Knowledge graph embedding via the properties of mapping and dynamic matrix
A Sangeetha Devi, A Kalaivani · Indian Journal of Science and Technology · 2021
Objective: The main objective of this research is constructing a knowledge graph using a superior model that leverage the knowledge through relational mapping properties and the matrix constructed dynamically based on the changes in the relational mapping. Methods: This research work proposes symbolic objects in the knowledge graph that are signified by two vectors, the first vector denotes the entity/relation and the vector denote the matrix which is constructed with the help of mapping properties. The proposed methodology uses only minimum parameters and avoids multiplication processes which make the proposed scheme effective in the large scale graphs. The matrix representation with the assistance of mapping properties reduces the computational complication and the time requires to attain the knowledge is also minimized. Findings: The empirical research on knowledge graph mining gives significant insights and it is applied on benchmark datasets namely FB15K-237 and WN18RR. The performance are evaluated in terms of precision and recall in percentage. Keywords: Knowledge embedding; Entity; Dynamic matrix; Relation and Edge