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

Year: 2024

Type: book-chapter

Abstract: 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.

Source: Studies in health technology and informatics

DOI: 10.3233/shti240854

Authors: Franz Matthies, Christoph Beger, Ralph Schäfermeier, Konrad Höffner, Alexandr Uciteli

Institution: Leipzig University

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

OpenAlex ID: W4402279337

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