WikiAutoCat: Information Retrieval System for Automatic Categorization of Wikipedia Articles

Refaei, Nesma; Hemayed, Elsayed E.; Mansour, Riham

doi:10.1007/s13369-018-3244-9

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…The performance of the proposed Wikipedia edit retrieval system (WRS) has been compared with the al-ready existing system WikiAutoCat [23]. The precision and recall have been measured and significant per-formance improvement has been observed.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

WIKI STREAMS: Wikipedia Article Recent Edit Retrieval System using Hierarchical Stream Clustering

M¹,

Sankaranarayanan²

2021

Preprint

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Stream analytics, a new paradigm in data analytics, has gained mo- mentum due to the voluminous stream data generation. With the huge increase in the edits performed on Wikipedia topics, it is tedious for the digital knowledge discovery users to nd their domain updates immediately. The users need to go through large information and spend more time to nd the potential data. There is a need for retrieving the Wikipedia edits based on the meta data of the article edits for later retriev-al. Hence, the clustering technique may be employed in order to group the Wikipedia article edits domain wise. Hence, in this paper, hierarchi- cal stream clustering is applied in order to retrieve the edits based on the user interest. Over a period of month, the data from Wikipedia is collected and used as a dataset. Our method is compared with the state-of-the-art clus-tering system WikiAutoCat and it is observed that the accuracy is improved by 10% and the clustering time is reduced by 20%.

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Section: Comparison With Existing Methodsmentioning

confidence: 99%

WIKI STREAMS: Wikipedia Article Recent Edit Retrieval System using Hierarchical Stream Clustering

M¹,

Sankaranarayanan²

2021

Preprint

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“…Many studies explored how to automatically categorize Wikipedia articles (e.g. Refaei et al , 2018; Perez et al , 2021; Simone and Strube, 2007; Strube, and Simone, 2006; Gantner and Schmidt-Thieme, 2009).…”

Section: Background and Related Studiesmentioning

confidence: 99%

Mapping knowledge domains on Wikipedia: an author bibliographic coupling analysis of traditional Chinese medicine

Zhao

Strotmann²

2021

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PurposeWikipedia has the lofty goal of compiling all human knowledge. The purpose of the present study is to map the structure of the Traditional Chinese Medicine (TCM) knowledge domain on Wikipedia, to identify patterns of knowledge representation on Wikipedia and to test the applicability of author bibliographic coupling analysis, an effective method for mapping knowledge domains represented in published scholarly documents, for Wikipedia data.Design/methodology/approachWe adapted and followed the well-established procedures and techniques for author bibliographic coupling analysis (ABCA). Instead of bibliographic data from a citation database, we used all articles on TCM downloaded from the English version of Wikipedia as our dataset. An author bibliographic coupling network was calculated and then factor analyzed using SPSS. Factor analysis results were visualized. Factors were labeled upon manual examination of articles that authors who load primarily in each factor have significantly contributed references to. Clear factors were interpreted as topics.FindingsSeven TCM topic areas are represented on Wikipedia, among which Acupuncture-related practices, Falun Gong and Herbal Medicine attracted the most significant contributors to TCM. Acupuncture and Qi Gong have the most connections to the TCM knowledge domain and also serve as bridges for other topics to connect to the domain. Herbal medicine is weakly linked to and non-herbal medicine is isolated from the rest of the TCM knowledge domain. It appears that specific topics are represented well on Wikipedia but their conceptual connections are not. ABCA is effective for mapping knowledge domains on Wikipedia but document-based bibliographic coupling analysis is not.Originality/valueGiven the prominent position of Wikipedia for both information users and for researchers on knowledge organization and information retrieval, it is important to study how well knowledge is represented and structured on Wikipedia. Such studies appear largely missing although studies from different perspectives both about Wikipedia and using Wikipedia as data are abundant. Author bibliographic coupling analysis is effective for mapping knowledge domains represented in published scholarly documents but has never been applied to mapping knowledge domains represented on Wikipedia.

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Distracting users as per their knowledge: Combining linked open data and word embeddings to enhance history learning

Blanco‐Fernández

Gil‐Solla

Pazos‐Arias

et al. 2020

Expert Systems with Applications

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WikiAutoCat: Information Retrieval System for Automatic Categorization of Wikipedia Articles

Cited by 3 publications

References 17 publications

WIKI STREAMS: Wikipedia Article Recent Edit Retrieval System using Hierarchical Stream Clustering

WIKI STREAMS: Wikipedia Article Recent Edit Retrieval System using Hierarchical Stream Clustering

Mapping knowledge domains on Wikipedia: an author bibliographic coupling analysis of traditional Chinese medicine

Distracting users as per their knowledge: Combining linked open data and word embeddings to enhance history learning

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