Wikipedia – Free and Reliable? Aspects of a Collaboratively Shaped Encyclopaedia*
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Content Categorization for Contextual Advertising Using Wikipedia
Content Categorization for Contextual Advertising Using Wikipedia Ingrid Grønlie Guren August 2, 2015 Content Categorization for Contextual Advertising Using Wikipedia Ingrid Grønlie Guren August 2, 2015 ii Abstract Automatic categorization of content is an important functionality in online ad- vertising and automated content recommendations, both for ensuring contextual relevancy of placements and for building up behavioral profiles for users that consume the content. Within the advertising domain, the taxonomy tree that content is classified into is defined with some commercial application in mind to somehow reflect the advertising platform’s ad inventory. The nature of the ad inventory and the language of the content might vary across brokers (i.e., the operator of the advertising platform), so it was of interest to develop a system that can easily bootstrap the development of a well-working classifier. We developed a dictionary-based classifier based on titles from Wikipedia articles where the titles represent entries in the dictionary. The idea of the dictionary-based classifier is so simple that it can be understood by users of the program, also those who lack technical experience. Further, it has the ad- vantage that its users easily can expand the dictionary with desirable words for specific advertisement purposes. The process of creating the classifier includes a processing of all Wikipedia article titles to a form more likely to occur in docu- ments, before each entry is graded to their most describing Wikipedia category path. The Wikipedia category paths are further mapped to categories based on the taxonomy of Interactive Advertising Bureau (IAB), which are categories relevant for advertising. -
Modeling Popularity and Reliability of Sources in Multilingual Wikipedia
information Article Modeling Popularity and Reliability of Sources in Multilingual Wikipedia Włodzimierz Lewoniewski * , Krzysztof W˛ecel and Witold Abramowicz Department of Information Systems, Pozna´nUniversity of Economics and Business, 61-875 Pozna´n,Poland; [email protected] (K.W.); [email protected] (W.A.) * Correspondence: [email protected] Received: 31 March 2020; Accepted: 7 May 2020; Published: 13 May 2020 Abstract: One of the most important factors impacting quality of content in Wikipedia is presence of reliable sources. By following references, readers can verify facts or find more details about described topic. A Wikipedia article can be edited independently in any of over 300 languages, even by anonymous users, therefore information about the same topic may be inconsistent. This also applies to use of references in different language versions of a particular article, so the same statement can have different sources. In this paper we analyzed over 40 million articles from the 55 most developed language versions of Wikipedia to extract information about over 200 million references and find the most popular and reliable sources. We presented 10 models for the assessment of the popularity and reliability of the sources based on analysis of meta information about the references in Wikipedia articles, page views and authors of the articles. Using DBpedia and Wikidata we automatically identified the alignment of the sources to a specific domain. Additionally, we analyzed the changes of popularity and reliability in time and identified growth leaders in each of the considered months. The results can be used for quality improvements of the content in different languages versions of Wikipedia. -
Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science
(forthcoming in the Journal of the Association for Information Science and Technology) Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science Misha Teplitskiy Grace Lu Eamon Duede Dept. of Sociology and KnowledgeLab Computation Institute and KnowledgeLab University of Chicago KnowledgeLab University of Chicago [email protected] University of Chicago [email protected] (773) 834-4787 [email protected] (773) 834-4787 5735 South Ellis Avenue (773) 834-4787 5735 South Ellis Avenue Chicago, Illinois 60637 5735 South Ellis Avenue Chicago, Illinois 60637 Chicago, Illinois 60637 Abstract With the rise of Wikipedia as a first-stop source for scientific knowledge, it is important to compare its representation of that knowledge to that of the academic literature. Here we identify the 250 most heavi- ly used journals in each of 26 research fields (4,721 journals, 19.4M articles in total) indexed by the Scopus database, and test whether topic, academic status, and accessibility make articles from these journals more or less likely to be referenced on Wikipedia. We find that a journal’s academic status (im- pact factor) and accessibility (open access policy) both strongly increase the probability of its being ref- erenced on Wikipedia. Controlling for field and impact factor, the odds that an open access journal is referenced on the English Wikipedia are 47% higher compared to paywall journals. One of the implica- tions of this study is that a major consequence of open access policies is to significantly amplify the dif- fusion of science, through an intermediary like Wikipedia, to a broad audience. Word count: 7894 Introduction Wikipedia, one of the most visited websites in the world1, has become a destination for information of all kinds, including information about science (Heilman & West, 2015; Laurent & Vickers, 2009; Okoli, Mehdi, Mesgari, Nielsen, & Lanamäki, 2014; Spoerri, 2007). -
