Knowledge Base Population Model Using Non-Negative Matrix Factorization

Jiho Kim, Sangha Nam, Key-Sun Choi

http://doi.org/10.5626/JOK.2018.45.9.918

The purpose of a knowledge base is to incorporate all the knowledge in the world in a format that machines can understand. In order for a knowledge base to be useful, it must continuously acquire and add new knowledge. However, it cannot if it lacks knowledge-acquisition ability. Knowledge is mainly acquired by analyzing natural language sentences. However, studies on internal knowledge acquisition are being neglected. In this paper, we introduce a non-negative matrix factorization method for knowledge base population. The model introduced in this paper transforms a knowledge base into a matrix and then learns the latent feature vector of each entity tuple and relation by decomposing the matrix and reassembling the vectors to score the reliability of the new knowledge. In order to demonstrate the effectiveness and superiority of our method, we present results of experiments and analysis performed with Korean DBpedia.