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Wikipedia and open access panel
No full textMuch knowledge has always been locked away, throughout history. It’s inaccessible — or expensive to access — for all but a privileged few. Two Internet-era social movements have sought to change that: Wikipedia, which invites any and all to participate in constructing a comprehensive encyclopedia; and the Open Access movement, which maintains that academic research — especially when funded by the public — should be readily accessible to the public, ideally under free licenses that permit virtually unrestricted redistribution.
The goals, tactics, and even communities of both movements overlap strongly. But on occasion, conflicts arise. Last month, Open Access advocate Michael Eisen took issue with the activities of The Wikipedia Library, a project which aims to help Wikipedia’s volunteer editors read and consult otherwise inaccessible publications (typically, academic journals).
Conflicting views on how to engage with closed-access publishers present an opportunity to discuss the goals and visions of both movements. This panel discussion explores the issues and invites discussion and commentary.
Introductory remarks:
Melissa Hagemann, Senior Program Manager, Open Society Foundations’ Information Program; Advisory Board, Wikimedia Foundation.
Panelists:
Michael Eisen: Associate Professor, UC Berkeley; founder, PLoS family of open access journals
Jake Orlowitz: founder, Wikipedia Library program
Rich Schneider: Associate Professor, UCSF School of Medicine; Author of UCSF’s and University of California systems’ Open Access policies
Stephen LaPorte, Legal Counsel, Wikimedia Foundation
John Dove, Former CEO of Credo Reference, first donor to the Wikipedia Library, and open access advocate
Pete Forsyth (moderator): longtime Wikipedia contributor; founder, Wiki Strategies training & consulting compan
Temporal characterization of the requests to Wikipedia
Get PDFThis paper presents an empirical study about the temporal patterns
characterizing the requests submitted by users to Wikipedia.
The study is based on the analysis of the log lines registered by the
Wikimedia Foundation Squid servers after having sent the appropriate
content in response to users' requests. The
analysis has been conducted regarding the ten most visited editions of
Wikipedia and has involved more than 14,000 million log lines
corresponding to the traffic of the entire year 2009. The conducted methodology
has mainly consisted in the parsing and filtering
of users' requests according to the study directives. As a result, relevant information
fields have been finally stored in a database for persistence and further
characterization. In this way, we, first, assessed, whether the traffic to Wikipedia could serve
as a reliable estimator of the overall traffic to all the Wikimedia Foundation
projects. Our subsequent analysis of the temporal evolutions corresponding to
the different types of requests to Wikipedia revealed interesting differences
and similarities among them that can be related to the users' attention to the Encyclopedia.
In addition, we have performed separated characterizations of each Wikipedia edition
to compare their respective evolutions over time
21st-century scholarship and Wikipedia
Get PDFWikipedia, the world’s fifth most-used Web site, is a good illustration of the growing credibility of online resources. In his article in Ariadne earlier this year, “Wikipedia: Reflections on Use and Academic Acceptance”, Brian Whalley described the debates around accuracy and review, in the context of geology. He concluded that ‘If Wikipedia is the first port of call, as it already seems to be, for information requirement traffic, then there is a commitment to build on Open Educational Resources (OERs) of various kinds and improve their quality.’ In a similar approach to the Geological Society event that Whalley describes, Sarah Fahmy of JISC worked with Wikimedia and the British Library on a World War One (WWI) Editathon. There is a rich discourse about the way that academics relate to Wikipedia
Measuring Wikipedia
Get PDFWikipedia, an international project that uses Wiki software to collaboratively create an encyclopaedia, is becoming more and more popular. Everyone can directly edit articles and every edit is recorded. The version history of all articles is freely available and allows a multitude of examinations. This paper gives an overview on Wikipedia research. Wikipedia’s fundamental components, i.e. articles, authors, edits, and links, as well as content and quality are analysed. Possibilities of research are explored including examples and first results. Several
characteristics that are found in Wikipedia, such as exponential growth and scale-free networks are already known in other context. However the Wiki architecture also possesses some intrinsic specialities. General trends are measured that are typical for all Wikipedias but vary between languages in detail
An effective, low-cost measure of semantic relatedness obtained from Wikipedia links
No full textThis paper describes a new technique for obtaining measures of semantic relatedness. Like other recent approaches, it uses Wikipedia to provide structured world knowledge about the terms of interest. Out approach is unique in that it does so using the hyperlink structure of Wikipedia rather than its category hierarchy or textual content. Evaluation with manually defined measures of semantic relatedness reveals this to be an effective compromise between the ease of computation of the former approach and the accuracy of the latter
Topic indexing with Wikipedia
No full textWikipedia article names can be utilized as a controlled vocabulary for identifying the main topics in a document. Wikipedia’s 2M articles cover the terminology of nearly any document collection, which permits controlled indexing in the absence of manually created vocabularies. We combine state-of-art strategies for automatic controlled indexing with Wikipedia’s unique property- a richly hyperlinked encyclopedia. We evaluated the scheme by comparing automatically assigned topics with those chosen manually by human indexers. Analysis of indexing consistency shows that our algorithm outperforms some human subjects
Mining meaning from Wikipedia
No full textWikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks.
