Ontology-Driven Semantic Interoperability Framework for Multi-Center Healthcare Big Data with Graph-Based Query Analysis

This has led to several challenges in attaining semantic compatibility of the large multi-center health care data systems obtained across different systems. IVERSITY of representation of data, formats, and terminologies make it difficult to match up and analyze data. In the light of these challenges, there is a proposed solution called the OntologyDriven Semantic Interoperability Framework which applies definitive ontologies of the domain to achieve the interlinking of health data. It thus facilitates data sharing across the different institutions, and also ensures that the data is consistent and can be interpreted in the same way. This component of our approach is called the graph-based query analysis where graph database and query optimization techniques are used to improve the efficiency of the data retrieval process. Our approach also effectively represents healthcare entities and their relationships as a knowledge graph to support human-controllable queries and various semantic reasoning. Moreover, for an automated matching of the different data sets to a common conceptual schema we employ machine learning techniques to automatically map various sources of input to the created ontology. The proposed framework facilitates the access of data, enables researchers and clinicians to make more-informed decisions and helps in big science in the biomedical field. Experimental analysis prove that arsenal is efficient in comparing to traditional relational databases in terms of execution of queries' time and their accuracy. Our work reveals that using the approaches based on ontology can effectively eliminate the issues of semantic mismatch and will contribute to the development of an intelligent environment for healthcare.