A Survey of Knowledge Graph Construction Using Machine Learning

Abstract

Knowledge graph (KG) serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework. This framework facilitates a transformation in information retrieval, transitioning it from mere string matching to far more sophisticated entity matching. In this transformative process, the advancement of artificial intelligence and intelligent information services is invigorated. Meanwhile, the role of machine learning method in the construction of KG is important, and these techniques have already achieved initial success. This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning. With a profound amalgamation of cutting-edge research in machine learning, this article undertakes a systematical exploration of KG construction methods in three distinct phases: entity learning, ontology learning, and knowledge reasoning. Especially, a meticulous dissection of machine learning-driven algorithms is conducted, spotlighting their contributions to critical facets such as entity extraction, relation extraction, entity linking, and link prediction. Moreover, this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled, large-scale KG construction.