Mohnish Dubey

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Natural Language Query Formalization involves semantically parsing queries in natural language and translating them into their corresponding formal representations. It is a key component for developing question-answering (QA) systems on RDF data. The chosen formal representation language in this case is often SPARQL. In this paper, we propose a framework,(More)
Being able to access knowledge bases 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(More)
Knowledge graphs, usually modelled via RDF or property graphs, have gained importance over the past decade. In order to decide which Data Management Solution (DMS) performs best for speci c query loads over a knowledge graph, it is required to perform benchmarks. Benchmarking is an extremely tedious task demanding repetitive manual e ort, therefore it is(More)
Developments in the context of Open, Big, and Linked Data have led to an enormous growth of structured data on the Web. To keep up with the pace of efficient consumption and management of the data at this rate, many data management solutions have been developed for specific tasks and applications. We present LITMUS, a framework for benchmarking data(More)
A large scale question answering dataset has a potential to enable development of robust and more accurate question answering systems. In this direction, we introduce a framework for creating such datasets which decreases the manual intervention and domain expertise traditionally needed. We describe in details the architecture and the design decision we(More)
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