Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing

Abstract

Natural language processing (NLP) is one of the most energizing topics in artificial intelligence (AI), and it has already spawned technologies such as voice assistants, chatbots, and translators, among many other everyday applications. NLP is a discipline of AI, computer science, and linguistics that is intimately linked to ontologies. Ontologies enable the interpretation of natural (human) language by providing an explicit and formal mechanism to analyze, integrate, and share data. This chapter we show how to use ontology for NLP in a variety of ways. Its purpose is to discuss and present ontology and knowledge graph concepts comprehensively, specifically: to explain the role of ontology and knowledge graphs in semantic analysis, to highlight current trends and the future scope of ontology and knowledge graph semantic analysis in NLP, and to present a state-of-the-art overview of ontological semantic analysis in NLP. Attempting to bridge the gap between ontology-driven and NLP-driven approaches, the chapter discusses practical scenarios for combining the two.