Language-Agnostic Relation Extraction from Abstracts in Wikis
information Article Language-Agnostic Relation Extraction from Abstracts in Wikis Nicolas Heist, Sven Hertling and Heiko Paulheim * ID Data and Web Science Group, University of Mannheim, Mannheim 68131, Germany; [email protected] (N.H.); [email protected] (S.H.) * Correspondence: [email protected] Received: 5 February 2018; Accepted: 28 March 2018; Published: 29 March 2018 Abstract: Large-scale knowledge graphs, such as DBpedia, Wikidata, or YAGO, can be enhanced by relation extraction from text, using the data in the knowledge graph as training data, i.e., using distant supervision. While most existing approaches use language-specific methods (usually for English), we present a language-agnostic approach that exploits background knowledge from the graph instead of language-specific techniques and builds machine learning models only from language-independent features. We demonstrate the extraction of relations from Wikipedia abstracts, using the twelve largest language editions of Wikipedia. From those, we can extract 1.6 M new relations in DBpedia at a level of precision of 95%, using a RandomForest classifier trained only on language-independent features. We furthermore investigate the similarity of models for different languages and show an exemplary geographical breakdown of the information extracted. In a second series of experiments, we show how the approach can be transferred to DBkWik, a knowledge graph extracted from thousands of Wikis. We discuss the challenges and first results of extracting relations from a larger set of Wikis, using a less formalized knowledge graph. Keywords: relation extraction; knowledge graphs; Wikipedia; DBpedia; DBkWik; Wiki farms 1. Introduction Large-scale knowledge graphs, such as DBpedia [1], Freebase [2], Wikidata [3], or YAGO [4], are usually built using heuristic extraction methods, by exploiting crowd-sourcing processes, or both [5]. -
Defending Democracy & Kristin Skare Orgeret Nordicom-Information
Defending Defending Oslo 8–11 August 2013 Democracy The 2013 NordMedia conference in Oslo marked the 40 years that had passed since the very first Nordic media conference. To acknowledge this 40-year anniversary, it made sense to have a conference theme that dealt with a major and important topic: Defending Democracy. Nordic Defending and Global Diversities in Media and Journalism. Focusing on the rela- tionship between journalism, other media practices and democracy, the plenary sessions raised questions such as: Democracy & Edited by What roles do media and journalism play in democratization • Kristin Skare Orgeret processes and what roles should they play? Nordic and Global Diversities How does the increasingly complex and omnipresent media in Media and Journalism • Hornmoen Harald field affect conditions for freedom of speech? This special issue contains the keynote speeches of Natalie Fenton, Stephen Ward and Ib Bondebjerg. A number of the conference papers have been revised and edited to become articles. Together, the articles presented should give the reader an idea of the breadth and depth of Edited by current Nordic scholarship in the area. Harald Hornmoen & Kristin Skare Orgeret SPECIAL ISSUE Nordicom Review | Volume 35 | August 2014 Nordicom-Information | Volume 36 | Number 2 | August 2014 Nordicom-Information Nordicom Review University of Gothenburg 2014 issue Special Box 713, SE 405 30 Göteborg, Sweden Telephone +46 31 786 00 00 (op.) | Fax +46 31 786 46 55 www.nordicom.gu.se | E-mail: [email protected] SPECIAL ISSUE Nordicom Review | Volume 35 | August 2014 Nordicom-Information | Volume 36 | Number 2 | August 2014 Nordicom Review Journal from the Nordic Information Centre for Media and Communication Research Editor NORDICOM invites media researchers to contri- Ulla Carlsson bute scientific articles, reviews, and debates. -
A Complete, Longitudinal and Multi-Language Dataset of the Wikipedia Link Networks
WikiLinkGraphs: A Complete, Longitudinal and Multi-Language Dataset of the Wikipedia Link Networks Cristian Consonni David Laniado Alberto Montresor DISI, University of Trento Eurecat, Centre Tecnologic` de Catalunya DISI, University of Trento [email protected] [email protected] [email protected] Abstract and result in a huge conceptual network. According to Wiki- pedia policies2 (Wikipedia contributors 2018e), when a con- Wikipedia articles contain multiple links connecting a sub- cept is relevant within an article, the article should include a ject to other pages of the encyclopedia. In Wikipedia par- link to the page corresponding to such concept (Borra et al. lance, these links are called internal links or wikilinks. We present a complete dataset of the network of internal Wiki- 2015). Therefore, the network between articles may be seen pedia