CubeQA—question answering on RDF data cubes

Abstract

Statistical data in the form of RDF Data Cubes is becoming increasingly valuable as it influences decisions in areas such as health care, policy and finance. While a growing amount is becoming freely available through the open data movement, this data is opaque to laypersons. Semantic Question Answering (SQA) technologies provide intuitive access via free-form natural language queries but general SQA systems cannot process RDF Data Cubes. On the intersection between RDF Data Cubes and SQA, we create a new subfield of SQA, called RDCQA. We create an RDQCA benchmark as task 3 of the QALD-6 evaluation challenge, to stimulate further research and enable quantitative comparison between RDCQA systems. We design and evaluate the domain independent CubeQA algorithm, which is the first RDCQA system and achieves a global F1 score of 0.43 on the QALD6T3-test benchmark, showing that RDCQA is feasible.