| dc.contributor.author | Seneviratne, Oshani | |
| dc.contributor.author | Rashid, Sabbir | |
| dc.contributor.author | Chari, Shruthi | |
| dc.contributor.author | McCusker, Jamie | |
| dc.contributor.author | Bennett, Kristin | |
| dc.contributor.author | Hendler, Jim | |
| dc.contributor.author | McGuinness, Deborah | |
| dc.date.accessioned | 2022-02-18T02:33:59Z | |
| dc.date.available | 2022-02-18T02:33:59Z | |
| dc.date.issued | 2018-10-01 | |
| dc.identifier.other | 40 | |
| dc.identifier.uri | http://ceur-ws.org/Vol-2180/paper-59.pdf | |
| dc.identifier.uri | https://hdl.handle.net/20.500.13015/4442 | |
| dc.description.abstract | We address the problem of characterizing breast cancer, which today is done using staging guidelines. Our demo will show different breast cancer staging results that leverage the Whyis semantic nanopublication knowledge graph framework [8]. The system we developed is able to ingest breast cancer characterization guidelines in a semi-automated manner and then use our deductive inferencer to generate new information based on those guidelines as described in our ISWC resource track paper ‘Knowledge Integration for Disease Characterization: A Breast Cancer Example’ [11]. In this paper we demonstrate the versatility of our framework using a synthetic patient profile. | |
| dc.relation.uri | https://tw.rpi.edu/project/HEALS | |
| dc.subject | Health Empowerment by Analytics, Learning, and Semantics (HEALS) | |
| dc.title | Ontology-enabled Breast Cancer Characterization | |
