About https://w3id.org/scholarlydata/inproceedings/iswc-2019-demo-537

Subject: https://w3id.org/scholarlydata/inproceedings/iswc-2019-demo-537Property: http://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://purl.org/spar/fabio/ProceedingsPaperhttps://w3id.org/scholarlydata/ontology/conference-ontology.owl#InProceedingshttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#InformationObjecthttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#Objecthttp://www.w3.org/2000/01/rdf-schema#Resourcehttp://www.w3.org/2002/07/owl#Thinghttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#SocialObjectProperty: http://www.w3.org/2000/01/rdf-schema#labelPredicting Missing Links Using PyKEENProperty: http://swrc.ontoware.org/ontology#abstractPyKEEN is a framework, which integrates several approaches to compute knowledge graph embeddings (KGEs). We demonstrate the usage of PyKEEN in an biomedical use case, i.e. we trained and evaluated several KGE models on a biological knowledge graph containing genes' annotations to pathways and pathway hierarchies from well-known databases. We used the best performing model to predict new links and present an evaluation in collaboration with a domain expert. Property: http://purl.org/dc/elements/1.1/creatorhttps://w3id.org/scholarlydata/person/daniel-domingo-fernandezhttps://w3id.org/scholarlydata/person/jens-lehmannhttps://w3id.org/scholarlydata/person/charles-tapley-hoythttps://w3id.org/scholarlydata/person/mehdi-aliProperty: http://purl.org/dc/elements/1.1/subjectMachine learning Knowledge Graphs Link Prediction BioinformaticsProperty: http://purl.org/dc/elements/1.1/titlePredicting Missing Links Using PyKEENProperty: http://purl.org/ontology/bibo/authorListhttps://w3id.org/scholarlydata/authorlist/iswc-2019-demo-537Property: http://xmlns.com/foaf/0.1/makerhttps://w3id.org/scholarlydata/person/charles-tapley-hoythttps://w3id.org/scholarlydata/person/mehdi-alihttps://w3id.org/scholarlydata/person/jens-lehmannhttps://w3id.org/scholarlydata/person/daniel-domingo-fernandezProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#abstractPyKEEN is a framework, which integrates several approaches to compute knowledge graph embeddings (KGEs). We demonstrate the usage of PyKEEN in an biomedical use case, i.e. we trained and evaluated several KGE models on a biological knowledge graph containing genes' annotations to pathways and pathway hierarchies from well-known databases. We used the best performing model to predict new links and present an evaluation in collaboration with a domain expert. Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasAuthorListhttps://w3id.org/scholarlydata/authorlist/iswc-2019-demo-537Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#isPartOfhttps://w3id.org/scholarlydata/conference/iswc/2019/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#keywordMachine learning Knowledge Graphs Link Prediction BioinformaticsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#titlePredicting Missing Links Using PyKEEN
Subject: https://w3id.org/scholarlydata/conference/iswc/2019/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasParthttps://w3id.org/scholarlydata/inproceedings/iswc-2019-demo-537
Subject: https://w3id.org/scholarlydata/person/charles-tapley-hoytProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2019-demo-537
Subject: https://w3id.org/scholarlydata/person/daniel-domingo-fernandezProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2019-demo-537
Subject: https://w3id.org/scholarlydata/person/mehdi-aliProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2019-demo-537
Subject: https://w3id.org/scholarlydata/person/jens-lehmannProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2019-demo-537