Graph databases are applied in many applications, including science and business, due to their low-complexity, low-overheads, and lower time-complexity. The graph-based storage offers the advantage of capturing the semantic and structural information rather than simply using the Bag-of-Words technique. An approach called Knowledgeable graphs (K-Graph) is proposed to capture semantic knowledge. Documents are stored using graph nodes. Thanks to weighted subgraphs, the frequent subgraphs are extracted and stored in the Fast Embedding Referral Table (FERT). The table is maintained at different levels according to the headings and subheadings of the documents. It reduces the memory overhead, retrieval, and access time of the subgraph needed. The authors propose an approach that will reduce the data redundancy to a larger extent. With real-world datasets, K-graph’s performance and power usage are threefold greater than the current methods. Ninety-nine per cent accuracy demonstrates the robustness of the proposed algorithm.


    Access

    Download


    Export, share and cite



    Title :

    K-Graph: Knowledgeable Graph for Text Documents


    Contributors:


    Publication date :

    2021




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Developing a Marine-Knowledgeable Society

    Bjur, D. M. | British Library Conference Proceedings


    Classification of Web Documents Using a Graph Model

    Schenker, A. / Last, M. / Bunke, H. et al. | British Library Conference Proceedings | 2003


    Classification of Web documents using a graph model

    Schenker, A. / Last, M. / Bunke, H. et al. | IEEE | 2003



    Visual graph mining for graph matching

    Zhang, Quanshi / Song, Xuan / Yang, Yu et al. | British Library Online Contents | 2019