On-the-spot Knowledge Refinement for an Interactive Recommender System

Ikemoto, Yuichiro; Kuwabara, Kazuhiro

doi:10.5220/0007571508170823

Cited by 2 publications

(1 citation statement)

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“…Information retrieval routinely uses crowdsourcing for such tasks [36,37,38,39]. Similarly, recommender systems ask people for explicit feedback on recommended items [40,41,42].…”

Section: Relevancementioning

confidence: 99%

Assessing the Quality of Sources in Wikidata Across Languages: A Hybrid Approach

Amaral

Piscopo

Kaffee

et al. 2021

J. Data and Information Quality

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Wikidata is one of the most important sources of structured data on the web, built by a worldwide community of volunteers. As a secondary source, its contents must be backed by credible references; this is particularly important, as Wikidata explicitly encourages editors to add claims for which there is no broad consensus, as long as they are corroborated by references. Nevertheless, despite this essential link between content and references, Wikidata's ability to systematically assess and assure the quality of its references remains limited. To this end, we carry out a mixed-methods study to determine the relevance, ease of access, and authoritativeness of Wikidata references, at scale and in different languages, using online crowdsourcing, descriptive statistics, and machine learning. Building on previous work of ours, we run a series of microtasks experiments to evaluate a large corpus of references, sampled from Wikidata triples with labels in several languages. We use a consolidated, curated version of the crowdsourced assessments to train several machine learning models to scale up the analysis to the whole of Wikidata. The findings help us ascertain the quality of references in Wikidata and identify common challenges in defining and capturing the quality of user-generated multilingual structured data on the web. We also discuss ongoing editorial practices, which could encourage the use of higher-quality references in a more immediate way. All data and code used in the study are available on GitHub for feedback and further improvement and deployment by the research community.

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“…Information retrieval routinely uses crowdsourcing for such tasks [36,37,38,39]. Similarly, recommender systems ask people for explicit feedback on recommended items [40,41,42].…”

Section: Relevancementioning

confidence: 99%

Assessing the Quality of Sources in Wikidata Across Languages: A Hybrid Approach

Amaral

Piscopo

Kaffee

et al. 2021

J. Data and Information Quality

View full text Add to dashboard Cite

show abstract

Assessing the quality of sources in Wikidata across languages: a hybrid approach

Amaral¹,

Piscopo²,

Kaffee³

et al. 2021

Preprint

View full text Add to dashboard Cite

Wikidata is one of the most important sources of structured data on the web, built by a worldwide community of volunteers. As a secondary source, its contents must be backed by credible references; this is particularly important as Wikidata explicitly encourages editors to add claims for which there is no broad consensus, as long as they are corroborated by references. Nevertheless, despite this essential link between content and references, Wikidata's ability to systematically assess and assure the quality of its references remains limited. To this end, we carry out a mixed-methods study to determine the relevance, ease of access, and authoritativeness of Wikidata references, at scale and in different languages, using online crowdsourcing, descriptive statistics, and machine learning. Building on previous work of ours, we run a series of microtasks experiments to evaluate a large corpus of references, sampled from Wikidata triples with labels in several languages. We use a consolidated, curated version of the crowdsourced assessments to train several machine learning models to scale up the analysis to the whole of Wikidata. The findings help us ascertain the quality of references in Wikidata, and identify common challenges in defining and capturing the quality of user-generated multilingual structured data on the web. We also discuss ongoing editorial practices, which could encourage the use of higher-quality references in a more immediate way. All data and code used in the study are available on GitHub for feedback and further improvement and deployment by the research community.

show abstract

On-the-spot Knowledge Refinement for an Interactive Recommender System

Cited by 2 publications

References 0 publications

Assessing the Quality of Sources in Wikidata Across Languages: A Hybrid Approach

Assessing the Quality of Sources in Wikidata Across Languages: A Hybrid Approach

Assessing the quality of sources in Wikidata across languages: a hybrid approach

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