=Paper=
{{Paper
|id=Vol-2982/paper-8
|storemode=property
|title=Coupling Wikipedia Categories with Wikidata Statements for Better Semantics
|pdfUrl=https://ceur-ws.org/Vol-2982/paper-8.pdf
|volume=Vol-2982
|authors=Houcemeddine Turki,Mohamed Ali Hadj Taieb,Mohamed Ben Aouicha
|dblpUrl=https://dblp.org/rec/conf/semweb/TurkiTA21
}}
==Coupling Wikipedia Categories with Wikidata Statements for Better Semantics==
https://ceur-ws.org/Vol-2982/paper-8.pdf
Coupling Wikipedia Categories with Wikidata
Statements for Better Semantics
Houcemeddine Turki1[0000-0003-3492-2014], Mohamed Ali Hadj Taieb1[0000-0002-2786-8913] and
Mohamed Ben Aouicha 1[0000-0002-2277-5814]
1
Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of
Sfax, Sfax, Tunisia
turkiabdelwaheb@hotmail.fr, {mohamed.benaouicha,
mohamedali.hajtaieb}@fss.usf.tn
Abstract. In this position paper, we explain how the combination of Wikipedia
Categories already in use for driving semantic applications with Wikidata
statements related to the categories and their direct members is possible from a
technical perspective thanks to the flexible data models of Wikipedia
Categories and Wikidata statements and to the programmatic options provided
by the Wikimedia Community. We also outline how such a combination can
bring an added value to the development of Wikipedia Projects as well as to the
enhancement of knowledge-based systems.
Keywords: Wikipedia Categories, Wikidata, Semantic Applications.
1 Introduction
Nowadays, knowledge-based systems have become interestingly used for automating
various tasks [1-3]. By contrast to other types of intelligent systems, knowledge-based
systems use information resources as references for verifying and assessing inputs and
generating reliable outputs [4]. These resources can be raw texts, semi-structured
datasets or fully structured semantic databases [4, 5]. In this context, Wikimedia
Projects hosted by Wikimedia Foundation can be valuable to drive knowledge-based
systems as they are freely available and retrievable online and provide rich semantic
information collaboratively developed by a large community of contributors [2]. In
fact, Wikipedia provides a textual outline of multiple entities with a variety of
semi-structured and structured information including wikilinks, redirections, infobox
data and categories [6]. The sum of these semi-structured data have been useful to
construct a series of knowledge graphs such as DBpedia for measuring semantic
similarity among other applications [2, 7]. Particularly, Wikipedia Categories has been
a very valuable resource to build an “is a” taxonomy that can be used for labelling
datasets or supporting semantic applications [2]. Wiktionary provides brief definitions
of nouns, adjectives, adverbs and verbs (so-called glosses) for a given language
allowing to efficiently find semantic relatedness between concepts [8] and
consequently to improve natural language processing algorithms [9]. Again,
Wiktionary classifies its entities into categories. However, they rarely represent
Copyright © 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
2
taxonomic relations between concepts and mostly provide an overview of the
grammatical and etymological features of the represented entities [10]. One bright
exception that should be generalized is the Russian Wiktionary which has 2
elaborated systems: so-called semantic categories built on the basis of taxonomic
relations and semantic relations (synonymy, hypernymy, and so on) in each glossary
entry. Wikidata (https://www.wikidata.org) is a large-scale multilingual knowledge
graph that represents concepts as items and describes them using statements in the
form of triples where Wikidata predicates define the types of the claims [11-12]. Here,
Wikidata properties are entities that are described by logical constraints and Wikidata
statements specifying the conditions of their practical usage and classes are assigned
Shape Expressions (ShEx) defining how their members should be structured [3, 13].
The sum of all this information provides the backbone of a semantically rich
information that can be used for various applications [3, 13]. Although every
Wikimedia Project has distinctive features that make it useful for driving semantic
applications, limited efforts have been made to combine the data provided by two
wikis or more to develop semantic computations. Effectively, the only effort in this
context was the combination of Wiktionary glosses with Wikipedia Categories for
measuring semantic relatedness [14-17].
In this position paper, we introduce the combined use of Wikipedia Categories and
Wikidata Statements for various applications related to Semantics and to the
development of the two Wikimedia Projects. We begin by explaining the technical
approaches that can be used to combine these two types of semantic data (Section 2).
Later, we provide a detailed overview of the possible useful applications of such a
combination in multiple fields (Section 3). Finally, we draw conclusions for our paper
and we outline future directions for our research work (Section 4).
2 Implementation options
Due to the flexible data model of Wikidata and to the easy use of Wikimedia
interfaces and tools, there are actually many options that programmatically allow data
integration between Wikipedia Categories and Wikidata Statements. As Wikimedia
Projects are hosted using MediaWiki software, Wikidata and language editions of
Wikipedia have data dumps, especially ones in the XML format, that can be
downloaded from https://dumps.wikimedia.org/. Direct download links for the dumps
of each project can be easily found in a specific page of its website1. As these dumps
are in structured formats, they can be easily processed using Python Libraries (e.g.
Python XML2) to extract and then analyze Wikipedia Categories and Wikidata
Statements for the development of interesting knowledge-based systems [18]. As
Wikidata dumps can be voluminous and as users can need a unique type of
information, WDumper (https://wdumps.toolforge.org/) can be used to create
customized Wikidata RDF dumps that only include a needed subset of labels, entities
and statements allowing to run the user’s bot without having to perform useless tasks
[19]. Similarly, Wikipedia dumps can be parsed with the help of several tools such as
1
https://en.wikipedia.org/wiki/Wikipedia:Database_download (English Wikipedia),
https://www.wikidata.org/wiki/Wikidata:Database_download (Wikidata).
