Wikidata and Libraries: Facilitating Open Knowledge
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What Do Wikidata and Wikipedia Have in Common? an Analysis of Their Use of External References
What do Wikidata and Wikipedia Have in Common? An Analysis of their Use of External References Alessandro Piscopo Pavlos Vougiouklis Lucie-Aimée Kaffee University of Southampton University of Southampton University of Southampton United Kingdom United Kingdom United Kingdom [email protected] [email protected] [email protected] Christopher Phethean Jonathon Hare Elena Simperl University of Southampton University of Southampton University of Southampton United Kingdom United Kingdom United Kingdom [email protected] [email protected] [email protected] ABSTRACT one hundred thousand. These users have gathered facts about Wikidata is a community-driven knowledge graph, strongly around 24 million entities and are able, at least theoretically, linked to Wikipedia. However, the connection between the to further expand the coverage of the knowledge graph and two projects has been sporadically explored. We investigated continuously keep it updated and correct. This is an advantage the relationship between the two projects in terms of the in- compared to a project like DBpedia, where data is periodically formation they contain by looking at their external references. extracted from Wikipedia and must first be modified on the Our findings show that while only a small number of sources is online encyclopedia in order to be corrected. directly reused across Wikidata and Wikipedia, references of- Another strength is that all the data in Wikidata can be openly ten point to the same domain. Furthermore, Wikidata appears reused and shared without requiring any attribution, as it is to use less Anglo-American-centred sources. These results released under a CC0 licence1. -
Position Description Addenda
POSITION DESCRIPTION January 2014 Wikimedia Foundation Executive Director - Addenda The Wikimedia Foundation is a radically transparent organization, and much information can be found at www.wikimediafoundation.org . That said, certain information might be particularly useful to nominators and prospective candidates, including: Announcements pertaining to the Wikimedia Foundation Executive Director Search Kicking off the search for our next Executive Director by Former Wikimedia Foundation Board Chair Kat Walsh An announcement from Wikimedia Foundation ED Sue Gardner by Wikimedia Executive Director Sue Gardner Video Interviews on the Wikimedia Community and Foundation and Its History Some of the values and experiences of the Wikimedia Community are best described directly by those who have been intimately involved in the organization’s dramatic expansion. The following interviews are available for viewing though mOppenheim.TV . • 2013 Interview with Former Wikimedia Board Chair Kat Walsh • 2013 Interview with Wikimedia Executive Director Sue Gardner • 2009 Interview with Wikimedia Executive Director Sue Gardner Guiding Principles of the Wikimedia Foundation and the Wikimedia Community The following article by Sue Gardner, the current Executive Director of the Wikimedia Foundation, has received broad distribution and summarizes some of the core cultural values shared by Wikimedia’s staff, board and community. Topics covered include: • Freedom and open source • Serving every human being • Transparency • Accountability • Stewardship • Shared power • Internationalism • Free speech • Independence More information can be found at: https://meta.wikimedia.org/wiki/User:Sue_Gardner/Wikimedia_Foundation_Guiding_Principles Wikimedia Policies The Wikimedia Foundation has an extensive list of policies and procedures available online at: http://wikimediafoundation.org/wiki/Policies Wikimedia Projects All major projects of the Wikimedia Foundation are collaboratively developed by users around the world using the MediaWiki software. -
Knihovny Současnosti 2016
Sdružení knihoven ČR Ústřední knihovnická rada ČR Knihovny současnosti 2016 Sborník z 24. ročníku knihovnické konference, konané pod záštitou Mgr. Daniela Hermana, ministra kultury ČR, a Ing. Jiřího Rozbořila, hejtmana Olomouckého kraje, pořádané ve dnech 6.–8. září 2016 v areálu Univerzity Palackého v Olomouci Prosinec 2016 Vydáno za finanční podpory Ministerstva kultury v rámci dotačního programu Knihovny 21. století. © Sdružení knihoven ČR, Praha 2016 Konference se konala pod záštitou Mgr. Daniela Hermana, ministra kultury ČR, a Ing. Jiřího Rozbořila, hejtmana Olomouckého kraje. OBSAH Úvodní slovo .......................................................................................... 8 Jak to vidí jinde .......................................................................................... 9 Úvod ...................................................................................................... 10 Inga Lundén: Všechno je o důvěře. Knihovny tvoří kompaktní města ...................................................................................................... 11 Knihovna jako vzdělávací instituce ...................................................... 21 Úvodní slovo k sekci „Knihovna jako vzdělávací instituce“ ......... 22 Mgr. Pavlína Mazáčová, Ph.D.: Inkluzivní přístup k edukaci informační gramotnosti v praxi učícího knihovníka ...................... 24 PhDr. Martin Krčál: Spolupráce knihoven a středních škol .......... 38 Mgr. et Mgr. Zuzana Teplíková: Vzdělávací aktivity Citace.com pro knihovny ....................................................................................... -
