Dbpedia-Acrystallization Point for the Web of Data
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A Macro-Micro Biological Tour of Wikidata Wikimania North America Preconference – Montreal - August 10, 2017
A Macro-Micro Biological Tour of Wikidata Wikimania North America Preconference – Montreal - August 10, 2017 Slide No. 1: Overview Thank you for joining me in this “Macro-Micro Biological Tour of Wikidata.” This tour will be a personal journey into the world of Wikidata to look at two extremes of living things – the Macro or Human scale, and the Micro or microbiological scale. I am most pleased to have as my traveling companion on this tour Dr. Yongqun He from the University of Michigan Medical Research Center. Yongqun goes by the name “Oliver” in the US, but currently he is in Beijing, keeping in touch by email and Skype. I will first describe how this adventure was conceived, and then describe what we have found, provide the conclusions we have reached, and offer some recommendations for the future. Slide No. 2: How We Got Here and Why (1) My adventure began before I had this beard, which I have been growing in order to look like a pirate in our local community theatre production of the Pirates of Penzance in September. In fact, my adventure began about two years ago when I made the following conjecture: There is an objective reality underlying human history, historical information is now in digital form, and current computer technology and emerging semantic web techniques should be able to analyze this information. By doing so, it may be possible to accurately describe the causal factors. It may not be possible to show true cause and effect relationships, but it should at least be able to disprove false narratives. -
Factsheet En V
WIKIMANIA 2013 CONFIRMED IN HK! The Wikimedia Foundation, an international nonprofit organization behind the Wikipedia, the largest online encyclopedia, announced this morning (3-May-2012, HKT) that it will stage its 2013 Wikimania conference in Hong Kong. QUICK FACTS Date: 7-11 August 2013 Hosts: Wikimedia Foundation Wikimedia Hong Kong Co-Host: DotAsia Venue: HK Polytechnic University (PolyU) Planned attendance: 700-1,000 Main Hall: Jockey Club Auditorium Wikimania 2011 group photo. Credits: Itzik Edri [1] WIKIMANIA Hong Kong 2013 WHAT IS WIKIMANIA? Wikimania is an annual international conference for users of the wiki projects operated by the Wikimedia Foundation (such as Wikipedia, Wikimedia Commons and Wiktionary). Topics of Wikimania presentations and (black circled) with various discussions include Wikimedia Wikimedia Foundation Projects projects, other wikis, opensource software, free knowledge and free content, and the different social and technical aspects which relate to these topics. The 2012 conference was held in Washington DC, with an attendance of 1400. [2] WIKIMANIA Hong Kong 2013 PAST WIKIMANIAS A collage of different logos of previous Wikimania [3] WIKIMANIA Hong Kong 2013 INITIAL SCHEDULE Date Wed 7th Thu 8th Fri 9th Sat 10th Sun 11th Main Main Main Pre-conference Pre-conference Time conference day conference day conference day day 1 day 2 1 2 3 Opening Jimbo's Keynote Morning Keynote Speech ceremony Board Panel Developer and Chapters and board meetings board meetings Parallel Chinese Late morning and English Parallel sessions Parallel sessions sessions Lunch break Lunch Lunch break Lunch break VIP party Parallel sessions Developer and Chapters and Afternoon Parallel sessions Parallel sessions Closing board meetings board meetings ceremony Beach party and Evening Welcome party barbecue BIDDING PROCESS Hong Kong is chosen after a five-month official bidding process, during which competing potential host cities present their case to a Wikimania jury comprising Wikimedia Foundation staff and past Wikimania organizers. -
Wikipedia's Economic Value
WIKIPEDIA’S ECONOMIC VALUE Jonathan Band and Jonathan Gerafi policybandwidth In the copyright policy debate, proponents of strong copyright protection tend to be dismissive of the quality of freely available content. In response to counter- examples such as open access scholarly publications and advertising-supported business models (e.g., newspaper websites and the over-the-air television broadcasts viewed by 50 million Americans), the strong copyright proponents center their attack on amateur content. In this narrative, YouTube is for cat videos and Wikipedia is a wildly unreliable source of information. Recent studies, however, indicate that the volunteer-written and -edited Wikipedia is no less reliable than professionally edited encyclopedias such as the Encyclopedia Britannica.1 Moreover, Wikipedia has far broader coverage. Britannica, which discontinued its print edition in 2012 and now appears only online, contains 120,000 articles, all in English. Wikipedia, by contrast, has 4.3 million articles in English and a total of 22 million articles in 285 languages. Wikipedia attracts more than 470 million unique visitors a month who view over 19 billion pages.2 According to Alexa, it is the sixth most visited website in the world.3 Wikipedia, therefore, is a shining example of valuable content created by non- professionals. Is there a way to measure the economic value of this content? Because Wikipedia is created by volunteers, is administered by a non-profit foundation, and is distributed for free, the normal means of measuring value— such as revenue, market capitalization, and book value—do not directly apply. Nonetheless, there are a variety of methods for estimating its value in terms of its market value, its replacement cost, and the value it creates for its users. -
A Comparison of On-Line Computer Science Citation Databases
