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Moreover, the analysis of the services visited the most by a patient before a new diagnosis, as well as the type of requested tests, may uncover patterns that contribute to earlier disease detection and treatment effectiveness. Built on knowledge-driven ecosystems, we devise DE4LungCancer, a health data ecosystem of data sources for lung cancer. In this data ecosystem, knowledge extracted from heterogeneous sources, e.g., clinical records, scientific publications, and pharmacological data, is integrated into knowledge graphs. Ontologies describe the meaning of the combined data, and mapping rules enable the declarative definition of the transformation and integration processes. DE4LungCancer is assessed regarding the methods followed for data quality assessment and curation. Lastly, the role of controlled vocabularies and ontologies in health data management is discussed, as well as their impact on transparent knowledge extraction and analytics. This paper presents the lessons learned in the DE4LungCancer development. It demonstrates the transparency level supported by the proposed knowledge-driven ecosystem, in the context of the lung cancer pilots of the EU H2020-funded project BigMedilytic, the ERA PerMed funded project P4-LUCAT, and the EU H2020 projects CLARIFY and iASiS.<\/jats:p>","DOI":"10.3233\/sw-223294","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T13:12:01Z","timestamp":1682082721000},"page":"943-976","source":"Crossref","is-referenced-by-count":11,"title":["Knowledge graphs for enhancing transparency in health data ecosystems1"],"prefix":"10.1177","volume":"14","author":[{"given":"Fotis","family":"Aisopos","sequence":"first","affiliation":[{"name":"Institute of Informatics & Telecommunications, National Centre for Scientific Research \u201cDemokritos\u201d, Greece"}]},{"given":"Samaneh","family":"Jozashoori","sequence":"additional","affiliation":[{"name":"Leibniz University of Hannover and L3S Research Center and TIB Leibniz Information Centre 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