From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment (2023)

Abstract: Entity Alignment (EA) aims to find the equivalent entities between two Knowledge Graphs (KGs). Existing methods usually encode the triples of entities as embeddings and learn to align the embeddings, which prevents the direct interaction between the original information of the cross-KG entities. Moreover, they encode the relational triples and attribute triples of an entity in heterogeneous embedding spaces, which prevents them from helping each other. In this paper, we transform both triples into unified textual sequences, and model the EA task as a bi-directional textual entailment task between the sequences of cross-KG entities. Specifically, we feed the sequences of two entities simultaneously into a pre-trained language model (PLM) and propose two kinds of PLM-based entity aligners that model the entailment probability between sequences as the similarity between entities. Our approach captures the unified correlation pattern of two kinds of information between entities, and explicitly models the fine-grained interaction between original entity information. The experiments on five cross-lingual EA datasets show that our approach outperforms the state-of-the-art EA methods and enables the mutual enhancement of the heterogeneous information. Codes are available at https://github.com/OreOZhao/TEA .

## introduction knowledge graphs (kgs) organize and store the facts in the real world to an effective structure, and have been applied to many knowledge-driven tasks, such as question answering @xcite , recommender systems @xcite , and information extraction @xcite . since the kgs are often from various domains, entity alignment (ea) provides fundamental techniques to find the equivalent entities in two kgs, which would complement the knowledge coverage of kgs. existing ea methods usually consist of two modules: (1) embedding module encodes entity information to entity embeddings, (2) alignment module guides the embeddings of the aligned entities to be similar @xcite . moreover, they usually incorporate two kinds of heterogeneous triples as shown in figure 1a: (1) relational triples (h, r, t), represents the relation r between head entity h and tail entity r, (2) attribute triples (e, a, v), represents the attribute value v of the attribute a of entity e. despite the progress of existing ea methods @xcite @xcite , they are limited by the embedding-based architecture in two folds: (1) lack of direct interaction between kgs. existing methods usually treat ea as a representation learning task. during the encoding process, the origin triples of entities are compressed to a continuous vector, which prevents them from directly interacting with each other. however, the origin information contains rich semantics information. take the entity "the rolling stones" in figure 1a as an example, the attribute value "rollingstones.com" and "1962" of the chinese kg are highly compatible with the value "the rolling stones" and "1962" in the english kg. the correlation between the values can directly indicate the alignment of two entities. inspired by recent progress of pre-trained language models (plms) @xcite @xcite , we transform both two kinds of triples into textual sequences, and propose a unified textual entailment framework for entity alignment tea. we model the ea task as a bi-directional textual entailment task between the sequences of cross-kg entities as shown in figure 1b to explicitly capture the fine-grained interaction between entity information. specifically, we combine two sequences of entities in one sequence with cloze-style templates and feed the combined sequence into a plm. we further propose two aligners to model the entailment probability as the pre-training tasks of plm, i.e. next sentence prediction (nsp) and masked language modeling (mlm). the nsp-aligner predicts the probability of whether one entity is next sentence of the other, while the mlm-aligner fills in the blanks between entity sequences with mapped label words "yes" or "no". the positive entailment probability is seen as entity similarity and is used for ranking the candidate entities. the experiments on five crosslingual ea datasets show that tea outperforms the state-of-the-art methods and enables the mutual enhancement of heterogeneous information. overall, the contributions of this paper can be summarized as follows: ## conclusion to address the limitations of the existing ea method, the lack of interaction and heterogeneous embedding spaces, we propose a unified textual entailment framework for entity alignment called tea. we transform the origin relational triples and attribute triples of an entity into textual sequences and model the ea task as a bi-directional textual entailment task between the sequences of cross-kg entities. we propose two kinds of plm-based aligners to capture the fine-grained correlation between entities with two kinds of sequences in a unified manner. the entailment probability is used for measuring entity similarity and ranking the entity candidates. experiment results on five cross-lingual datasets show that tea outperforms existing ea methods and enables the mutual enhancement between the heterogeneous information.