Integrating Manifold Knowledge for Global Entity Linking with Heterogeneous Graphs

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 manifold knowledge helpful for linking, 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 manifold 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.

Integration of Semantic and Topological Structural Similarity Comparison for Entity Alignment without Pre-Training

“…Among the selected methods, we included translation-based approaches such as MTransE [13] and BootEA [14]. Additionally, we incorporated GNN-based techniques such as GCN-Align [17], RDGCN [18] and Dual-AMN [19]. Each baseline model has unique characteristics and high performance that make them suitable for evaluating the effectiveness of our proposed method.…”

“…This facilitates tasks such as semantic relevance reasoning, entity relationship reasoning and knowledge graph applications. GNN-based methods [16], represented by models such as GCN-Align [17], RDGCN [18] and Dual-AMN [19], generate entity embeddings by aggregating neighborhood information using graph neural networks. These models effectively capture structural information and learn entity embeddings.…”

LLM-KGMQA: Large Language Model-Augmented Multi-Hop Question-Answering System based on Knowledge Graph in Medical Field

“…Entity Linking (EL) (Mingyang et al, 2021, Zhibin et al, 2022 (Cai et al, 2023) addressed the difficulty of models in learning diverse and more challenging negative samples in zero-shot entity linking scenarios. They proposed a generative negative sampling method that enables models to learn negative samples better, thereby improving the performance of zero-shot entity linking.…”