Natural Language Processing for Ontology Development in IoT-Enabled Smart Healthcare

Abstract

The healthcare industry is a knowledge-driven system and continuously expanding with enormous volumes of narrative data that are typically stored in unstructured and non-standardized formats. Therefore, it is challenging for systems to manage massive amounts of narrative data, comprehend the contents of these data, find relevant and useful healthcare information, and make decisions. In the healthcare domain, ontologies provide a formal specification of health data for knowledge representation and data integration. Therefore, ontologies enable the representation of health information in a machine-readable form and allow this information to be shared, reused, and used to make deductions. In recent years, modern healthcare services have been significantly affected by the growth of the interconnectedness of systems and the improvements in the Internet of Things (IoT). Moreover, wearables and implantable appliances enhance people’s quality of life and ease their life by performing continuous health monitoring. IoT technologies facilitate data flow across multiple entities in healthcare systems and deal with different data formats. Semantic Web and ontologies provide interoperability among IoT ecosystems by describing concepts and relationships between different entities. Nevertheless, extracting concepts and relationships from the healthcare domain is one of the most important processes in ontology development. Therefore, Semantic Web technologies benefit from Natural Language Processing (NLP) technologies to convert unstructured health data to meaningful representations. Thus, the structured and unstructured data can be combined by integrating NLP and Semantic Web technologies. Hence, NLP is a fundamental capability for cognitive computing systems and is frequently characterized as a behavioral technology that assists computers in understanding and comprehending human language. In the health domain, NLP techniques are used to gather unstructured healthcare data, examine its grammatical structure, and ascertain its meaning. Consequently, NLP techniques increase the quality of healthcare services while reducing costs. This chapter presents the NLP methods applied to the IoT-enabled healthcare domain in the scope of semantic intelligence. For this purpose, the main principles of NLP for the Semantic Web, and methodologies for creating ontologies by utilizing the NLP techniques to process heterogeneous data sources are presented. Also, the relationship between NLP and Semantic Web is explored within the context of ontology creation and population for IoT-based healthcare systems. Finally, the recent studies, applications, opportunities, and challenges in the related field are examined.