A Topic-Aligned Multilingual Corpus of Wikipedia Articles for Studying Information Asymmetry in Low Resource Languages
Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 2373–2380 Marseille, 11–16 May 2020 c European Language Resources Association (ELRA), licensed under CC-BY-NC A Topic-Aligned Multilingual Corpus of Wikipedia Articles for Studying Information Asymmetry in Low Resource Languages Dwaipayan Roy, Sumit Bhatia, Prateek Jain GESIS - Cologne, IBM Research - Delhi, IIIT - Delhi [email protected], [email protected], [email protected] Abstract Wikipedia is the largest web-based open encyclopedia covering more than three hundred languages. However, different language editions of Wikipedia differ significantly in terms of their information coverage. We present a systematic comparison of information coverage in English Wikipedia (most exhaustive) and Wikipedias in eight other widely spoken languages (Arabic, German, Hindi, Korean, Portuguese, Russian, Spanish and Turkish). We analyze the content present in the respective Wikipedias in terms of the coverage of topics as well as the depth of coverage of topics included in these Wikipedias. Our analysis quantifies and provides useful insights about the information gap that exists between different language editions of Wikipedia and offers a roadmap for the Information Retrieval (IR) community to bridge this gap. Keywords: Wikipedia, Knowledge base, Information gap 1. Introduction other with respect to the coverage of topics as well as Wikipedia is the largest web-based encyclopedia covering the amount of information about overlapping topics. -
Wikipedia Knowledge Graph with Deepdive
The Workshops of the Tenth International AAAI Conference on Web and Social Media Wiki: Technical Report WS-16-17 Wikipedia Knowledge Graph with DeepDive Thomas Palomares Youssef Ahres [email protected] [email protected] Juhana Kangaspunta Christopher Re´ [email protected] [email protected] Abstract This paper is organized as follows: first, we review the related work and give a general overview of DeepDive. Sec- Despite the tremendous amount of information on Wikipedia, ond, starting from the data preprocessing, we detail the gen- only a very small amount is structured. Most of the informa- eral methodology used. Then, we detail two applications tion is embedded in unstructured text and extracting it is a non trivial challenge. In this paper, we propose a full pipeline that follow this pipeline along with their specific challenges built on top of DeepDive to successfully extract meaningful and solutions. Finally, we report the results of these applica- relations from the Wikipedia text corpus. We evaluated the tions and discuss the next steps to continue populating Wiki- system by extracting company-founders and family relations data and improve the current system to extract more relations from the text. As a result, we extracted more than 140,000 with a high precision. distinct relations with an average precision above 90%. Background & Related Work Introduction Until recently, populating the large knowledge bases relied on direct contributions from human volunteers as well With the perpetual growth of web usage, the amount as integration of existing repositories such as Wikipedia of unstructured data grows exponentially. Extract- info boxes. These methods are limited by the available ing facts and assertions to store them in a struc- structured data and by human power. -
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. -
News Release
NEWS RELEASE For immediate release Sue Gardner to deliver 16th annual LaFontaine-Baldwin Lecture Former head of CBC.ca and Wikimedia Foundation to open 6 Degrees Toronto TORONTO, August 13, 2018—6 Degrees announces that the 2018 LaFontaine-Baldwin Lecture will be delivered by leading digital pioneer Sue Gardner. The lecture will be given on September 24 as part of 6 Degrees Toronto, a project of the Institute for Canadian Citizenship. As senior director of CBC.ca, Gardner reinvented the Canadian Broadcasting Corporation’s place in the world of digital news. Later, as executive director of the Wikimedia Foundation, she played a crucial role in the explosive growth of Wikipedia. The San Francisco–based Gardner continues to be a sought-after global thought-leader: she currently advises media and technology companies, and serves on the boards of Privacy International and the Organized Crime and Corruption Reporting Project. “Sue Gardner is on the forefront of ideas on technology, democracy, and women’s roles in our society,” said ICC Co-founder and Co-chair John Ralston Saul. “As we witness the alarming erosion of democratic institutions, I can’t think of a more relevant voice to address the challenges ahead. I can’t wait to welcome Sue home to Toronto to deliver this year’s LaFontaine-Baldwin Lecture.” Titled Dark Times Ahead: Taking Back Truth, Freedom, and Technology, the interactive event will include Gardner in conversation with John Ralston Saul. Gardner joins an illustrious list of past LaFontaine-Baldwin lecturers, including His Highness the Aga Khan, Naomi Klein, Shawn A-in-chut Atleo, Michael Sandel, and Naheed Nenshi. -
