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Computer Science > Information Retrieval

arXiv:2312.10049 (cs)
[Submitted on 2 Dec 2023]

Title:Knowledge Graph Reasoning Based on Attention GCN

Authors:Meera Gupta, Ravi Khanna, Divya Choudhary, Nandini Rao
Download a PDF of the paper titled Knowledge Graph Reasoning Based on
Attention GCN, by Meera Gupta and 3 other authors
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    Abstract:We propose a novel technique to enhance Knowledge Graph
    Reasoning by combining Graph Convolution Neural Network (GCN)
    with the Attention Mechanism. This approach utilizes the
    Attention Mechanism to examine the relationships between entities
    and their neighboring nodes, which helps to develop detailed
    feature vectors for each entity. The GCN uses shared parameters
    to effectively represent the characteristics of adjacent
    entities. By integrating the attributes of the entities and their
    interactions, this method generates extensive implicit feature
    vectors for each entity, improving performance in tasks including
    entity classification and link prediction, outperforming
    traditional neural network models. To conclude, this work
    provides crucial methodological support for a range of
    applications, such as search engines, question-answering systems,
    recommendation systems, and data integration tasks.

Subjects: Information Retrieval (cs.IR)
Cite as:  arXiv:2312.10049 [cs.IR]
          (or arXiv:2312.10049v1 [cs.IR] for this version)
          https://doi.org/10.48550/arXiv.2312.10049
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Meera Gupta [view email]
[v1] Sat, 2 Dec 2023 06:36:14 UTC (324 KB)
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