About https://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-123

Subject: https://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-123Property: http://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://purl.org/spar/fabio/ProceedingsPaperhttp://www.w3.org/2002/07/owl#Thinghttp://www.w3.org/2000/01/rdf-schema#Resourcehttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#InformationObjecthttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#Objecthttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#SocialObjecthttps://w3id.org/scholarlydata/ontology/conference-ontology.owl#InProceedingsProperty: http://www.w3.org/2000/01/rdf-schema#labelThe KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and TransferabilityProperty: http://swrc.ontoware.org/ontology#abstractThere is an emerging trend of embedding knowledge graphs (KGs) in continuous vector spaces in order to use those for machine learning tasks. Recently, many knowledge graph embedding (KGE) mod- els have been proposed that learn low dimensional representations while trying to maintain the structural properties of the KGs such as the sim- ilarity of nodes depending on their edges to other nodes. KGEs can be used to address tasks within KGs such as the prediction of novel links and the disambiguation of entities. They can also be used for downstream tasks like question answering and fact-checking. Overall, these tasks are relevant for the semantic web community. Despite their popularity, the reproducibility of KGE experiments and the transferability of proposed KGE models to research fields outside the machine learning community can be a major challenge. Therefore, we present the KEEN Universe, an ecosystem for knowledge graph embeddings that we have developed with a strong focus on reproducibility and transferability. The KEEN Universe currently consists of the Python packages PyKEEN (Python KnowlEdge Graph EmbeddiNgs), BioKEEN (Biological KnowlEdge Graph Embed- diNgs), and the KEEN Model Zoo for sharing trained KGE models with the community.Property: http://purl.org/dc/elements/1.1/creatorhttps://w3id.org/scholarlydata/person/mehdi-alihttps://w3id.org/scholarlydata/person/jens-lehmannhttps://w3id.org/scholarlydata/person/hajira-jabeenhttps://w3id.org/scholarlydata/person/charles-tapley-hoytProperty: http://purl.org/dc/elements/1.1/subjectSemantic Web Machine Learning Knowledge Graphs Knowledge Graph EmbeddingsProperty: http://purl.org/dc/elements/1.1/titleThe KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and TransferabilityProperty: http://purl.org/ontology/bibo/authorListhttps://w3id.org/scholarlydata/authorlist/iswc-2019-resource-123Property: http://xmlns.com/foaf/0.1/makerhttps://w3id.org/scholarlydata/person/charles-tapley-hoythttps://w3id.org/scholarlydata/person/hajira-jabeenhttps://w3id.org/scholarlydata/person/jens-lehmannhttps://w3id.org/scholarlydata/person/mehdi-aliProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#abstractThere is an emerging trend of embedding knowledge graphs (KGs) in continuous vector spaces in order to use those for machine learning tasks. Recently, many knowledge graph embedding (KGE) mod- els have been proposed that learn low dimensional representations while trying to maintain the structural properties of the KGs such as the sim- ilarity of nodes depending on their edges to other nodes. KGEs can be used to address tasks within KGs such as the prediction of novel links and the disambiguation of entities. They can also be used for downstream tasks like question answering and fact-checking. Overall, these tasks are relevant for the semantic web community. Despite their popularity, the reproducibility of KGE experiments and the transferability of proposed KGE models to research fields outside the machine learning community can be a major challenge. Therefore, we present the KEEN Universe, an ecosystem for knowledge graph embeddings that we have developed with a strong focus on reproducibility and transferability. The KEEN Universe currently consists of the Python packages PyKEEN (Python KnowlEdge Graph EmbeddiNgs), BioKEEN (Biological KnowlEdge Graph Embed- diNgs), and the KEEN Model Zoo for sharing trained KGE models with the community.Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasAuthorListhttps://w3id.org/scholarlydata/authorlist/iswc-2019-resource-123Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#isPartOfhttps://w3id.org/scholarlydata/conference/iswc/2019/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#keywordSemantic Web Machine Learning Knowledge Graphs Knowledge Graph EmbeddingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#titleThe KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and TransferabilityProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#relatesToEventhttps://w3id.org/scholarlydata/talk/iswc-2019-resource-123
Subject: https://w3id.org/scholarlydata/conference/iswc/2019/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasParthttps://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-123
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Subject: https://w3id.org/scholarlydata/person/mehdi-aliProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-123
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Subject: https://w3id.org/scholarlydata/person/jens-lehmannProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-123
Subject: https://w3id.org/scholarlydata/person/hajira-jabeenProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-123