Omnipedia: Bridging the Wikipedia Language
Omnipedia: Bridging the Wikipedia Language Gap Patti Bao*†, Brent Hecht†, Samuel Carton†, Mahmood Quaderi†, Michael Horn†§, Darren Gergle*† *Communication Studies, †Electrical Engineering & Computer Science, §Learning Sciences Northwestern University {patti,brent,sam.carton,quaderi}@u.northwestern.edu, {michael-horn,dgergle}@northwestern.edu ABSTRACT language edition contains its own cultural viewpoints on a We present Omnipedia, a system that allows Wikipedia large number of topics [7, 14, 15, 27]. On the other hand, readers to gain insight from up to 25 language editions of the language barrier serves to silo knowledge [2, 4, 33], Wikipedia simultaneously. Omnipedia highlights the slowing the transfer of less culturally imbued information similarities and differences that exist among Wikipedia between language editions and preventing Wikipedia’s 422 language editions, and makes salient information that is million monthly visitors [12] from accessing most of the unique to each language as well as that which is shared information on the site. more widely. We detail solutions to numerous front-end and algorithmic challenges inherent to providing users with In this paper, we present Omnipedia, a system that attempts a multilingual Wikipedia experience. These include to remedy this situation at a large scale. It reduces the silo visualizing content in a language-neutral way and aligning effect by providing users with structured access in their data in the face of diverse information organization native language to over 7.5 million concepts from up to 25 strategies. We present a study of Omnipedia that language editions of Wikipedia. At the same time, it characterizes how people interact with information using a highlights similarities and differences between each of the multilingual lens. -
Title of Thesis: ABSTRACT CLASSIFYING BIAS
ABSTRACT Title of Thesis: CLASSIFYING BIAS IN LARGE MULTILINGUAL CORPORA VIA CROWDSOURCING AND TOPIC MODELING Team BIASES: Brianna Caljean, Katherine Calvert, Ashley Chang, Elliot Frank, Rosana Garay Jáuregui, Geoffrey Palo, Ryan Rinker, Gareth Weakly, Nicolette Wolfrey, William Zhang Thesis Directed By: Dr. David Zajic, Ph.D. Our project extends previous algorithmic approaches to finding bias in large text corpora. We used multilingual topic modeling to examine language-specific bias in the English, Spanish, and Russian versions of Wikipedia. In particular, we placed Spanish articles discussing the Cold War on a Russian-English viewpoint spectrum based on similarity in topic distribution. We then crowdsourced human annotations of Spanish Wikipedia articles for comparison to the topic model. Our hypothesis was that human annotators and topic modeling algorithms would provide correlated results for bias. However, that was not the case. Our annotators indicated that humans were more perceptive of sentiment in article text than topic distribution, which suggests that our classifier provides a different perspective on a text’s bias. CLASSIFYING BIAS IN LARGE MULTILINGUAL CORPORA VIA CROWDSOURCING AND TOPIC MODELING by Team BIASES: Brianna Caljean, Katherine Calvert, Ashley Chang, Elliot Frank, Rosana Garay Jáuregui, Geoffrey Palo, Ryan Rinker, Gareth Weakly, Nicolette Wolfrey, William Zhang Thesis submitted in partial fulfillment of the requirements of the Gemstone Honors Program, University of Maryland, 2018 Advisory Committee: Dr. David Zajic, Chair Dr. Brian Butler Dr. Marine Carpuat Dr. Melanie Kill Dr. Philip Resnik Mr. Ed Summers © Copyright by Team BIASES: Brianna Caljean, Katherine Calvert, Ashley Chang, Elliot Frank, Rosana Garay Jáuregui, Geoffrey Palo, Ryan Rinker, Gareth Weakly, Nicolette Wolfrey, William Zhang 2018 Acknowledgements We would like to express our sincerest gratitude to our mentor, Dr. -
Towards a Korean Dbpedia and an Approach for Complementing the Korean Wikipedia Based on Dbpedia
