About https://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-219

Subject: https://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-219Property: http://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.w3.org/2002/07/owl#Thinghttp://purl.org/spar/fabio/ProceedingsPaperhttps://w3id.org/scholarlydata/ontology/conference-ontology.owl#InProceedingshttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#SocialObjecthttp://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#labelQaldGen: Towards Microbenchmarking of Question Answering Systems Over Knowledge GraphsProperty: http://swrc.ontoware.org/ontology#abstractOver the last years, a number of Linked Data-based Question Answering (QA) systems have been developed. Consequently, a serious of Question Answering Over Linked Data (QALD1-QALD9) challenges and other datasets have been proposed to evaluate these systems. However, the QA datasets contain a fixed number of natural language questions and do not allow users to generate micro benchmarks tailored towards specific use-cases. We propose QaldGen, a natural language benchmark generation framework for Linked Data which is able to generate customised QA benchmarks from existing QA repositories. The framework is flexible enough to generate benchmarks of varying sizes and according to the user-defined criteria on the most important features to be considered for QA benchmarking. This is achieved using different clustering algorithms. We compare state-of-the-art QA systems over knowledge graphs by using different QA benchmarks. The observed results show that specialised micro-benchmarking is important to pinpoint the limitations of the various components of QA systems.Property: http://purl.org/dc/elements/1.1/creatorhttps://w3id.org/scholarlydata/person/felix-conradshttps://w3id.org/scholarlydata/person/abhishek-nadgerihttps://w3id.org/scholarlydata/person/kuldeep-singhhttps://w3id.org/scholarlydata/person/muhammad-saleemhttps://w3id.org/scholarlydata/person/jens-lehmannhttps://w3id.org/scholarlydata/person/jeff-z-panhttps://w3id.org/scholarlydata/person/axel-cyrille-ngonga-ngomoProperty: http://purl.org/dc/elements/1.1/subjectQuestion Answering Knowledge Graphs Micro Benchmarking ReusabilityProperty: http://purl.org/dc/elements/1.1/titleQaldGen: Towards Microbenchmarking of Question Answering Systems Over Knowledge GraphsProperty: http://purl.org/ontology/bibo/authorListhttps://w3id.org/scholarlydata/authorlist/iswc-2019-resource-219Property: http://xmlns.com/foaf/0.1/makerhttps://w3id.org/scholarlydata/person/axel-cyrille-ngonga-ngomohttps://w3id.org/scholarlydata/person/jens-lehmannhttps://w3id.org/scholarlydata/person/muhammad-saleemhttps://w3id.org/scholarlydata/person/kuldeep-singhhttps://w3id.org/scholarlydata/person/felix-conradshttps://w3id.org/scholarlydata/person/jeff-z-panhttps://w3id.org/scholarlydata/person/abhishek-nadgeriProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#abstractOver the last years, a number of Linked Data-based Question Answering (QA) systems have been developed. Consequently, a serious of Question Answering Over Linked Data (QALD1-QALD9) challenges and other datasets have been proposed to evaluate these systems. However, the QA datasets contain a fixed number of natural language questions and do not allow users to generate micro benchmarks tailored towards specific use-cases. We propose QaldGen, a natural language benchmark generation framework for Linked Data which is able to generate customised QA benchmarks from existing QA repositories. The framework is flexible enough to generate benchmarks of varying sizes and according to the user-defined criteria on the most important features to be considered for QA benchmarking. This is achieved using different clustering algorithms. We compare state-of-the-art QA systems over knowledge graphs by using different QA benchmarks. The observed results show that specialised micro-benchmarking is important to pinpoint the limitations of the various components of QA systems.Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasAuthorListhttps://w3id.org/scholarlydata/authorlist/iswc-2019-resource-219Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#isPartOfhttps://w3id.org/scholarlydata/conference/iswc/2019/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#keywordQuestion Answering Knowledge Graphs Micro Benchmarking ReusabilityProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#titleQaldGen: Towards Microbenchmarking of Question Answering Systems Over Knowledge GraphsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#relatesToEventhttps://w3id.org/scholarlydata/talk/iswc-2019-resource-219
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