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Our approach learns to answer questions based on graph isomorphisms from basic graph patterns of SPARQL queries. ... Knowledge Graph Question Answering (KGQA) systems are based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that ... We acknowledge the support of the Federal Ministry for Economic Affairs and Energy (BMWi) project SPEAKER (FKZ 01MK20011A), ScaDS.AI (01/S18026A) as well as the Fraunhofer Zukunftsstiftung project JOSEPH ...arXiv:2103.06752v1 fatcat:v2pv7z3lyrdszhwrvdpu4n2pom
We present the techniques used by the QA systems which were evaluated on a popular series of benchmarks: Question Answering over Linked Data (QALD). ... To make this information available, many question answering (QA) systems over KBs were created in the last years. ... Query construction using machine learning CASIA ) uses a machine learning approach for the whole QA process. ...doi:10.1007/s10115-017-1100-y fatcat:dlt5tnm3fzbjtdiqq63urkcfhe
Although several question answering benchmarks can be used to evaluate question-answering systems over a number of popular knowledge graphs, choosing a benchmark to accurately assess the quality of a question ... In this paper, we introduce CBench, an extensible, and more informative benchmarking suite for analyzing benchmarks and evaluating question answering systems. ... SPARQL templates are automatically generated and are converted into natural question templates. These general templates are manually transformed into natural language questions. ...arXiv:2105.00811v1 fatcat:dkf4hi6dlbbr3cc22clvml6ppa