Towards a Korean DBpedia and an Approach for Complementing the Korean Wikipedia based on DBpedia Eun-kyung Kim1, Matthias Weidl2, Key-Sun Choi1, S¨orenAuer2 1 Semantic Web Research Center, CS Department, KAIST, Korea, 305-701 2 Universit¨at Leipzig, Department of Computer Science, Johannisgasse 26, D-04103 Leipzig, Germany [email protected], [email protected] [email protected], [email protected] Abstract. In the first part of this paper we report about experiences when applying the DBpedia extraction framework to the Korean Wikipedia. We improved the extraction of non-Latin characters and extended the framework with pluggable internationalization components in order to fa- cilitate the extraction of localized information. With these improvements we almost doubled the amount of extracted triples. We also will present the results of the extraction for Korean. In the second part, we present a conceptual study aimed at understanding the impact of international resource synchronization in DBpedia. In the absence of any informa- tion synchronization, each country would construct its own datasets and manage it from its users. Moreover the cooperation across the various countries is adversely affected. Keywords: Synchronization, Wikipedia, DBpedia, Multi-lingual 1 Introduction Wikipedia is the largest encyclopedia of mankind and is written collaboratively by people all around the world. Everybody can access this knowledge as well as add and edit articles. Right now Wikipedia is available in 260 languages and the quality of the articles reached a high level [1]. However, Wikipedia only offers full-text search for this textual information. For that reason, different projects have been started to convert this information into structured knowledge, which can be used by Semantic Web technologies to ask sophisticated queries against Wikipedia. -
An Analysis of Contributions to Wikipedia from Tor
Are anonymity-seekers just like everybody else? An analysis of contributions to Wikipedia from Tor Chau Tran Kaylea Champion Andrea Forte Department of Computer Science & Engineering Department of Communication College of Computing & Informatics New York University University of Washington Drexel University New York, USA Seatle, USA Philadelphia, USA [email protected] [email protected] [email protected] Benjamin Mako Hill Rachel Greenstadt Department of Communication Department of Computer Science & Engineering University of Washington New York University Seatle, USA New York, USA [email protected] [email protected] Abstract—User-generated content sites routinely block contri- butions from users of privacy-enhancing proxies like Tor because of a perception that proxies are a source of vandalism, spam, and abuse. Although these blocks might be effective, collateral damage in the form of unrealized valuable contributions from anonymity seekers is invisible. One of the largest and most important user-generated content sites, Wikipedia, has attempted to block contributions from Tor users since as early as 2005. We demonstrate that these blocks have been imperfect and that thousands of attempts to edit on Wikipedia through Tor have been successful. We draw upon several data sources and analytical techniques to measure and describe the history of Tor editing on Wikipedia over time and to compare contributions from Tor users to those from other groups of Wikipedia users. Fig. 1. Screenshot of the page a user is shown when they attempt to edit the Our analysis suggests that although Tor users who slip through Wikipedia article on “Privacy” while using Tor. Wikipedia’s ban contribute content that is more likely to be reverted and to revert others, their contributions are otherwise similar in quality to those from other unregistered participants and to the initial contributions of registered users. -
Improving Knowledge Base Construction from Robust Infobox Extraction
Improving Knowledge Base Construction from Robust Infobox Extraction Boya Peng∗ Yejin Huh Xiao Ling Michele Banko∗ Apple Inc. 1 Apple Park Way Sentropy Technologies Cupertino, CA, USA {yejin.huh,xiaoling}@apple.com {emma,mbanko}@sentropy.io∗ Abstract knowledge graph created largely by human edi- tors. Only 46% of person entities in Wikidata A capable, automatic Question Answering have birth places available 1. An estimate of 584 (QA) system can provide more complete million facts are maintained in Wikipedia, not in and accurate answers using a comprehen- Wikidata (Hellmann, 2018). A downstream appli- sive knowledge base (KB). One important cation such as Question Answering (QA) will suf- approach to constructing a comprehensive knowledge base is to extract information from fer from this incompleteness, and fail to answer Wikipedia infobox tables to populate an ex- certain questions or even provide an incorrect an- isting KB. Despite previous successes in the swer especially for a question about a list of enti- Infobox Extraction (IBE) problem (e.g., DB- ties due to a closed-world assumption. Previous pedia), three major challenges remain: 1) De- work on enriching and growing existing knowl- terministic extraction patterns used in DBpe- edge bases includes relation extraction on nat- dia are vulnerable to template changes; 2) ural language text (Wu and Weld, 2007; Mintz Over-trusting Wikipedia anchor links can lead to entity disambiguation errors; 3) Heuristic- et al., 2009; Hoffmann et al., 2011; Surdeanu et al., based extraction of unlinkable entities yields 2012; Koch et al., 2014), knowledge base reason- low precision, hurting both accuracy and com- ing from existing facts (Lao et al., 2011; Guu et al., pleteness of the final KB.