  Tables are common and important in scientific documents, yet most text-based
document search systems do not capture structures and semantics specific to
tables. How to bridge different types of mismatch between keywords queries and
scientific tables and what influences ranking quality needs to be carefully
investigated. This paper considers the structure of tables and gives different
emphasis to table components. On the query side, thanks to external knowledge
such as knowledge bases and ontologies, key concepts are extracted and used to
build structured queries, and target quantity types are identified and used to
expand original queries. A probabilistic framework is proposed to incorporate
structural and semantic information from both query and table sides. We also
construct and release TableArXiv, a high quality dataset with 105 queries and
corresponding relevance judgements for scientific table search. Experiments
demonstrate significantly higher accuracy overall and at the top of the
rankings than several baseline methods.
