Abstract
Entity Linking (EL) aims to automatically link the mentions in unstructured documents to corresponding entities in a knowledge base (KB), which has recently been dominated by global models. Although many global EL methods attempt to model the topical coherence among all linked entities, most of them failed in exploiting the correlations among various linking clues, such as the semantics of mentions and their candidates, the neighborhood information of candidate entities in KB and the fine-grained type information of entities. As we will show in the paper, interactions among these types of information are very useful for better characterizing the topic features of entities and more accurately estimating the topical coherence among all the referred entities within the same document. In this paper, we present a novel HEterogeneous Graph-based Entity Linker (HEGEL) for global entity linking, which builds an informative heterogeneous graph for every document to collect various linking clues. Then HEGEL utilizes a novel heterogeneous graph neural network (HGNN) to integrate the different types of information and model the interactions among them. Experiments on the standard benchmark datasets demonstrate that HEGEL can well capture the global coherence and outperforms the prior state-of-the-art EL methods.
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Acknowledgments
This work is supported in part by the National Key R&D Program of China (2020AAA0106600) and the Key Laboratory of Science, Technology and Standard in Press Industry (Key Laboratory of Intelligent Press Media Technology).
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Chen, Z., Wu, Y., Feng, Y., Zhao, D. (2021). Integrating Manifold Knowledge for Global Entity Linking with Heterogeneous Graphs. In: Qin, B., Jin, Z., Wang, H., Pan, J., Liu, Y., An, B. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction. CCKS 2021. Communications in Computer and Information Science, vol 1466. Springer, Singapore. https://doi.org/10.1007/978-981-16-6471-7_7
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