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Tables are common and important in scientific documents, yet most text-baseddocument search systems do not capture structures and semantics specific totables. How to bridge different types of mismatch between keywords queries andscientific tables and what influences ranking quality needs to be carefullyinvestigated. This paper considers the structure of tables and gives differentemphasis to table components. On the query side, thanks to external knowledgesuch as knowledge bases and ontologies, key concepts are extracted and used tobuild structured queries, and target quantity types are identified and used toexpand original queries. A probabilistic framework is proposed to incorporatestructural and semantic information from both query and table sides. We alsoconstruct and release TableArXiv, a high quality dataset with 105 queries andcorresponding relevance judgements for scientific table search. Experimentsdemonstrate significantly higher accuracy overall and at the top of therankings than several baseline methods.

Scientific Table Search Using Keyword Queries