About https://w3id.org/scholarlydata/inproceedings/iswc-2019-research-270

Subject: https://w3id.org/scholarlydata/inproceedings/iswc-2019-research-270Property: http://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.w3.org/2002/07/owl#Thinghttp://purl.org/spar/fabio/ProceedingsPaperhttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#SocialObjecthttps://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#ResourceProperty: http://www.w3.org/2000/01/rdf-schema#labelUsing a KG-Copy Network for Non-Goal Oriented DialoguesProperty: http://swrc.ontoware.org/ontology#abstractNon-goal oriented, generative dialogue systems lack the ability to generate answers with grounded facts. A knowledge graph can be considered an abstraction of the real world consisting of well-grounded facts. This paper addresses the problem of generating well-grounded responses by integrating knowledge graphs into the dialogue systems response generation process, in an end-to-end manner. A dataset for non-goal oriented dialogues is proposed in this paper in the domain of soccer, conversing on different clubs and national teams along with a knowledge graph for each of these teams. A novel neural network architecture is also proposed as a baseline on this dataset, which can integrate knowledge graphs into the response generation process, producing well articulated, knowledge grounded responses. Empirical evidence suggests that the proposed model performs better than other state-of-the-art models for knowledge graph integrated dialogue systems.Property: http://purl.org/dc/elements/1.1/creatorhttps://w3id.org/scholarlydata/person/simon-kwoczekhttps://w3id.org/scholarlydata/person/md-rashad-al-hasan-ronyhttps://w3id.org/scholarlydata/person/jens-lehmannhttps://w3id.org/scholarlydata/person/debanjan-chaudhuriProperty: http://purl.org/dc/elements/1.1/subjectdialogue systems knowledge graphs question answering neural networks knowledge-driven conversationProperty: http://purl.org/dc/elements/1.1/titleUsing a KG-Copy Network for Non-Goal Oriented DialoguesProperty: http://purl.org/ontology/bibo/authorListhttps://w3id.org/scholarlydata/authorlist/iswc-2019-research-270Property: http://xmlns.com/foaf/0.1/makerhttps://w3id.org/scholarlydata/person/jens-lehmannhttps://w3id.org/scholarlydata/person/md-rashad-al-hasan-ronyhttps://w3id.org/scholarlydata/person/debanjan-chaudhurihttps://w3id.org/scholarlydata/person/simon-kwoczekProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#abstractNon-goal oriented, generative dialogue systems lack the ability to generate answers with grounded facts. A knowledge graph can be considered an abstraction of the real world consisting of well-grounded facts. This paper addresses the problem of generating well-grounded responses by integrating knowledge graphs into the dialogue systems response generation process, in an end-to-end manner. A dataset for non-goal oriented dialogues is proposed in this paper in the domain of soccer, conversing on different clubs and national teams along with a knowledge graph for each of these teams. A novel neural network architecture is also proposed as a baseline on this dataset, which can integrate knowledge graphs into the response generation process, producing well articulated, knowledge grounded responses. Empirical evidence suggests that the proposed model performs better than other state-of-the-art models for knowledge graph integrated dialogue systems.Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasAuthorListhttps://w3id.org/scholarlydata/authorlist/iswc-2019-research-270Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#isPartOfhttps://w3id.org/scholarlydata/conference/iswc/2019/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#keyworddialogue systems knowledge graphs question answering neural networks knowledge-driven conversationProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#titleUsing a KG-Copy Network for Non-Goal Oriented DialoguesProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#relatesToEventhttps://w3id.org/scholarlydata/talk/iswc-2019-research-270
Subject: https://w3id.org/scholarlydata/conference/iswc/2019/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasParthttps://w3id.org/scholarlydata/inproceedings/iswc-2019-research-270
Subject: https://w3id.org/scholarlydata/person/md-rashad-al-hasan-ronyProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2019-research-270
Subject: https://w3id.org/scholarlydata/person/simon-kwoczekProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2019-research-270
Subject: https://w3id.org/scholarlydata/talk/iswc-2019-research-270Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#isEventRelatedTohttps://w3id.org/scholarlydata/inproceedings/iswc-2019-research-270
Subject: https://w3id.org/scholarlydata/person/jens-lehmannProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2019-research-270
Subject: https://w3id.org/scholarlydata/person/debanjan-chaudhuriProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2019-research-270