An Experimental Study of State-of-the-Art Entity Alignment Approaches

Abstract

Entity alignment (EA) finds equivalent entities that are located in different knowledge graphs (KGs), which is an essential step to enhance the quality of KGs, and hence of significance to downstream applications (e.g., question answering and recommendation). Recent years have witnessed a rapid increase of EA approaches, yet the relative performance of them remains unclear, partly due to the incomplete empirical evaluations, as well as the fact that comparisons were carried out under different settings (i.e., datasets, information used as input, etc.). In this paper, we fill in the gap by conducting a comprehensive evaluation and detailed analysis of state-of-the-art EA approaches. We first propose a general EA framework that encompasses all the current methods, and then group existing methods into three major categories. Next, we judiciously evaluate these solutions on a wide range of use cases, based on their effectiveness, efficiency and robustness. Finally, we construct a new EA dataset to mirror the real-life challenges of alignment, which were largely overlooked by existing literature. This study strives to provide a clear picture of the strengths and weaknesses of current EA approaches, so as to inspire quality follow-up research. ! 1. As where we are standing, EA can be deemed as a special case of entity resolution (ER), which recalls a pile of literature (to be discussed in Section 2.2). Thus, some ER methods (with minor adaptation to handle EA) are also involved in this study to ensure the comprehensiveness of the research.this article, we provide an empirical evaluation of state-of-the-art EA approaches with the following features: (1) Fair comparison within and across categories. Almost all recent studies [5], [24], [38], [55], [60], [61], [62], [63],[67] are confined to comparing with only a subset of methods. In addition, different approaches follow different settings: some merely use the KG structure for alignment, while others also utilize additional information; some align KGs in one pass, while others employ an iterative (re-)training strategy. Although a direct comparison of these methods, as reported in the literature, demonstrates the overall effectiveness of the solutions, a more preferable and fairer practice would be to group these methods into categories and then compare the results both within and across categories.In this study, we include most state-of-the-art methods for lateral comparison, including those very recent efforts that have not yet been compared with others before. By dividing them into three groups and conducting detailed analysis on both intraand inter-group evaluations, we are able to better position these approaches and assess their effectiveness.(2) Comprehensive evaluation on representative datasets. To evaluate the performance of EA systems, several datasets have been constructed, which can be broadly categorized into cross-lingual benchmarks, represented by DBP15K [53], and mono-lingual benchmarks, represented by DWY100K [54]. A very recent study [24] points out th...

Toward Entity Alignment in the Open World: An Unsupervised Approach with Confidence Modeling

“…Entity alignment The majority of state-of-the-art methods are supervised or semi-supervised, which can be roughly divided into three categories, i.e., methods merely using the structural information, methods that utilize the iterative training strategy, and methods using information in addition to the structural information [20].…”

“…State-of-the-art EA solutions generate for each source entity a corresponding target entity and fail to consider the potential unmatchable issue. Nevertheless, as discussed in [20], in real-life settings, KGs contain entities that other KGs do not contain. For instance, when aligning YAGO 4 and IMDB, only 1% of entities in YAGO 4 are related to movies, while the other 99% of entities in YAGO 4 necessarily have no match in IMDB.…”

“…Concretely, we adopt the two cross-lingual datasets, − and − , which are extracted from the multilingual KGs of DBpedia. Note that we do not use the mono-lingual KG pairs in SRPRS since using the side information can already achieve the ground-truth results [20]. There are no unmatchable entities in SRPRS.…”

A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

“…In other cases, the authors propose the creation of new KGs such as the Question-Answering KG [18], Eventcentric Tourism Knowledge Graph (ETKG) [15], POI-Sensitive Knowledge Graph [20], Cohort Knowledge Graph [75], among others. Other works perform both tasks, i.e., create a KG (DBP-FB) and reuse other existing sets to enrich or complete information on the original graph [80]. Finally, another group of works corresponds to studies that discuss certain tasks related to KG [13,22,63,73,82].…”

Towards Knowledge Graphs Federations: Issues and Technologies

“…We conducted experiments on three mainstream EA datasets, DBP15K, DWY100K, and SRPRS, for three groups of EA models. We provide the results on DBP15K in Table 1 and leave out the performance on the other two datasets in the interest of space, which can be found in [39]. Based on the experimental results in Table 1, we provide some guidelines and suggestions for users of EA approaches.…”