Update sotab.py

sotab.py

@@ -20,51 +20,22 @@ import datasets
 
 # Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
-
+@inproceedings{madoc63868, pages = {14--19}, booktitle = {SemTab 2022 : Proceedings of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, co-located with the 21st International semantic Web Conference (ISWC 2022), virtual conference, October 23-27, 2022}, address = {Aachen, Germany}, editor = {Vasilis Efthymiou and Ernesto Jim{\'e}nez-Ruiz and Jiaoyan Chen and Vincenzo Cutrona and Oktie Hassanzadeh and Juan Sequeda and Kavitha Srinivas and Nora Abdelmageed and Madelon Hulsebos}, journal = {CEUR Workshop Proceedings}, year = {2022}, title = {SOTAB: The WDC Schema.org table annotation benchmark}, publisher = {RWTH Aachen}, language = {Englisch}, author = {Keti Korini and Ralph Peeters and Christian Bizer}, volume = {3320}, abstract = {Understanding the semantics of table elements is a prerequisite for many data integration and data discovery tasks. Table annotation is the task of labeling table elements with terms from a given vocabulary. This paper presents the WDC Schema.org Table Annotation Benchmark (SOTAB) for comparing the performance of table annotation systems. SOTAB covers the column type annotation (CTA) and columns property annotation (CPA) tasks. SOTAB provides {$\sim$}50,000 annotated tables for each of the tasks containing Schema.org data from different websites. The tables cover 17 different types of entities such as movie, event, local business, recipe, job posting, or product. The tables stem from the WDC Schema.org Table Corpus which was created by extracting Schema.org annotations from the Common Crawl. Consequently, the labels used for annotating columns in SOTAB are part of the Schema.org vocabulary. The benchmark covers 91 types for CTA and 176 properties for CPA distributed across textual, numerical and date/time columns. The tables are split into fixed training, validation and test sets. The test sets are further divided into subsets focusing on specific challenges, such as columns with missing values or different value formats, in order to allow a more fine-grained comparison of annotation systems. The evaluation of SOTAB using Doduo and TURL shows that the benchmark is difficult to solve for current state-of-the-art systems.}, url = {https://madoc.bib.uni-mannheim.de/63868/} }
 """
 
 # You can copy an official description
 _DESCRIPTION = """\
+Understanding the semantics of table elements is a prerequisite for many data integration and data discovery tasks. Table annotation is the task of labeling table elements with terms from a given vocabulary. This paper presents the WDC Schema.org Table Annotation Benchmark (SOTAB) for comparing the performance of table annotation systems. SOTAB covers the column type annotation (CTA) and columns property annotation (CPA) tasks. SOTAB provides ∼50,000 annotated tables for each of the tasks containing Schema.org data from different websites. The tables cover 17 different types of entities such as movie, event, local business, recipe, job posting, or product. The tables stem from the WDC Schema.org Table Corpus which was created by extracting Schema.org annotations from the Common Crawl. Consequently, the labels used for annotating columns in SOTAB are part of the Schema.org vocabulary. The benchmark covers 91 types for CTA and 176 properties for CPA distributed across textual, numerical and date/time columns. The tables are split into fixed training, validation and test sets. The test sets are further divided into subsets focusing on specific challenges, such as columns with missing values or different value formats, in order to allow a more fine-grained comparison of annotation systems. The evaluation of SOTAB using Doduo and TURL shows that the benchmark is difficult to solve for current state-of-the-art systems.
 """
 
-_HOMEPAGE = ""
+_HOMEPAGE = "https://webdatacommons.org/structureddata/sotab/"
 
 _LICENSE = ""
 
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_DATA_URL = (
-    # "https://github.com/ppasupat/WikiTableQuestions/releases/download/v1.0.2/WikiTableQuestions-1.0.2-compact.zip"
-)
-
-
 class WikiTableQuestions(datasets.GeneratorBasedBuilder):
     """The SOTAB dataset is a large-scale dataset for the task of column type annotation on semi-structured tables."""
 
-    VERSION = datasets.Version("1.0.2")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="random-split-1",
-            version=VERSION,
-            description="",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = (
-        "random-split-1"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-    )
+    VERSION = datasets.Version("1.0.0")
 
     def _info(self):
         features = datasets.Features(