RDFIO: extending Semantic MediaWiki for interoperable biomedical data management

Lampa, Samuel; Willighagen, Egon; Kohonen, Pekka; King, Ali; Vrandečić, Denny; Grafström, Roland C.; Spjuth, Ola

doi:10.1186/s13326-017-0136-y

Cited by 6 publications

(1 citation statement)

References 32 publications

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“…Subsetting is a recent research problem in KGs. To the best of our knowledge, the early demand for creating a biomedical subset of Wikidata was in 2017 [29], the subsetting discussions in the Wikidata biomedical community were concretely started at the 12th international SWAT4HCLS conference in 2019 by Andra Waagmeester et al [42] and then followed in Project 35 11 of ELIXIR BioHackathon-Europe 2020 [27], Project 21 12 of ELIXIR BioHackathon-Europe 2021, and Project 11 13 of ELIXIR BioHackathon-Europe 2022 [26].…”

Section: Subsetting State Of the Artmentioning

confidence: 99%

Wikidata subsetting: Approaches, tools, and evaluation

Hosseini Beghaeiraveri,

Labra Gayo,

Waagmeester

et al. 2023

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Wikidata is a massive Knowledge Graph (KG), including more than 100 million data items and nearly 1.5 billion statements, covering a wide range of topics such as geography, history, scholarly articles, and life science data. The large volume of Wikidata is difficult to handle for research purposes; many researchers cannot afford the costs of hosting 100 GB of data. While Wikidata provides a public SPARQL endpoint, it can only be used for short-running queries. Often, researchers only require a limited range of data from Wikidata focusing on a particular topic for their use case. Subsetting is the process of defining and extracting the required data range from the KG; this process has received increasing attention in recent years. Specific tools and several approaches have been developed for subsetting, which have not been evaluated yet. In this paper, we survey the available subsetting approaches, introducing their general strengths and weaknesses, and evaluate four practical tools specific for Wikidata subsetting – WDSub, KGTK, WDumper, and WDF – in terms of execution performance, extraction accuracy, and flexibility in defining the subsets. Results show that all four tools have a minimum of 99.96% accuracy in extracting defined items and 99.25% in extracting statements. The fastest tool in extraction is WDF, while the most flexible tool is WDSub. During the experiments, multiple subset use cases have been defined and the extracted subsets have been analyzed, obtaining valuable information about the variety and quality of Wikidata, which would otherwise not be possible through the public Wikidata SPARQL endpoint.

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