Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment (2020)

Abstract: Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. GNN-based EA methods present promising performance by modeling the KG structure defined by relation triples. However, attribute triples can also provide crucial alignment signal but have not been well explored yet. In this paper, we propose to utilize an attributed value encoder and partition the KG into subgraphs to model the various types of attribute triples efficiently. Besides, the performances of current EA methods are overestimated because of the name-bias of existing EA datasets. To make an objective evaluation, we propose a hard experimental setting where we select equivalent entity pairs with very different names as the test set. Under both the regular and hard settings, our method achieves significant improvements (5.10% on average Hits@1 in DBP15k) over 12 baselines in cross-lingual and monolingual datasets. Ablation studies on different subgraphs and a case study about attribute types further demonstrate the effectiveness of our method. Source code and data can be found at https://github.com/thunlp/explore-and-evaluate .

## introduction the prosperity of data mining has spawned knowledge graphs (kgs) in many domains that are often complementary to each other. entity alignment (ea) provides an effective way to integrate the complementary knowledge in these kgs into a unified kg by linking equivalent entities, thus benefiting knowledge-driven applications such as question answering @xcite , recommendation @xcite and information extraction @xcite . however, ea is a non-trivial task that it could be formulated as * corresponding author. a kg comprises a set of triples, with each triple consisting of a subject, predicate, and object. there are two types of triples: (1) relation triples, in which both the subject and object are entities, and the predicate is often called relation (see figure 1(a) ); and (2) attribute triples, in which the subject is an entity and the object is a value, which is either a number or literal string (see figure 1(c )), and the predicate is often called attribute. most of the previous ea models @xcite @xcite rely on the structure assumption that, the adjacencies of two equivalent entities in kgs usually contain equivalent entities @xcite (see @xcite 1(a) ). these models mainly focus on modeling kg structure defined by the relation triples. however, we argue that attribute triples can also provide important clues for judging whether two entities are the same, based on the attribute assumption that: equivalent entities often share similar attributes and values in kgs. for example, in figure 1(b) , the equivalent entities e and e share the attribute area with similar values of 153, 909 and 154, 077. therefore, we aim to improve ea using attribute triples. we have identified the challenges of attribute incorporation and dataset bias. ## related work recent entity alignment methods can be classified into embedding-based methods and graph neural network-based (gnn-based) methods. ## methodology the key idea of attrgnn is to use graph partition and attributed value encoder to deal with various types of attribute triples. in this section, we first define kg and then introduce our graph partition strategy. further, we design different gnn channels for different subgraphs and present two methods to ensemble all channels' outputs for final evaluation. ## experiments in this section, we compare attrgnn with 12 baselines on the regular setting and our designed hard setting of ea. we also present an ablation study and a case study to evaluate attributes' and values' effects for ea. ## conclusion and future work we propose a novel ea model (attrgnn) and contribute a hard experimental setting for practical evaluation. attrgnn can integrate both attribute and relation triples with varying importance for better performance. experimental results under the regular and hard settings present significant improvements of our proposed model, and the severe dataset bias can be effectively alleviated in our proposed hard setting. in the future, we are interested in replacing bert with knowledge enhanced and number sensitive text representations models @xcite .