Open Information Extraction from Texts: Part II. Extraction of Semantic Relationships Using Unsupervised Machine Learning

Abstract

In this paper we discuss open information extraction from natural language texts. We present an approach to extraction of semantic relationships using unsupervised machine learning. The presented approach is based on deep clustering methods in which the clusterization algorithm is integrated in a multi-layer auto-encoder neural network. This method allows one to generalize surface relationships (triplets) into semantic relationships. This paper also provides a method of surface relationship extraction.