identical entities in different kgs, such as english entity thailand and its french counterpart source kg entities target kg entities dangling entities in the target kg dangling entities in the source kg thaïlande. to tackle this important problem, literature has attempted with the embedding-based entity alignment methods @xcite @xcite @xcite @xcite . these methods jointly embed different kgs and put similar entities at close positions in a vector space, where the nearest neighbor search can retrieve entity alignment. due to its effectiveness, embedding-based entity alignment has drawn extensive attention in recent years @xcite . nonetheless, to practically support the alignment of kgs as a real-world task, existing studies suffer one common problem of identifying entities without alignment across kgs (called dangling entities). specifically, current methods are all built upon the assumption that any source entity has a counterpart in the target kg @xcite , and are accordingly developed with learning resources that enforce the same assumption. hence, given every entity in a source kg, a model always tends to predict a counterpart via the nearest neighbor search in the embedding space. however, since each kg may be independently created based on separate corpora @xcite or contributed by different crowds @xcite , it is natural for kgs to possess different sets of entities @xcite , as illustrated in fig. 1 . essentially, this problem overlooked in prior studies causes existing methods to fall short of distinguishing between matchable and dangling entities, hence hinders any of such methods to align kgs in a real-world scenario. towards more practical solutions of entity alignment for kgs, we provide a redefinition of the task with the incorporation of dangling cases ( §2.1), as the first contribution of this work. given a source entity, our setting does not assume that it must have a counterpart in the target kg as what previous studies do. instead, conducting entity alignment also involves identifying whether the counterpart of an entity actually exists in another kg. hence, a system to tackle this realistic problem setting of entity alignment is also challenged by the requirement for justifying the validity of its prediction. to facilitate the research towards the new problem, the second contribution of this work is to construct a new dataset dbp2.0 for entity alignment with dangling cases ( §2.2). as being discussed, existing benchmarks for entity alignment, including dbp15k @xcite , wk3l @xcite and the more recent openea @xcite , are set with the constraint that any entity to be aligned should have a valid counterpart. we use the full dbpedia @xcite to build a new dataset and the key challenge lies in that we need to guarantee the selected dangling entities actually do not have counterparts. we first extract two subgraphs with one-to-one entity alignment (i.e., all entities have counterparts). then, we randomly remove some entities to make their left counterparts in the peer kg dangling. although embedding-based entity alignment has been investigated for several years, handling with dangling entities has not been studied yet. as the third contribution, we present a multi-task learning framework for the proposed task ( §3). it consists of two jointly optimized modules for entity alignment and dangling entity detection, respectively. while the entity alignment module can basically incorporate any existing techniques from prior studies @xcite , in this paper, we experiment with two representative techniques, i.e., relational embedding based @xcite and neighborhood aggregation based @xcite ) methods. for dangling entity detection, our framework incorporates an auxiliary learning objective, which seeks to learn a confidence metric for the inferred entity alignment. the principle to realize such metric learning is that the embeddings of dangling entities should be isolated and are distant from others. according to this principle, we exploit several techniques to distinguish between matchable and dangling entities based on their distance distribution with their neighbors ( §3), including nearest neighbor classification, marginal ranking and background ranking @xcite . we conduct comprehensive experiments on the new dbp2.0 dataset, which demonstrate the proposed techniques to solve the dangling entity detection problem to different extents. moreover, we observe that training the dangling detection model (marginal ranking) provides an effective indirect supervision that improves the detection of alignment for matchable entities. we hope our task, dataset and framework can foster further investigation of entity alignment techniques in the suggested real scenario, leading to more effective and practical solutions to this challenging but important problem. ## task and dataset we hereby describe the problem setting of our task and introduce the new dataset. ## entity alignment with dangling cases we propose a multi-task learning framework for entity alignment with dangling cases, as illustrated in fig. 2 . it has two jointly optimized modules, i.e., entity alignment and dangling entity detection. the entity alignment module takes as input relational triples of two kgs (for kg embedding) and seed entity alignment (for alignment learning). as for the detection of dangling entities, the module uses a small number of labeled dangling entities to jump-start the learning of a confidence metric for distinguishing between matchable and dangling entities. in the inference stage for entity alignment, our framework is able to first identify and remove dangling entities, then predict alignment for those that are decided to be matchable. ## experiments in this section, we report our experimental results. we start with describing the experimental setups ( §4.1). next, we separately present the experimentation under two different evaluation settings ( §4.2- §4.3), followed by an analysis on the similarity score distribution of the obtained representations for matchable and dangling entities ( §4.4). to faciliate the use of the contributed dataset and software, we have incorporated these resources into the ope-nea benchmark 3 @xcite . ## conclusion and future work in this paper, we propose and study a new entity alignment task with dangling cases. we construct a dataset to support the study of the proposed problem setting, and design a multi-learning framework for both entity alignment and dangling entity detection. three types of dangling detection techniques are studied, which are based on nearest neighbor classification, marginal ranking, and background ranking. comprehensive experiments demonstrate the effectiveness of the method, and provide insights to foster further investigation on this new problem. we further find that dangling entity detection can, in turn, effectively provide auxiliary supervision signals to improve the performance of entity alignment. for future work, we plan to extend the benchmarking on dbp2.0 with results from more base models of entity alignment as well as more abstention inference techniques. extending our framework to support more prediction tasks with abstention, such as entity type inference @xcite and relation extraction @xcite , is another direction with potentially broad impact.