Smell and Taste Disorders Knowledge Graph: Answering Questions Using Health Data

Smell and taste disorders have become a more prominent issue due to their association with Covid-19, and their impact on quality of life and health outcomes. However, pertinent information regarding these disorders is often inaccessible and poorly organized, with the majority of data stored solely in clinical data repositories. To rectify this, a technological solution capable of digitizing, semantically modeling, and integrating health data is necessary. The knowledge graph, an emerging technology capable of organizing inconsistent and heterogeneous health data and inferring implicit knowledge, presents a viable solution to this problem. In pursuit of the aforementioned goal, an existing ontology pertaining to smell and taste disorders was enriched by introducing additional relevant concepts and relationships. Subsequently, a knowledge graph was constructed based on the defined ontology and patients’ data. The resultant knowledge graph was subjected to a rigorous evaluation, encompassing dimensions such as completeness, coherency, coverage, and succinctness. The evaluation established the effectiveness and usability of the knowledge graph, with only minor issues detected through the OOPS! pitfall scanner. Furthermore, as a proof-of-concept for clinical application, a user interface was created, enabling users to access pertinent information concerning smell and taste disorders, including causative factors, medications, and etiology, among others. The interface generates a graph-based structure based on the selected question from a drop-down menu. The end-user can modify the query by merely clicking on the generated graph to ask related questions. This study showcases the potential of knowledge graphs centered on smell and taste disorders to organize and provide accessible health data to end-users.