https://www.ronpub.com/ojsw/OJSW_2020v7i1n03_Erekhinskaya.html Channel of the paper: Tatiana Erekhinskaya, Marta Tatu, Mithun Balakrishna, Sujal Patel, Dmitry Strebkov and Dan Moldovan: Ten Ways of Leveraging Ontologies for Rapid Natural Language Processing Customization for Multiple Use Cases in Disjoint Domains, Open Journal of Semantic Web (OJSW), 7 (1), pages 33-51, URN: urn:nbn:de:101:1-2020112218332779310329, 2020 en-us https://www.ronpub.com/ojsw/OJSW_2020v7i1n03_Erekhinskaya.html http://nbn-resolving.de/urn:nbn:de:101:1-2020112218332779310329 With the ever-growing adoption of AI technologies by large enterprises, purely data-driven approaches have dominated the field in the recent years. For a single use case, a development process looks simple: agreeing on an annotation schema, labeling the data, and training the models. As the number of use cases and their complexity increases, the development teams face issues with collective governance of the models, scalability and reusablity of data and models. These issues are widely addressed on the engineering side, but not so much on the knowledge side. Ontologies have been a well-researched approach for capturing knowledge and can be used to augment a data-driven methodology. In this paper, we discuss 10 ways of leveraging ontologies for Natural Language Processing (NLP) and its applications. We use ontologies for rapid customization of a NLP pipeline, ontologyrelated standards to power a rule engine and provide standard output format. We also discuss various use cases for medical, enterprise, financial, legal, and security domains, centered around three NLP-based applications: semantic search, question answering and natural language querying.