The State of the Art on Knowledge Graph Construction from Text: Named Entity Recognition and Relation Extraction Perspectives

Abstract

Knowledge graphs (KG) play a crucial role in many modern applications. The industrial knowledge is scattered in large volumes of both structured and unstructured data sources and bringing them to a unified knowledge graph can bring a lot of value. However, automatically constructing a KG from natural language text is challenging due to the ambiguity and impreciseness of the natural languages. Recently, many approaches have been proposed to transform natural language text into triples to construct KGs. This presentation aims to summarize the research progress over the KG construction from text with a specific focus on the information acquisition branch entailing entity and relation extraction covering the state of the art methods and tools. In this talk, we will provide an overview of the field of knowledge graph construction from text including the state of the art methods, techniques and tools for constructing knowledge graphs from text, their capabilities, limitations and current challenges. This will be useful for any practitioner who is interested in building knowledge graphs for their organizations.