2
e.g., https://pypi.org/project/xml-python/
3
PetScan and Quarry [20-21]. As well, the MediaWiki API3 provides a set of actions
that can be used to retrieve specific information about Wikipedia Categories and
Wikidata items and to adjust them when needed [22]. Such actions can be
programmatically performed without any fear of exceeding query limits thanks to
several Python Libraries that are designed to parse MediaWiki APIs like Pywikibot4
and Wikibase Integrator5. Given that Wikidata statements are represented in RDF,
Wikidata has its own SPARQL endpoint (WDQS, https://query.wikidata.org) for
analyzing and extracting features from the Wikidata statements [12, 23]. It can not
only allow to process Wikidata statements related to Wikipedia Categories but also to
parse the Wikidata statements related to the direct members of Categories thanks to
the possibility of a query federation between the Wikidata SPARQL endpoint and the
MediaWiki API of a language edition of Wikipedia6. WDQS can be a valuable
resource for developers as its results can be downloaded in various structured formats
(e.g. TSV) and as its queries can be embedded to source codes in various
programming languages such as Python. The information returned by the Wikidata
Query Service can be augmented through the use of the “Wikidata SPARQL query
equivalent” [P3921] statements as well as of the “category combines topics” [P971]
and “category contains” [P4224] statements7. Further than these two options,
Wikipedia and Wikidata pages can be explored using user scripts developed in
JavaScript. This is made possible thanks to the Document Object Model of Wikimedia
Pages on one hand and to a special set of MediaWiki configurations named
mw.config8. Furthermore, data about Wikidata items can be automatically retrieved
and embedded in Wikipedia Pages using Lua Scripts allowing the creation of pages
where the user-generated information about an entity is shown at the same Wikipedia
page as the Wikidata statements about it9.
3 Applications
The combination of Wikidata statements with Wikipedia categories can be useful for
various applications. First, the statements involving the Wikidata items corresponding
to the members of a given Wikipedia category can be analyzed to infer a structured
description of the category to be represented as “category combines topics” [P971]
and “category contains” [P4224] statements. Such a description is multilingual and
can allow users to understand the content of a category even if they lack consistent
proficiency of the language of the assessed Wikipedia edition. As well, this
description can provide a snapshot of the needed Wikidata statements to describe,
validate or adjust the new inconsistent or incomplete Wikidata items of the category
members. Second, the comparison of the outputs of a given Wikipedia Category to the
3
e.g., https://www.wikidata.org/w/api.php.
4
https://github.com/wikimedia/pywikibot
5
https://github.com/LeMyst/WikibaseIntegrator
6
e.g., https://w.wiki/3nyu, Credit: Houcemeddine Turki, Jan Ainali and Dipsacus fullonum
7
Examples for the usage of P3921, P971, and P4224 properties can be found at
https://www.wikidata.org/wiki/Q7439502.
8
https://www.mediawiki.org/wiki/Manual:Interface/JavaScript.
9
https://en.wikipedia.org/wiki/Module:Wikidata.
4
list of Wikidata items that can be involved in the category can be very efficient to
identify the gaps and limitations in the representation of the topic of the category in
Wikipedia or Wikidata. The lack of completeness of a given subset of Wikipedia
Category Graph can be solved through the machine translation of the labels of the
unsupported Wikidata items. Similarly, the lack of assignment of taxonomic relations
(e.g., instance of) and non-taxonomic ones (e.g., main subject) to a category member
can be fixed through the conversion of the Wikidata statements describing the given
Wikipedia category into claims that can be assigned to the Wikidata item. Third, the
analysis of Wikidata statements can be efficient to identify whether the
subcategorization in the Wikipedia Category Graph is motivated by the fact that one
category is a subclass (so-called hyponym) of the other one and consequently to
eliminate non-transitive relations from the Wikipedia Category Graph for the
generation of an “is a” taxonomy that can be used to drive semantic applications. So
far, this process is achieved using complicated and time-consuming statistical and
probabilistic approaches [2, 24]. However, this trimming becomes simple thanks to
the semantic information provided by Wikidata.
Beyond the Wikimedia Projects, Wikidata statements coupled to Wikipedia
categories can be useful to drive knowledge-based systems. The most common
examples of such an application are semantic similarity measures driven by Wikipedia
Category Graph that can be further enhanced through the use of Wikidata statements
[2, 17-18]. Another example can be the use of Wikidata statements to analyze the full
texts of category members in Wikipedia editions for modelling the topics of a
category as well as for training knowledge graph embeddings and machine learning
models to process natural language texts and for enabling knowledge graph
enrichment and validation in a multilingual context.
4 Conclusion
In this position paper, we emphasized the usefulness of the combination of Wikipedia
Categories with the Wikidata statements involving the categories and their direct
members to achieve a better semantic representation for the Wikipedia Category
Graph as well as for the Wikidata items. This association can drive multiple semantic
applications bringing information retrieval, semantic web and natural language
processing to the next stage. As a future direction for this work, we look forward to
developing semantic applications driven by the Wikipedia-Wikidata combination.
5 Acknowledgements
This work is developed as a part of the Structured Categories project available at
https://www.wikidata.org/wiki/Wikidata:Structured_Categories. We have to thank
Anton V. Chupin (Institute of Continuous Media Mechanics of the Ural Branch of the
Russian Academy of Sciences, Russian Federation) for providing useful comments
and discussion about the topic of the research paper.
5
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