Special Issue "Knowledge Discovery on the Web"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 31 January 2020.

Special Issue Editors

Dr. Giuliano Armano
E-Mail Website
Guest Editor
Dipartimento di Matematica e Informatica, Università di Cagliari, Palazzo delle Scienze, Via Ospedale, 72, 09124 Cagliari, Italy
Interests: artificial intelligence; knowledge discovery; information retrieval
Dr. Matteo Cristani
E-Mail Website
Guest Editor
Dipartimento di Informatica, Università di Verona, Strada Le Grazie 15, 37134 Verona, Italy
Interests: artificial intelligence; knowledge discovery; automated reasoning; social networks

Special Issue Information

Dear Colleagues,

Following the workshop KDWEB 2019 at ICWE, we propose a Special Issue of Information.

Knowledge discovery is an interdisciplinary area focusing upon methodologies for identifying valid, novel, potentially useful, and meaningful patterns from data, and currently is widespread in numerous fields, including science, engineering, healthcare, business, and medicine. Recently, the rapid growth of social networks and online services has entailed that knowledge discovery approaches focus on the World Wide Web (WWW), whose popular use as a global information system has led to a huge amount of digital data.

KDWeb 2019 focused on the field of knowledge discovery from digital data, paying particular attention to data mining, machine learning, and information retrieval methods, systems, and applications. KDWeb 2019 aimed to provide a venue to researchers, scientists, students, and practitioners involved in the fields of knowledge discovery on data mining, information retrieval, and the semantic Web, for presenting and discussing novel and emerging ideas. KDWeb 2019 will contribute to discussing and comparing suitable novel solutions based on intelligent techniques applied in real-world applications.

Topics of Interest

The workshop has been accepting submissions of fresh investigations concerning experimental and applied studies on Web knowledge discovery. Topics include but are not limited to the following:

Big data on the Web;
Deep learning on the Web;
Feature selection and the extraction of Web data;
Hierarchical categorization of Web data;
Linked Web data;
Machine learning applications on the Web;
Open Web data;
Semantic Web;
Semantics and ontology engineering for Web applications;
Social media mining;
Social media measures and applications;
Text categorization on the Web;
Text mining for Web applications;
Web data mining;
Web information filtering and retrieval;
Web personalization and recommendation.

Dr. Giuliano Armano
Dr. Matteo Cristani
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Open AccessArticle
Analysis of Data Persistence in Collaborative Content Creation Systems: The Wikipedia Case
Information 2019, 10(11), 330; https://doi.org/10.3390/info10110330 - 25 Oct 2019
A very common problem in designing caching/prefetching systems, distribution networks, search engines, and web-crawlers is determining how long a given content lasts before being updated, i.e., its update frequency. Indeed, while some content is not frequently updated (e.g., videos), in other cases revisions [...] Read more.
A very common problem in designing caching/prefetching systems, distribution networks, search engines, and web-crawlers is determining how long a given content lasts before being updated, i.e., its update frequency. Indeed, while some content is not frequently updated (e.g., videos), in other cases revisions periodically invalidate contents. In this work, we present an analysis of Wikipedia, currently the 5th most visited website in the world, evaluating the statistics of updates of its pages and their relationship with page view statistics. We discovered that the number of updates of a page follows a lognormal distribution. We provide fitting parameters as well as a goodness of fit analysis, showing the statistical significance of the model to describe the empirical data. We perform an analysis of the views–updates relationship, showing that in a time period of a month, there is a lack of evident correlation between the most updated pages and the most viewed pages. However, observing specific pages, we show that there is a strong correlation between the peaks of views and updates, and we find that in more than 50% of cases, the time difference between the two peaks is less than a week. This reflects the underlying process whereby an event causes both an update and a visit peak that occurs with different time delays. This behavior can pave the way for predictive traffic analysis applications based on content update statistics. Finally, we show how the model can be used to evaluate the performance of an in-network caching scenario. Full article
(This article belongs to the Special Issue Knowledge Discovery on the Web)
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