About https://w3id.org/scholarlydata/inproceedings/iswc-2017-iswc-2017-resources-152

Subject: https://w3id.org/scholarlydata/inproceedings/iswc-2017-iswc-2017-resources-152Property: http://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.w3.org/2002/07/owl#Thinghttps://w3id.org/scholarlydata/ontology/conference-ontology.owl#InProceedingshttp://purl.org/spar/fabio/ProceedingsPaperhttp://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.ontologydesignpatterns.org/ont/dul/DUL.owl#SocialObjectProperty: http://www.w3.org/2000/01/rdf-schema#labelA Corpus for Complex Question Answering over Knowledge GraphsProperty: http://www.w3.org/2002/07/owl#sameAshttp://data.semanticweb.org/conference/iswc-2017-iswc-2017-resources-152Property: http://swrc.ontoware.org/ontology#abstractBeing able to access knowledge graphs in an intuitive way has been an active area of research over the past years. In particular, several question answering (QA) approaches which allow to query RDF datasets in natural language have been developed as they allow end users to access knowledge without needing to learn the schema of a knowledge base and learn a formal query language. To foster this research area, several training datasets have been created, e.g. in the QALD (Question Answering over Linked Data) initiative. However, existing datasets are insufficient in terms of size, variety or complexity to apply and evaluate a range of machine learning based QA approaches for learning complex SPARQL queries. With the provision of the Large-Scale Complex Question Answering Dataset (LC-QuAD), we close this gap by providing a dataset with 5000 questions and their corresponding SPARQL queries over the DBpedia dataset. In this article, we describe the dataset creation process and how we ensure a high variety of questions, which should enable to assess the robustness and accuracy of the next generation of QA systems for knowledge graphs.Property: http://purl.org/dc/elements/1.1/creatorhttps://w3id.org/scholarlydata/person/gaurav-maheshwarihttps://w3id.org/scholarlydata/person/mohnish-dubeyhttps://w3id.org/scholarlydata/person/priyansh-trivedihttps://w3id.org/scholarlydata/person/jens-lehmannProperty: http://purl.org/dc/elements/1.1/subjectQuestion GenerationQuestion Answering DatasetComplex QuestionProperty: http://purl.org/dc/elements/1.1/titleA Corpus for Complex Question Answering over Knowledge GraphsProperty: http://purl.org/ontology/bibo/authorListhttps://w3id.org/scholarlydata/authorlist/iswc-2017-iswc-2017-resources-152Property: http://xmlns.com/foaf/0.1/makerhttps://w3id.org/scholarlydata/person/priyansh-trivedihttps://w3id.org/scholarlydata/person/mohnish-dubeyhttps://w3id.org/scholarlydata/person/jens-lehmannhttps://w3id.org/scholarlydata/person/gaurav-maheshwariProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#abstractBeing able to access knowledge graphs in an intuitive way has been an active area of research over the past years. In particular, several question answering (QA) approaches which allow to query RDF datasets in natural language have been developed as they allow end users to access knowledge without needing to learn the schema of a knowledge base and learn a formal query language. To foster this research area, several training datasets have been created, e.g. in the QALD (Question Answering over Linked Data) initiative. However, existing datasets are insufficient in terms of size, variety or complexity to apply and evaluate a range of machine learning based QA approaches for learning complex SPARQL queries. With the provision of the Large-Scale Complex Question Answering Dataset (LC-QuAD), we close this gap by providing a dataset with 5000 questions and their corresponding SPARQL queries over the DBpedia dataset. In this article, we describe the dataset creation process and how we ensure a high variety of questions, which should enable to assess the robustness and accuracy of the next generation of QA systems for knowledge graphs.Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasAuthorListhttps://w3id.org/scholarlydata/authorlist/iswc-2017-iswc-2017-resources-152Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#isPartOfhttps://w3id.org/scholarlydata/conference/iswc/2017/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#keywordQuestion Answering DatasetQuestion GenerationComplex QuestionProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#titleA Corpus for Complex Question Answering over Knowledge Graphs
Subject: https://w3id.org/scholarlydata/person/jens-lehmannProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2017-iswc-2017-resources-152
Subject: https://w3id.org/scholarlydata/person/mohnish-dubeyProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2017-iswc-2017-resources-152
Subject: https://w3id.org/scholarlydata/conference/iswc/2017/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasParthttps://w3id.org/scholarlydata/inproceedings/iswc-2017-iswc-2017-resources-152
Subject: https://w3id.org/scholarlydata/person/gaurav-maheshwariProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2017-iswc-2017-resources-152
Subject: https://w3id.org/scholarlydata/person/priyansh-trivediProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2017-iswc-2017-resources-152