anhaidgroup/polaris-wikitables-v2

WikiTables v2

WikiTables is one of six datasets in Polaris: Learning to Generate Table Descriptions from Retrieval Feedback, alongside aw, arctic, lter, ecir, and wtr.

It holds 3,361 tables scraped from Wikipedia articles and 57 keyword queries over them. For each query–table pair, a person scored how well that table answers that query; those scores are the relevance judgments, and they live in qrels.csv.

The tables have no names. What describes a table is its column names and the page context around it — the article title, the section heading, and the caption.

Each Polaris dataset comes in two versions. v2, this one, adds the tuples — the rows of each table, in tuples.zip — on top of the table metadata, the queries, and the relevance judgments. v1 is the same without the tuples and is at polaris-wikitables-v1.

Files

queries.csv — one row per query. Note that query_id is a plain integer here, not the q1 form used by aw, arctic, lter, and ecir.

query_id,query
1,world interest rates table
2,2008 beijing olympics

qrels.csv — one row per query–table pair that a person scored, graded 0, 1, or 2.

query_id,table_id,relevance_score
1,table-0370-614,2

Judgments came with the source benchmark rather than being made here, and they cover a candidate pool larger than the 3,361 tables released, so 1,841 rows point at tables that are not in metadata.csv. All 1,841 are grade 0, so every gold table is present. Drop them if your loader expects every table_id to resolve.

metadata.csv — one row per table, here the table above.

table_id,column_names,table_context
table-0370-614,"[""Date"", ""Deposit facility"", ""Main refinancing operations"", ""Marginal lending facility""]","{""pgTitle"": ""Eurozone"", ""secondTitle"": ""Interest rates"", ""caption"": ""Interest rates""}"

tuples.zip — one CSV per table, at Tuples/<table_id>.csv. table-0370-614 is the table that scores 2 for query 1. Its first rows:

Date,Deposit facility,Main refinancing operations,Marginal lending facility
1999-01-01,2.00,3.00,4.50
1999-01-04 The ECB announced...,2.75,3.00,3.25
1999-01-22,2.00,3.00,4.50

Each file's header matches that table's column_names. Tables were capped at 500,000 rows; none came close to that cap.

Statistics

A table counts as gold if it scores above 0 for at least one query.

Statistic	Value
Domain	Web
Tables	3,361
Queries	57
Gold tables	845
Relevant tables per query	min 1, max 59, average 14.9
Metadata fields	column names, table context
Relevance	graded, 0 to 2
Tuples	62,982 rows across the tables
Rows per table	median 9, min 0, max 1,396
tuples.zip	2.3 MB, 4.7 MB unzipped

Download

The repo is about 3.3 MB, almost all of it tuples.zip, which unpacks to 4.7 MB. You do not need a Hugging Face account to download it.

Option 1 — click the Files tab at the top of this page and save each file.

Option 2 — command line (recommended):

pip install huggingface_hub
hf download anhaidgroup/polaris-wikitables-v2 --repo-type dataset --local-dir wikitables

Polaris has six datasets in total and WikiTables is one of them. Each sits in its own repository, so to download all six quickly — the v2 repositories, with tuples, about 4.8 GB in total:

for d in aw arctic lter ecir wikitables wtr; do
  hf download anhaidgroup/polaris-$d-v2 --repo-type dataset --local-dir polaris_v2/$d
done

Usage

column_names and table_context are an array and an object, so they need parsing when you load the file. For example:

import ast
import pandas as pd

metadata = pd.read_csv("wikitables/metadata.csv")
queries = pd.read_csv("wikitables/queries.csv", dtype={"query_id": str})
qrels = pd.read_csv("wikitables/qrels.csv", dtype={"query_id": str})

metadata["columns"] = metadata["column_names"].apply(ast.literal_eval)
metadata["context"] = metadata["table_context"].apply(ast.literal_eval)

relevant = qrels.loc[(qrels.query_id == "1") & (qrels.relevance_score > 0), "table_id"].tolist()

The dtype={"query_id": str} keeps the ids as strings, so the same code works across all six datasets.

To read one table's tuples without unpacking the archive:

import zipfile

with zipfile.ZipFile("wikitables/tuples.zip") as z:
    with z.open("Tuples/table-0370-614.csv") as f:
        rows = pd.read_csv(f)

How is this dataset created?

The tables come from a corpus of Wikipedia tables that Bhagavatula et al. (ISWC 2015) assembled for entity linking. The retrieval benchmark built over it pairs 60 queries with 1.6 million tables and scores each pair 0, 1, or 2: irrelevant, partially relevant, fully relevant. A Wikipedia table has no title of its own, so what identifies it here is its column names and the page it sits on.

Producing a description for 1.6 million tables was not practical, so the Polaris authors cut the corpus down. For each query they ran BM25 over the column names and page context, took the 50 highest-scoring tables, and then added back every table already scored above 0.

That leaves the corpus harder than a random sample would, not easier. What survives is every relevant table plus the non-relevant tables that BM25 ranks highest — the ones a system is most likely to confuse them with. Queries left without a single relevant table were dropped, giving 3,361 tables and 57 queries.

Citation

@misc{cai2026polaris,
  title  = {Polaris: Learning to Generate Table Descriptions from Retrieval Feedback},
  author = {Cai, Ting and Phan, Tuan Minh and Doan, AnHai},
  year   = {2026}
}

Please also cite the corpus and the benchmark it is built on:

@inproceedings{bhagavatula2015tabel,
  author    = {Chandra Sekhar Bhagavatula and Thanapon Noraset and Doug Downey},
  title     = {{TabEL}: Entity Linking in Web Tables},
  booktitle = {Proceedings of the International Semantic Web Conference ({ISWC})},
  pages     = {425--441},
  year      = {2015},
}

@inproceedings{zhang2018adhoc,
  author    = {Shuo Zhang and Krisztian Balog},
  title     = {Ad Hoc Table Retrieval Using Semantic Similarity},
  booktitle = {Proceedings of the Web Conference ({WWW})},
  pages     = {1553--1562},
  year      = {2018},
}

License

What	Comes from	License
Table metadata and tuples	Wikipedia, via the TabEL corpus	CC BY-SA 4.0 and GFDL, so credit Wikipedia, say what you changed, and release anything derived from it under the same terms
Queries and relevance judgments	Zhang and Balog (WWW 2018)	No licence is stated on the source repository, so there is no explicit grant — cite them as above

Contact

Email minhrua@cs.wisc.edu, valid until May 2029. After that, email anhai@cs.wisc.edu.