Extending the TOP Framework with an Ontology-Based Text Search Component.

INTRODUCTION: Constructing search queries that deal with complex concepts is a challenging task without proficiency in the underlying query language - which holds true for either structured or unstructured data. Medical data might encompass both types, with valuable information present in one type but not the other.

METHOD: The TOP Framework provides clinical practitioners as well as researchers with a unified framework for querying diverse data types and, furthermore, facilitates an easier and intuitive approach. Additionally, it supports collaboration on query modeling and sharing.

RESULTS: Having demonstrated its effectiveness with structured data, we introduce the integration of a component for unstructured data, specifically medical documents.

CONCLUSION: Our proof-of-concept shows a query language agnostic framework to model search queries for unstructured and structured data.