Open Government Data Corpus for Table Search

TL;DR

This paper introduces a new benchmark dataset for large-scale table search using open government data, addressing the gap in existing benchmarks that focus on display tables, and demonstrates relatedness detection methods.

Contribution

It creates the first dataset for benchmarking table search over open government data, including three relatedness notions and automatic keyword search, with baseline evaluations.

Findings

New dataset for large-scale table search benchmarking.

Three relatedness notions demonstrated: same organization, same dataset, overlapping tags.

Baseline results provided for traditional and neural methods.

Abstract

Increasing amounts of structured data can provide value for research and business if the relevant data can be located. Often the data is in a data lake without a consistent schema, making locating useful data challenging. Table search is a growing research area, but existing benchmarks have been limited to displayed tables. Tables sized and formatted for display in a Wikipedia page or ArXiv paper are considerably different from data tables in both scale and style. By using metadata associated with open data from government portals, we create the first dataset to benchmark search over data tables at scale. We demonstrate three styles of table-to-table related table search. The three notions of table relatedness are: tables produced by the same organization, tables distributed as part of the same dataset, and tables with a high degree of overlap in the annotated tags. The keyword tags provided with the metadata also permit the automatic creation of a keyword search over tables benchmark. We provide baselines on this dataset using existing methods including traditional and neural approaches.