Ontologies and Knowledge Graphs in Oncology Research
Open accessAbstract
The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.
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Ontology-based systems can effectively merge disparate clinical notions and serve as a single source of standardized reasoning and inference across different datasets.
“Ontology-based systems can effectively merge disparate clinical notions and therefore serve as a single source of standardized reasoning and inference across different datasets49.”
From K-STAMM: a knowledge-enhanced spatial - temporal attention model with multimodal fusion for pneumonia prediction (2026) · cites this paper as [49]
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Flagged first, then by citations
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Sorted by citations
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“Such systems that integrate deep learning with structured expert knowledge are more transparent and can more effectively show the user the evidence for the decision, ensuring that the AI forecasts correspond with the clinical procedures 49 .”
- Authors
- Silva, M. C., Eugénio, P., Faria, D., & Pesquita, C.
- Affiliations
- University of Lisbon (PT)
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
- Corresponding author
- Silva, Marta Contreiras
- Venue
- Cancers
- Publisher
- MDPI AG
- Published
- 2022-04-10
- Volume
- 14
- Issue
- 8
- Pages
- 1906
- ISSN
- 2072-6694 (ISSN-L)
- PMC
- PMC9029532
- OpenAlex
- W4223568044
- Cited by
- 46
- References
- 152
- Citations by year
- 2026: 10 · 2025: 14 · 2024: 17 · 2023: 4 · 2022: 1
- FWCI
- 0.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cancers (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fundação para a Ciência e Tecnologia (UIDB/00408/2020, UIDP/00408/2020)
- European Union’s Horizon 2020 (No 101017453)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- biomedical ontologies, knowledge graphs, cancer research, precision oncology, heterogeneous data integration, data interoperability, knowledge representation, artificial intelligence in oncology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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