Welcome to the website of GitTables!
Processing table files
Each table is stored in a Parquet file which can be easily processed by different data processing libraries like Pandas, Spark, and Pyarrow. Each Parquet file consists of the table itself and its metadata.
The table and metadata can be extracted using Pyarrow as follows:
Please be aware that this dataset is uncurated, hence the underlying data files might exhibit sensitive, harmful or otherwise undesired data. The spread and exact replication of such content should be avoided, please report any such observations so that we can remove these files accordingly.
It is also important to assess derived artefacts on the presence of any negative bias before deploying or publishing them. In case harmful biases are observed we would like to be notified so that we can mitigate these problems and improve our guidelines for using GitTables. You can report this through the contact form on https://gittables.github.io.
The code used to extract, parse and annotate the tables from CSV files can be found in this GitHub repository. For details on these procedures we recommend reading the paper. The table corpus itself is difficult to replicate exactly due to the dependency on the unstable GitHub Search API: a search query today yields different results than the same query tomorrow.
The analysis presented in the paper has been conducted on the GitTables 1.7M subset of GitTables, which consists of 1.7M tables. The GitHub repository provides Jupyter notebooks that were used for the analyses and experiments.
To understand the quality of the column annotations obtained by the GitTables annotation pipeline versus the human-labeled annotations, we annotated the human-labeled T2Dv2 benchmark using the DBpedia ontology and our annotation pipelines. For the semantic pipieline and syntactic pipeline we find the same annotation as T2Dv2 in 54% and 61% of the table columns. We manually reviewed the deviating annotations for both pipelines and find that the GitTables annotations (based on the DBpedia ontology as on 28-05-2021) are more accurate in many cases. Our manual reviews (n=3) can be downloaded through this link.
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