links for the 9 largest language editions. The dataset as a giant mind map, emerging from the links established by contains yearly snapshots of the network and spans 17 years, the community. Such graph is not static but is continuously from the creation of Wikipedia in 2001 to March 1st, 2018. growing and evolving, reflecting the endless collaborative While previous work has mostly focused on the complete hy- process behind it. perlink graph which includes also links automatically gener- The English Wikipedia includes over 163 million con- ated by templates, we parsed each revision of each article to nections between its articles. This huge graph has been track links appearing in the main text. In this way we obtained exploited for many purposes, from natural language pro- a cleaner network, discarding more than half of the links and cessing (Yeh et al. -
Complete Issue
Culture Unbound: Journal of Current Cultural Research Thematic Section: Changing Orders of Knowledge? Encyclopedias in Transition Edited by Jutta Haider & Olof Sundin Extraction from Volume 6, 2014 Linköping University Electronic Press Culture Unbound: ISSN 2000-1525 (online) URL: http://www.cultureunbound.ep.liu.se/ © 2014 The Authors. Culture Unbound, Extraction from Volume 6, 2014 Thematic Section: Changing Orders of Knowledge? Encyclopedias in Transition Jutta Haider & Olof Sundin Introduction: Changing Orders of Knowledge? Encyclopaedias in Transition ................................ 475 Katharine Schopflin What do we Think an Encyclopaedia is? ........................................................................................... 483 Seth Rudy Knowledge and the Systematic Reader: The Past and Present of Encyclopedic Reading .............................................................................................................................................. 505 Siv Frøydis Berg & Tore Rem Knowledge for Sale: Norwegian Encyclopaedias in the Marketplace .............................................. 527 Vanessa Aliniaina Rasoamampianina Reviewing Encyclopaedia Authority .................................................................................................. 547 Ulrike Spree How readers Shape the Content of an Encyclopedia: A Case Study Comparing the German Meyers Konversationslexikon (1885-1890) with Wikipedia (2002-2013) ........................... 569 Kim Osman The Free Encyclopaedia that Anyone can Edit: The -
Wikimatrix: Mining 135M Parallel Sentences
Under review as a conference paper at ICLR 2020 WIKIMATRIX:MINING 135M PARALLEL SENTENCES IN 1620 LANGUAGE PAIRS FROM WIKIPEDIA Anonymous authors Paper under double-blind review ABSTRACT We present an approach based on multilingual sentence embeddings to automat- ically extract parallel sentences from the content of Wikipedia articles in 85 lan- guages, including several dialects or low-resource languages. We do not limit the extraction process to alignments with English, but systematically consider all pos- sible language pairs. In total, we are able to extract 135M parallel sentences for 1620 different language pairs, out of which only 34M are aligned with English. This corpus of parallel sentences is freely available.1 To get an indication on the quality of the extracted bitexts, we train neural MT baseline systems on the mined data only for 1886 languages pairs, and evaluate them on the TED corpus, achieving strong BLEU scores for many language pairs. The WikiMatrix bitexts seem to be particularly interesting to train MT systems between distant languages without the need to pivot through English. 1 INTRODUCTION Most of the current approaches in Natural Language Processing (NLP) are data-driven. The size of the resources used for training is often the primary concern, but the quality and a large variety of topics may be equally important. Monolingual texts are usually available in huge amounts for many topics and languages. However, multilingual resources, typically sentences in two languages which are mutual translations, are more limited, in particular when the two languages do not involve English. An important source of parallel texts are international organizations like the European Parliament (Koehn, 2005) or the United Nations (Ziemski et al., 2016). -
Strengthening and Unifying the Visual Identity of Wikimedia Projects: a Step Towards Maturity
Strengthening and unifying the visual identity of Wikimedia projects: a step towards maturity Guillaume Paumier∗ Elisabeth Bauer [[m:User:guillom]] [[m:User:Elian]] Abstract In January 2007, the Wikimedian community celebrated the sixth birthday of Wikipedia. Six years of constant evolution have now led to Wikipedia being one of the most visited websites in the world. Other projects developing free content and supported by the Wikimedia Foundation have been expanding rapidly too. The Foundation and its projects are now facing some communication issues due to the difference of scale between the human and financial resources of the Foundation and the success of its projects. In this paper, we identify critical issues in terms of visual identity and marketing. We evaluate the situation and propose several changes, including a redesign of the default website interface. Introduction The first Wikipedia project was created in January 2001. In these days, the technical infrastructure was provided by Bomis, a dot-com company. In June 2003, Jimmy Wales, founder of Wikipedia and owner of Bomis, created the Wikimedia Foundation [1] to provide a long-term administrative and technical structure dedicated to free content. Since these days, both the projects and the Foundation have been evolving. New projects have been created. All have grown at different rates. Some have got more fame than the others. New financial, technical and communication challenges have risen. In this paper, we will first identify some of these challenges and issues in terms of global visual identity. We will then analyse logos, website layouts, projects names, trademarks so as to provide some hindsight. -
