ESGNet: A multimodal network model incorporating entity semantic graphs for information extraction from Chinese resumes

Luo, Shun; Yu, Juan

doi:10.1016/j.ipm.2023.103524

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…This enables accurate correspondence when performing operations such as data integration [7], querying [8] and analysis [9] across different data sources. EA plays a crucial role in natural language processing, information extraction [10], intelligent question-answering systems [11] and social network analysis [12].…”

Section: Introductionmentioning

confidence: 99%

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

Liu,

Liu

2024

Electronics

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Entity alignment (EA) is a critical task in integrating diverse knowledge graph (KG) data and plays a central role in data-driven AI applications. Traditional EA approaches rely on entity embeddings, but their effectiveness is limited by scarce KG input data and representation learning techniques. Large language models have shown promise, but face challenges such as high hardware requirements, large model sizes and computational inefficiency, which limit their applicability. To overcome these limitations, we propose an entity-alignment model that compares the similarity between entities by capturing both semantic and topological information to enable the alignment of entities with high similarity. First, we analyze descriptive information to quantify semantic similarity, including individual features such as types and attributes. Then, for topological analysis, we introduce four conditions based on graph connectivity and structural patterns to determine subgraph similarity within three hops of the entity’s neighborhood, thereby improving accuracy. Finally, we integrate semantic and topological similarity using a weighted approach that considers dataset features. Our model requires no pre-training and is designed to be compact and generalizable to different datasets. Experimental results on four standard EA datasets validate the effectiveness of our proposed model.

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