Cleansing Wikipedia Categories using Centrality

Boldi, Paolo; Monti, Corrado

doi:10.1145/2872518.2891111

Cited by 18 publications

(17 citation statements)

References 15 publications

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“…Each language chapter can define own structure and hierarchy of categories. As a result in some language versions that structure is often too fine-grained to be directly analyzed [65], which may make it difficult to determine the number of possible topics to deal with.…”

Section: Main Topic Classificationsmentioning

confidence: 99%

“…As mentioned before, the category structure is a complex and ever-changing, as it can be edited by 208 any person -users can add or change a category assignment to other category. The resulting category 209 structure is noisy [64], sparse and it contains duplications and oversights [65]. So, we can also face 210 the situation that categories are repeated at different levels of the tree, in which the root can be a 211 different other main categories (one of the 27 considered).…”

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confidence: 99%

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Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics

Lewoniewski¹,

Węcel²,

Abramowicz³

2019

Preprint

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In Wikipedia, articles about various topics can be created and edited independently in each language version. Therefore, quality of information about the same topic depends on language. Any interested user can improve an article and that improvement may depend on popularity of the article. The goal of this study is to show what topics are best represented in different language versions of Wikipedia using results of quality assessment for over 39 million articles in 55 languages. In this paper we also analyze how popular are selected topics among readers and authors in various languages. We used two approaches to assign articles to various topics. First, we divided articles into 27 main topics based on information extracted from over 10 million categories in 55 language versions and analyzed about 400 million links from articles to over 10 million categories and over 26 million links between categories. In the second approach we used data from DBpedia and Wikidata. We also showed how the results of the study can be used to build local and global rankings of the Wikipedia content.

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Section: Main Topic Classificationsmentioning

confidence: 99%

mentioning

confidence: 99%

Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics

Lewoniewski¹,

Węcel²,

Abramowicz³

2019

Preprint

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“…Concretely, we extract from DBPedia, for each article, its top-level type in the DBPedia type hierarchy (there are 55 top-level types). 5 For each category, we then construct a type histogram, which summarizes the DBPedia types of the articles contained in the category, and model the homogeneity, or purity, of the category as the Gini coefficient of its type histogram. A low Gini coefficient means that a histogram distributes its probability mass more evenly over the 55 DBPedia types, which indicates an impure, non-ontological category.…”

Section: Cleaning the Category Networkmentioning

confidence: 99%

Structuring Wikipedia Articles with Section Recommendations

Piccardi

Catasta

Zia

et al. 2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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Sections are the building blocks of Wikipedia articles. They enhance readability and can be used as a structured entry point for creating and expanding articles. Structuring a new or already existing Wikipedia article with sections is a hard task for humans, especially for newcomers or less experienced editors, as it requires significant knowledge about how a well-written article looks for each possible topic. Inspired by this need, the present paper defines the problem of section recommendation for Wikipedia articles and proposes several approaches for tackling it. Our systems can help editors by recommending what sections to add to already existing or newly created Wikipedia articles. Our basic paradigm is to generate recommendations by sourcing sections from articles that are similar to the input article. We explore several ways of defining similarity for this purpose (based on topic modeling, collaborative filtering, and Wikipedia's category system). We use both automatic and human evaluation approaches for assessing the performance of our recommendation system, concluding that the category-based approach works best, achieving precision@10 of about 80% in the human evaluation.

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“…and hierarchy of categories. Moreover, in some language versions that structure is often too fine-grained to be directly analyzed [15]. All this may make it difficult to determine the number of possible topics to deal with.…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned before, the category structure is a complex and ever-changing, as it can be edited by any person-users can add or change a category assignment to another category. The resulting category structure is noisy [14], sparse and it contains duplications and oversights [15]. So, we can also face the situation that categories are repeated at different levels of the tree, in which the root can be another main category (one of the 27 considered).…”

Section: Introductionmentioning

confidence: 99%

Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics

2019

View full text Add to dashboard Cite

On Wikipedia, articles about various topics can be created and edited independently in each language version. Therefore, the quality of information about the same topic depends on the language. Any interested user can improve an article and that improvement may depend on the popularity of the article. The goal of this study is to show what topics are best represented in different language versions of Wikipedia using results of quality assessment for over 39 million articles in 55 languages. In this paper, we also analyze how popular selected topics are among readers and authors in various languages. We used two approaches to assign articles to various topics. First, we selected 27 main multilingual categories and analyzed all their connections with sub-categories based on information extracted from over 10 million categories in 55 language versions. To classify the articles to one of the 27 main categories, we took into account over 400 million links from articles to over 10 million categories and over 26 million links between categories. In the second approach, we used data from DBpedia and Wikidata. We also showed how the results of the study can be used to build local and global rankings of the Wikipedia content.

show abstract

Cleansing Wikipedia Categories using Centrality

Cited by 18 publications

References 15 publications

Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics

Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics

Structuring Wikipedia Articles with Section Recommendations

Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics

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