Wikidata and DBpedia: A Comparative Study

Abián, David; Guerra, Francesco; Martínez-Romanos, J.; Trillo-Lado, Raquel

doi:10.1007/978-3-319-74497-1_14

Cited by 18 publications

(11 citation statements)

References 6 publications

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“…For Ringler and Paulheim (2017), although “DBpedia, YAGO, or Wikidata, are often considered similar in nature and coverage, there are, in fact, quite a few differences.” In their work, they quantified those differences and identified the overlapping and complementary parts of these three public knowledge graphs, finding them “hardly interchangeable,” each with advantages and issues that depended on the desired application or domain. Abián et al (2018) also compared Wikidata with DBpedia in regard to “the most relevant data quality dimensions” and highlighted how Wikidata has “an open centralised nature” and its multi-lingual capacity, while DBpedia is “more popular in the Semantic Web and the Linked Open Data communities.” On the other hand, Thakkar et al (2016) ran a quality assessment of linked data in DBpedia and Wikidata from the perspective of question answering, a popular application scenario for knowledge databases, and found “the quality of Wikidata with regard to the majority of relevant metrics […] higher than that of DBpedia.”…”

Section: Resultsmentioning

confidence: 99%

“…Wikidata is an open, collaborative project started on October 30, 2012 by Wikimedia Deutschland, hosted and supported by the Wikimedia Foundation (Abián et al , 2018), continuously increasing its popularity since its creation (Vrandečić, 2013; Vrandečić and Krötzsch, 2014) and whose main goals are two: to be the central storage for the structured data of all its Wikimedia sister projects (such as Wikipedia itself), avoiding duplicate and contradicting information, but also facilitating multi-language capabilities and management; and to provide data to other third-party projects and initiatives, and allowing complex queries on the existing base of knowledge. Wikidata does not only store facts, but also the corresponding reference sources, allowing data validation and the creation of timelines (e.g.…”

Section: Introductionmentioning

confidence: 99%

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A systematic literature review on Wikidata

Mora‐Cantallops

Sánchez‐Alonso

García‐Barriocanal

2019

DTA

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Purpose The purpose of this paper is to review the current status of research on Wikidata and, in particular, of articles that either describe applications of Wikidata or provide empirical evidence, in order to uncover the topics of interest, the fields that are benefiting from its applications and which researchers and institutions are leading the work. Design/methodology/approach A systematic literature review is conducted to identify and review how Wikidata is being dealt with in academic research articles and the applications that are proposed. A rigorous and systematic process is implemented, aiming not only to summarize existing studies and research on the topic, but also to include an element of analytical criticism and a perspective on gaps and future research. Findings Despite Wikidata’s potential and the notable rise in research activity, the field is still in the early stages of study. Most research is published in conferences, highlighting such immaturity, and provides little empirical evidence of real use cases. Only a few disciplines currently benefit from Wikidata’s applications and do so with a significant gap between research and practice. Studies are dominated by European researchers, mirroring Wikidata’s content distribution and limiting its Worldwide applications. Originality/value The results collect and summarize existing Wikidata research articles published in the major international journals and conferences, delivering a meticulous summary of all the available empirical research on the topic which is representative of the state of the art at this time, complemented by a discussion of identified gaps and future work.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A systematic literature review on Wikidata

Mora‐Cantallops

Sánchez‐Alonso

García‐Barriocanal

2019

DTA

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“…In this research paper, we report on the efforts of the Wikidata community (including our own) to meet the COVID-19 data challenges outlined in the previous section by using Wikidata as a platform for collaboratively collecting, curating and visualizing COVID-19-related knowledge at scales commensurate with the pandemic. While the relative merits of Wikidata with respect to other knowledge graphs have been discussed previously [17][18][19], we focus on leveraging the potential of Wikidata as an existing platform with an existing community in a timely fashion for an emerging transdisciplinary application like the COVID-19 response.…”

Section: Organization Of the Manuscriptmentioning

confidence: 99%

“…1). The assignment of a single language-independent identifier for each entity in Wikidata helps minimize the size of the knowledge graph and avoids issues seen in databases such as DBpedia, where separate items are needed for each language [19]. Such a feature is allowed thanks to the use of Wikibase software -a 5 Cf.…”

Section: Data Modelmentioning

confidence: 99%

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Representing COVID-19 information in collaborative knowledge graphs: The case of Wikidata

Turki

Taieb

Shafee

et al. 2022

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Information related to the COVID-19 pandemic ranges from biological to bibliographic, from geographical to genetic and beyond. The structure of the raw data is highly complex, so converting it to meaningful insight requires data curation, integration, extraction and visualization, the global crowdsourcing of which provides both additional challenges and opportunities. Wikidata is an interdisciplinary, multilingual, open collaborative knowledge base of more than 90 million entities connected by well over a billion relationships. It acts as a web-scale platform for broader computer-supported cooperative work and linked open data, since it can be written to and queried in multiple ways in near real time by specialists, automated tools and the public. The main query language, SPARQL, is a semantic language used to retrieve and process information from databases saved in Resource Description Framework (RDF) format. Here, we introduce four aspects of Wikidata that enable it to serve as a knowledge base for general information on the COVID-19 pandemic: its flexible data model, its multilingual features, its alignment to multiple external databases, and its multidisciplinary organization. The rich knowledge graph created for COVID-19 in Wikidata can be visualized, explored, and analyzed for purposes like decision support as well as educational and scholarly research.

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