This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying Wikipedia to natural language processing; using it to facilitate information retrieval and information extraction; and as a resource for ontology building. The article addresses how Wikipedia is being used as is, how it is being improved and adapted, and how it is being combined with other structures to create entirely new resources. We identify the research groups and individuals involved, and how their work has developed in the last few years. We provide a comprehensive list of the open-source software they have produced. We also discuss the implications of this work for the long-awaited semantic web
Applying Wikipedia to Interactive Information Retrieval
No full textThere are many opportunities to improve the interactivity of information retrieval systems beyond the ubiquitous search box. One idea is to use knowledge bases—e.g. controlled vocabularies, classification schemes, thesauri and ontologies—to organize, describe and navigate the information space. These resources are popular in libraries and specialist collections, but have proven too expensive and narrow to be applied to everyday webscale search. Wikipedia has the potential to bring structured knowledge into more widespread use. This online, collaboratively generated encyclopaedia is one of the largest and most consulted reference works in existence. It is broader, deeper and more agile than the knowledge bases put forward to assist retrieval in the past. Rendering this resource machine-readable is a challenging task that has captured the interest of many researchers. Many see it as a key step required to break the knowledge acquisition bottleneck that crippled previous efforts. This thesis claims that the roadblock can be sidestepped: Wikipedia can be applied effectively to open-domain information retrieval with minimal natural language processing or information extraction. The key is to focus on gathering and applying human-readable rather than machine-readable knowledge. To demonstrate this claim, the thesis tackles three separate problems: extracting knowledge from Wikipedia; connecting it to textual documents; and applying it to the retrieval process. First, we demonstrate that a large thesaurus-like structure can be obtained directly from Wikipedia, and that accurate measures of semantic relatedness can be efficiently mined from it. Second, we show that Wikipedia provides the necessary features and training data for existing data mining techniques to accurately detect and disambiguate topics when they are mentioned in plain text. Third, we provide two systems and user studies that demonstrate the utility of the Wikipedia-derived knowledge base for interactive information retrieval
Clustering documents with active learning using Wikipedia
No full textWikipedia has been applied as a background knowledge base to various text mining problems, but very few attempts have been made to utilize it for document clustering. In this paper we propose to exploit the semantic knowledge in Wikipedia for clustering, enabling the automatic grouping of documents with similar themes. Although clustering is intrinsically unsupervised, recent research has shown that incorporating supervision improves clustering performance, even when limited supervision is provided. The approach presented in this paper applies supervision using active learning. We first utilize Wikipedia to create a concept-based representation of a text document, with each concept associated to a Wikipedia article. We then exploit the semantic relatedness between Wikipedia concepts to find pair-wise instance-level constraints for supervised clustering, guiding clustering towards the direction indicated by the constraints. We test our approach on three standard text document datasets. Empirical results show that our basic document representation strategy yields comparable performance to previous attempts; and adding constraints improves clustering performance further by up to 20%
Wikipedia @ 20
No full textWikipedia's first twenty years: how what began as an experiment in collaboration became the world's most popular reference work. We have been looking things up in Wikipedia for twenty years. What began almost by accident—a wiki attached to a nascent online encyclopedia—has become the world's most popular reference work. Regarded at first as the scholarly equivalent of a Big Mac, Wikipedia is now known for its reliable sourcing and as a bastion of (mostly) reasoned interaction. How has Wikipedia, built on a model of radical collaboration, remained true to its original mission of “free access to the sum of all human knowledge” when other tech phenomena have devolved into advertising platforms? In this book, scholars, activists, and volunteers reflect on Wikipedia's first twenty years, revealing connections across disciplines and borders, languages and data, the professional and personal. The contributors consider Wikipedia's history, the richness of the connections that underpin it, and its founding vision. Their essays look at, among other things, the shift from bewilderment to respect in press coverage of Wikipedia; Wikipedia as “the most important laboratory for social scientific and computing research in history”; and the acknowledgment that “free access” includes not just access to the material but freedom to contribute—that the summation of all human knowledge is biased by who documents it. Contributors Phoebe Ayers, Omer Benjakob, Yochai Benkler, William Beutler, Siko Bouterse, Rebecca Thorndike-Breeze, Amy Carleton, Robert Cummings, LiAnna L. Davis, Siân Evans, Heather Ford, Stephen Harrison, Heather Hart, Benjamin Mako Hill, Dariusz Jemielniak, Brian Keegan, Jackie Koerner, Alexandria Lockett, Jacqueline Mabey, Katherine Maher, Michael Mandiberg, Stephane Coillet-Matillon, Cecelia A. Musselman, Eliza Myrie, Jake Orlowitz, Ian A. Ramjohn, Joseph Reagle, Anasuya Sengupta, Aaron Shaw, Melissa Tamani, Jina Valentine, Matthew Vetter, Adele Vrana, Denny Vrandeci
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