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K-Graph: knowledgeable graph for text documents

Mittal Varsha, Gangodkar Durgaprasad, Pant Bhaskar

Journal of KONBiN

2021

Vol. 51, iss. 1

73--89

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 realworld 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.

B FGMAC : breadth - first frequent subgraph mining with ARC consistency

Douar B., Latiri C., Liquiere M., Slimani Y.

Journal of Artificial Intelligence and Soft Computing Research

2011

Vol. 1, No. 4

269--281

The paper presents a new projection operator for graphs named AC-projection, which exhibits nice theoretical complexity properties unlike to the graph isomorphism operator typically used in graph mining. We study the size of the search space as well as some practical properties of the projection operator. We also introduce a novel breadth-first algorithm for frequent AC-reduced subgraphs mining. Then, we prove experimentally that we can achieve an important performance gain (polynomial complexity projection) without or with non-significant loss of discovered patterns in terms of quality.