An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection (2022)

Abstract: Knowledge graph integration typically suffers from the widely existing dangling entities that cannot find alignment cross knowledge graphs (KGs). The dangling entity set is unavailable in most real-world scenarios, and manually mining the entity pairs that consist of entities with the same meaning is labor-consuming. In this paper, we propose a novel accurate Unsupervised method for joint Entity alignment (EA) and Dangling entity detection (DED), called UED. The UED mines the literal semantic information to generate pseudo entity pairs and globally guided alignment information for EA and then utilizes the EA results to assist the DED. We construct a medical cross-lingual knowledge graph dataset, MedED, providing data for both the EA and DED tasks. Extensive experiments demonstrate that in the EA task, UED achieves EA results comparable to those of state-of-the-art supervised EA baselines and outperforms the current state-of-the-art EA methods by combining supervised EA data. For the DED task, UED obtains high-quality results without supervision.

## introduction entity alignment (ea) that aligns the equivalent entities in different knowledge graphs (kgs) is a fundamental technique for knowledge graph integration. a typical application of ea is constructing a large-scale kg by integrating different kgs to facilitate various downstream tasks such as question answering @xcite @xcite , recommendation @xcite , and search engines @xcite . the existing embedding-based ea methods align each entity to its closest counterpart cross kgs according to entity embeddings. in recent years, they have emerged as the dominant ea solutions due to their effectiveness and strong ability to utilize information such as entity name strings, entity description, attributes, and graph structure. these ea methods @xcite @xcite @xcite are built upon the assumption that there exists a counterpart in the target kg for any source entity @xcite . therefore, ideally, their performances are assessed by only considering the entities in the set of testing entity pairs. in the real-world scenario, four facts should be considered when aligning kgs: (1) the entities that do not have counterparts in another kg are ubiquitous. these entities are referred to as dangling entities, following @xcite . therefore, it is necessary to identify the dangling entities and then align the remaining matchable entities to their counterparts. the widely used approach of integrating kgs according to the cross kg similarity between entities loses sight of identifying dangling entities. (2) dangling entity sets are not labeled in most cases, while some entity pairs are relatively available but labor-consuming. for example, we can preliminarily obtain pseudo entity pairs with high similarity according to extra information to align entities and then manually extract the correct pairs. the extra information could be cross kg links or literal semantic information from machine translation or word embeddings. however, identifying a dangling entity requires manual comparisons between an entity and all entities in the target kg, which is tedious and almost impossible for large kgs. dangling entity detection (ded) methods need to avoid reliance on supervision. (3) literal semantic information has an essential impact on ea. as shown in previous works @xcite @xcite , competitive ea results can be achieved by translating entity names to the same language and calculating the vector representation from glove @xcite , suggesting that it is possible to get rid of manually annotated entity pairs by automatically mining literal semantic information. (4) align-ments are associated with each other. traditional ea methods align entities in the local alignment way by calculating the cross kgs similarity of entities and selecting the most similar entity as ea results. the local alignment neglects the association between alignment and suffers from conflicting many-to-one and many-to-many alignments. considering the above facts, we propose ued, an accurate unsupervised method for joint ea and ded. for ea, to automatically mine the literal semantic information, we generate pseudo entity pairs for the align loss and design a semantic-based globally guided loss to guide the alignment for all entities, not only for those in entity pairs. for ded, since verifying the dangling entity has to check all the entities in the target kg and the dangling entity set is unavailable, we add empty entities into two kgs and transfer the ea and ded tasks into a modified global optimal transport problem (otp) to identify dangling entities relying on pseudo entity pairs only. we propose a simple but effective way to reduce the complexity of otp. our experiments show that the dangling entity identification mechanism also enhances the ea performance. there are several traditional ea datasets widely used in the ea task. nevertheless, neither dataset provides a dangling test set for ded. as mentioned above, identifying dangling entities is crucial in real-world knowledge graph integration. to demonstrate the effectiveness of our method and incentivize future studies, we construct a cross-lingual medical knowledge graph dataset with ea task and ded task, called meded, based on the unified medical language system (umls) @xcite . we summarize the main contributions as follows: in the ea task, ued achieves comparable results with stateof-the-art supervised baselines, and the supervised version of ued outperforms the current state-of-the-art methods. the source code of ued is publicly available at https://github.com/luosx18/ued . ## ued framework in this section, we first briefly describe the tasks of ea and ded and then elucidate our unified unsupervised approach to solve ea along with ded. an overview of our method is depicted in figure 1 . ## conclusion this paper proposes a novel unified unsupervised method for both ea and ded, which better fits the realistic scenario for integrating kgs. ued contains four modules: pseudo entity pair generation, information aggregation, globally guided similarity and loss, and a modified otp for global ea and ded. the first three modules mine the information in kgs to get rid of supervised entity pairs, while the last module integrates ea and ded into a unified framework to identify dangling entities without supervision and provide better ea results. we also construct a new dataset for the ea and ded tasks and perform experiments to demonstrate the effectiveness of ued.