Detecting the Medical Plant Association from PubMed Using Hypergraph-based Clustering with Dominating Set

Abstract

Medicinal plants provide immunity against diseases and can also be taken in a precautionary sense against them. It is pivotal to know the benefits of these plants against various ailments.  The identification of these plants’ essential properties can give a great impact on medicinal research and practice. This research focuses on identifying the cardinal properties of five plants namely- Aloe Vera, Fennel, Fenugreek, Mint, and Tulsi by using the concept of text analytic features and NLP functions. Text data on medicinal plants are extracted from the biomedical literature dataset. Text mining is used for the extraction of the implicit relations between medicinal plants and their biomedical properties. The intricate relationship between the keywords and the medicinal plants is captured using hypergraph clustering and dominating sets. The visualization of the correlation between the keywords and the plants is carried out by clustering. With an emphasis on their potential in preventative and medical care, this model lists the common characteristics and health advantages of medicinal plants. Strong clustering is indicated by the modularity score of 0.577, with five separate communities each reflecting a unique set of features. In order to facilitate future studies, these findings offer a methodical and data-driven viewpoint.