Ten ways of leveraging ontologies for natural language processing and its enterprise applications

In the last years, Artificial Intelligence and Deep Learning have matured from a facinating research area to real-word applications across multiple domains. Enterprises adopt data-driven approaches for various use cases. With the increased adoption, such issues as governance of the models, deployment, scalability, reusablity and maintenance are widely addressed on the engineering side, but not so much on the knowledge side. In this paper, we demonstrate 10 ways of leveraging ontology for Natural Language Processing. Specifically, we explore the usage of ontologies and related standards for labeling schema, configuration, providing lexical data, powering rule engine and automated generation of rules, as well as providing a standard output format. Additionally, we discuss three NLP-based applications: semantic search, question answering and natural language querying and show how they can benefit from ontology usage. The paper summarizes our experience of using ontology in a number of projects for medical, enterprise, financial, legal and security domains.