Collective Entity Disambiguation Based on Deep Semantic Neighbors and Heterogeneous Entity Correlation

Abstract

Entity Disambiguation (ED) aims to associate entity mentions recognized in text corpus with the corresponding unambiguous entry in knowledge base (KB). A large number of models were proposed based on the topical coherence assumption. Recently, several works have proposed a new assumption: topical coherence only needs to hold among neighboring mentions, which proved to be effective. However, due to the complexity of the text, there are still some challenges in how to accurately obtain the local coherence of the mention set. Therefore, we introduce the self-attention mechanism in our work to capture the long-distance dependencies between mentions and quantify the degree of topical coherence. Based on the internal semantic correlation, we find the semantic neighbors for every mention. Besides, we introduce the idea of “simple to complex” to the construction of entity correlation graph, which achieves a self-reinforcing effect of low-ambiguity mention towards high-ambiguity mention during collective disambiguation. Finally, we apply the graph attention network to integrate the local and global features extracted from key information and entity correlation graph. We validate our graph neural collective entity disambiguation (GNCED) method on six public datasets and the results demonstrate a better performance improvement compared with state-of-the-art baselines.