{"data":{"id":"10.6084/m9.figshare.3206062.v3","type":"dois","attributes":{"doi":"10.6084/m9.figshare.3206062.v3","prefix":"10.6084","suffix":"m9.figshare.3206062.v3","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Burns, Gully","nameType":"Personal","givenName":"Gully","familyName":"Burns","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1493-865X","nameIdentifierScheme":"ORCID"}]},{"name":"Ambite, Jose Luis","nameType":"Personal","givenName":"Jose Luis","familyName":"Ambite","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0087-080X","nameIdentifierScheme":"ORCID"}]},{"name":"Hermjakob, Ulf","nameType":"Personal","givenName":"Ulf","familyName":"Hermjakob","affiliation":[],"nameIdentifiers":[]},{"name":"Team, The AMR Development","nameType":"Personal","givenName":"The AMR Development","familyName":"Team","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Biomedical Abstract Meaning Representation as Linked Data (v0.8.2)"}],"publisher":"figshare","container":{},"publicationYear":2016,"subjects":[{"subject":"Cancer"},{"subject":"Molecular Biology"},{"subject":"200302 English Language","subjectScheme":"FOR"},{"subject":"FOS: Languages and literature","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Languages and literature","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"80107 Natural Language Processing","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"170203 Knowledge Representation and Machine Learning","subjectScheme":"FOR"},{"subject":"FOS: Psychology","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Psychology","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2016-10-11","dateType":"Created"},{"date":"2016-10-11","dateType":"Updated"},{"date":"2016","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["216443301 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This provides the release of an RDF-based linked data translation of the bio-AMR-v0.8 data prepared by the L2K2R2 curation team (acknowledging the original curation work by ISI/USC and SDL, and released at http://amr.isi.edu/download.html).\u003cbr\u003e\u003cbr\u003eThis corpus includes annotations of cancer-related PubMed articles, covering 3 full papers (PMID:24651010, PMID:11777939, PMID:15630473) as well as the result sections of 46 additional PubMed papers. The corpus also includes about 1000 sentences each from the BEL BioCreative training corpus and the Chicago Corpus. The Bio AMR corpus is split into dev (500 snt.), training (5,452 snt.), test (500 snt.) sets and is available as RDF translations of core AMR data and automatically-aligned AMR data. \u003cbr\u003e\u003cbr\u003eThis release contains training, development and test files with an explanatory README file. We include a written note describing the differences between the Propbank and AMR representations used in this version.\u003cbr\u003eUPDATE (10/11/2016). We have corrected some issues with grounding in the following two files:1. amr-bio-v2.grounded.txt\u003cbr\u003e2. amr-bio-v2.grounded.rdfIn this, we reconfigured our automatic grounding service to resolve certain typed entities to URLs from UNIPROT, the Gene Ontology and PubChem and linked to the best matching candidate in all cases (rather than presenting the top five). 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