Using Natural Language Generation to Bootstrap Missing Wikipedia Articles
Semantic Web 0 (0) 1 1 IOS Press 1 1 2 2 3 3 4 Using Natural Language Generation to 4 5 5 6 Bootstrap Missing Wikipedia Articles: 6 7 7 8 8 9 A Human-centric Perspective 9 10 10 * 11 Lucie-Aimée Kaffee , Pavlos Vougiouklis and Elena Simperl 11 12 School of Electronics and Computer Science, University of Southampton, UK 12 13 E-mails: [email protected], [email protected], [email protected] 13 14 14 15 15 16 16 17 17 Abstract. Nowadays natural language generation (NLG) is used in everything from news reporting and chatbots to social media 18 18 management. Recent advances in machine learning have made it possible to train NLG systems that seek to achieve human- 19 19 level performance in text writing and summarisation. In this paper, we propose such a system in the context of Wikipedia and 20 evaluate it with Wikipedia readers and editors. Our solution builds upon the ArticlePlaceholder, a tool used in 14 under-resourced 20 21 Wikipedia language versions, which displays structured data from the Wikidata knowledge base on empty Wikipedia pages. 21 22 We train a neural network to generate a ’introductory sentence from the Wikidata triples shown by the ArticlePlaceholder, and 22 23 explore how Wikipedia users engage with it. The evaluation, which includes an automatic, a judgement-based, and a task-based 23 24 component, shows that the summary sentences score well in terms of perceived fluency and appropriateness for Wikipedia, and 24 25 can help editors bootstrap new articles. -
Wikipedia Data Analysis
Wikipedia data analysis. Introduction and practical examples Felipe Ortega June 29, 2012 Abstract This document offers a gentle and accessible introduction to conduct quantitative analysis with Wikipedia data. The large size of many Wikipedia communities, the fact that (for many languages) the already account for more than a decade of activity and the precise level of detail of records accounting for this activity represent an unparalleled opportunity for researchers to conduct interesting studies for a wide variety of scientific disciplines. The focus of this introductory guide is on data retrieval and data preparation. Many re- search works have explained in detail numerous examples of Wikipedia data analysis, includ- ing numerical and graphical results. However, in many cases very little attention is paid to explain the data sources that were used in these studies, how these data were prepared before the analysis and additional information that is also available to extend the analysis in future studies. This is rather unfortunate since, in general, data retrieval and data preparation usu- ally consumes no less than 75% of the whole time devoted to quantitative analysis, specially in very large datasets. As a result, the main goal of this document is to fill in this gap, providing detailed de- scriptions of available data sources in Wikipedia, and practical methods and tools to prepare these data for the analysis. This is the focus of the first part, dealing with data retrieval and preparation. It also presents an overview of useful existing tools and frameworks to facilitate this process. The second part includes a description of a general methodology to undertake quantitative analysis with Wikipedia data, and open source tools that can serve as building blocks for researchers to implement their own analysis process. -
Wikipedia and Wikis Haider, Jutta
Wikipedia and Wikis Haider, Jutta; Sundin, Olof Published in: The Handbook of Peer Production DOI: 10.1002/9781119537151.ch13 2021 Document Version: Peer reviewed version (aka post-print) Link to publication Citation for published version (APA): Haider, J., & Sundin, O. (2021). Wikipedia and Wikis. In M. O'Neil, C. Pentzold, & S. Toupin (Eds.), The Handbook of Peer Production (pp. 169-184). (Wiley Handbooks in Communication and Media Series ). Wiley. https://doi.org/10.1002/9781119537151.ch13 Total number of authors: 2 Creative Commons License: CC BY-NC-ND General rights Unless other specific re-use rights are stated the following general rights apply: Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal Read more about Creative commons licenses: https://creativecommons.org/licenses/ Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. LUND UNIVERSITY PO Box 117 221 00 Lund +46 46-222 00 00 This is the author’s version of a chapter accepted for publication in the Handbook of Peer Production.