Survey and open problems in privacy-preserving knowledge graph: merging, query, representation, completion, and applications

Chen, Chaochao; Zheng, Fei; Cui, Jamie; Cao, Yuwei; Liu, Guanfeng; Wu, Jia; Zhou, Jun

doi:10.1007/s13042-024-02106-6

Cited by 4 publications

(1 citation statement)

References 138 publications

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“…Its objective is to establish associations between entities that share the same semantic meaning. 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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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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Causal intervention for knowledge graph denoising in recommender systems

Guo,

Song,

Feng

et al. 2024

Int. J. Mach. Learn. & Cyber.

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Privacy preserving knowledge graph for healthcare applications

Sharma

Bhatt

2022

J. Phys.: Conf. Ser.

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Knowledge graphs in healthcare applications help all the stakeholders in the healthcare domain. The doctors can consult across various hospitals for a particular scenario or can query the knowledge graph for the history of the patients. Similarly, the patients can query the knowledge graph through well-defined interface or a Chabot system for location of the nearest doctor with a high rating. The physicians can query the knowledge graph for a generic drug family or detecting salt of a particular drug. The paper proposes architecture for knowledge graph in healthcare domain. Further, privacy preserving for a medical domain is an essential requirement. The paper proposes privacy preserving schemes for knowledge graph for healthcare applications.

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Survey and open problems in privacy-preserving knowledge graph: merging, query, representation, completion, and applications

Cited by 4 publications

References 138 publications

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

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

Causal intervention for knowledge graph denoising in recommender systems

Privacy preserving knowledge graph for healthcare applications

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