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Searching for Reliable Facts over a Medical Knowledge Base
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CoreKB stores over 230,000 gene expression-cancer associations automatically generated by the CORE system.
“CoreKB stores over 230, 000 gene expression-cancer associations, automatically generated by the Collaborative Oriented Relation Extraction (CORE) system [9].”
From Provenance-driven nanopublications: representing source lineage and trust networks for multi-source assertions (2025) · cites this paper as [8]
The Introduction says CoreKB provides access to a knowledge base with more than 230,000 gene expression–cancer facts. Section 2.1 says automated methods generate facts during deployment, but also describes manual annotation during bootstrapping; the Introduction calls extraction semi-automatic. Our copy does not identify the construction system as “Collaborative Oriented Relation Extraction.” (LLM, conf=0.86)
Experimental — accuracy not yet measuredCoreKB contains information about 23,879 genes and 11,530 diseases for a total of more than 230,000 fine-grained facts supported by 1,037,845 sentences from 251,038 research articles.
“CoreKB contains information about 23, 879 genes and 11, 530 diseases for a total of more than 230, 000 fine-grained facts supported by 1, 037, 845 sentences from 251, 038 research articles.”
From Provenance-driven nanopublications: representing source lineage and trust networks for multi-source assertions (2025) · cites this paper as [8]
Each GCS is linked to the sentences supporting the fact and those conflicting with it.
“In addition, each GCS is linked to the sentences supporting the fact, i.e., identifying the same gene class, and those conflicting with it.”
From Provenance-driven nanopublications: representing source lineage and trust networks for multi-source assertions (2025) · cites this paper as [8]
Each GCS comprises information about the gene and disease involved, identified by NCBI Gene IDs and UMLS CUIs, together with the assigned gene class.
“Each GCS comprises information about the gene and disease involved, which are identified by the National Center for Biotechnology Information (NCBI) Gene IDs and the Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs) respectively, together with the assigned gene class.”
From Provenance-driven nanopublications: representing source lineage and trust networks for multi-source assertions (2025) · cites this paper as [8]
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Citations
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Research works printed but not linked: 15
- 10.1145/3417996 · DOI
- 10.1561/1500000032 · DOI
- M. Biryukov, V. Grouès, and V. P. Satagopam. 2017. BioKB - Text mining and semantic technologies for the biomedical content discovery. In Proc. of the 10th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4LS 2017), Rome, Italy, December 4-7, 2017 (CEUR Workshop Proceedings, Vol. 2042). CEUR-WS.org. http://ceur-ws.org/Vol-2042/paper5.pdf
- 10.1007/s12687-017-0331-7 · DOI
- 10.1200/CCI.19.00117 · DOI
- 10.1038/nrd.2017.226 · DOI · Paper page
- J. Piñero González, J. M. Ramírez-Anguita, J. Saüch-Pitarch, F. Ronzano, E. Centeno, F. Sanz, and L. I. Furlong. 2020. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res., Vol. 48, Database-Issue (2020), D845-D855.
- S. Gupta, H. Dingerdissen, K. E. Ross, Y. Hu, C. H. Wu, R. Mazumder, and K. Vijay-Shanker. 2018. DEXTER: Disease-Expression Relation Extraction from Text. Database J. Biol. Databases Curation, Vol. 2018 (2018), bay045.
- H. J. Lee, T. Cuong Dang, H. Lee, and J. C. Park. 2014. OncoSearch: cancer gene search engine with literature evidence. Nucleic Acids Res., Vol. 42, Webserver-Issue (2014), 416--421.
- 10.3389/fcell.2020.00048 · DOI
- 10.1093/bib/bbw114 · DOI
- 10.1186/s12859-022-04646-6 · DOI
- 10.1038/s41598-021-84211-y · DOI
- 10.1561/1900000064 · DOI
- 10.1145/2452376.2452394 · DOI
Cited by
Sorted by citations
Menotti, L., Marchesin, S., Giachelle, F., & Silvello, G. (2025) · International Journal on Digital Libraries · doi:10.1007/s00799-025-00431-x · Cited by 5
Self-citation
Metadata
- Authors
- Giachelle, F., Marchesin, S., Silvello, G., & Alonso, O.
- Venue
- Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2023-07-18
- Pages
- 3205-3209
- Cited by
- 7
- References
- 0
- Funders
- Horizon 2020 Framework Programme (825292)
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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.
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