A Comparison of On-Line Computer Science Citation Databases Vaclav Petricek1,IngemarJ.Cox1,HuiHan2, Isaac G. Councill3, and C. Lee Giles3 1 University College London, WC1E 6BT, Gower Street, London, United Kingdom [email protected], [email protected] 2 Yahoo! Inc., 701 First Avenue, Sunnyvale, CA, 94089 [email protected] 3 The School of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802, USA [email protected] [email protected] Abstract. This paper examines the difference and similarities between the two on-line computer science citation databases DBLP and CiteSeer. The database entries in DBLP are inserted manually while the CiteSeer entries are obtained autonomously via a crawl of the Web and automatic processing of user submissions. CiteSeer’s autonomous citation database can be considered a form of self-selected on-line survey. It is important to understand the limitations of such databases, particularly when citation information is used to assess the performance of authors, institutions and funding bodies. We show that the CiteSeer database contains considerably fewer single author papers. This bias can be modeled by an exponential process with intuitive explanation. The model permits us to predict that the DBLP database covers approximately 24% of the entire literature of Computer Science. CiteSeer is also biased against low-cited papers. Despite their difference, both databases exhibit similar and signif- icantly different citation distributions compared with previous analysis of the Physics community. In both databases, we also observe that the number of authors per paper has been increasing over time. 1 Introduction Several public1 databases of research papers became available due to the advent of the Web [1,22,5,3,2,4,8] These databases collect papers in different scientific disciplines, index them and annotate them with additional metadata. -
Linking Educational Resources on Data Science
The Thirty-First AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI-19) Linking Educational Resources on Data Science Jose´ Luis Ambite,1 Jonathan Gordon,2∗ Lily Fierro,1 Gully Burns,1 Joel Mathew1 1USC Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, California 90292, USA 2Department of Computer Science, Vassar College, 124 Raymond Ave, Poughkeepsie, New York 12604, USA [email protected], [email protected], flfierro, burns, [email protected] Abstract linked data (Heath and Bizer 2011) with references to well- known entities in DBpedia (Auer et al. 2007), DBLP (Ley The availability of massive datasets in genetics, neuroimag- 2002), and ORCID (orcid.org). Here, we detail the meth- ing, mobile health, and other subfields of biology and ods applied to specific problems in building ERuDIte. First, medicine promises new insights but also poses significant we provide an overview of our project. In later sections, we challenges. To realize the potential of big data in biomedicine, the National Institutes of Health launched the Big Data to describe our resource collection effort, the resource schema, Knowledge (BD2K) initiative, funding several centers of ex- the topic ontology, our linkage approach, and the linked data cellence in biomedical data analysis and a Training Coordi- resource provided. nating Center (TCC) tasked with facilitating online and in- The National Institutes of Health (NIH) launched the Big person training of biomedical researchers in data science. A Data to Knowledge (BD2K) initiative to fulfill the promise major initiative of the BD2K TCC is to automatically identify, of biomedical “big data” (Ohno-Machado 2014). The NIH describe, and organize data science training resources avail- BD2K program funded 15 major centers to investigate how able on the Web and provide personalized training paths for data science can benefit diverse fields of biomedical research users. -
Wikimedia Conferentie Nederland 2012 Conferentieboek
http://www.wikimediaconferentie.nl WCN 2012 Conferentieboek CC-BY-SA 9:30–9:40 Opening 9:45–10:30 Lydia Pintscher: Introduction to Wikidata — the next big thing for Wikipedia and the world Wikipedia in het onderwijs Technische kant van wiki’s Wiki-gemeenschappen 10:45–11:30 Jos Punie Ralf Lämmel Sarah Morassi & Ziko van Dijk Het gebruik van Wikimedia Commons en Wikibooks in Community and ontology support for the Wikimedia Nederland, wat is dat precies? interactieve onderwijsvormen voor het secundaire onderwijs 101wiki 11:45–12:30 Tim Ruijters Sandra Fauconnier Een passie voor leren, de Nederlandse wikiversiteit Projecten van Wikimedia Nederland in 2012 en verder Bliksemsessie …+discussie …+vragensessie Lunch 13:15–14:15 Jimmy Wales 14:30–14:50 Wim Muskee Amit Bronner Lotte Belice Baltussen Wikipedia in Edurep Bridging the Gap of Multilingual Diversity Open Cultuur Data: Een bottom-up initiatief vanuit de erfgoedsector 14:55–15:15 Teun Lucassen Gerard Kuys Finne Boonen Scholieren op Wikipedia Onderwerpen vinden met DBpedia Blijf je of ga je weg? 15:30–15:50 Laura van Broekhoven & Jan Auke Brink Jeroen De Dauw Jan-Bart de Vreede 15:55–16:15 Wetenschappelijke stagiairs vertalen onderzoek naar Structured Data in MediaWiki Wikiwijs in vergelijking tot Wikiversity en Wikibooks Wikipedia–lemma 16:20–17:15 Prijsuitreiking van Wiki Loves Monuments Nederland 17:20–17:30 Afsluiting 17:30–18:30 Borrel Inhoudsopgave Organisatie 2 Voorwoord 3 09:45{10:30: Lydia Pintscher 4 13:15{14:15: Jimmy Wales 4 Wikipedia in het onderwijs 5 11:00{11:45: Jos Punie -
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. -
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. -
Analysis of Computer Science Communities Based on DBLP
Analysis of Computer Science Communities Based on DBLP Maria Biryukov1 and Cailing Dong1,2 1 Faculty of Science, Technology and Communications, MINE group, University of Luxembourg, Luxembourg [email protected] 2 School of Computer Science and Technology, Shandong University, Jinan, 250101, China cailing [email protected] Abstract. It is popular nowadays to bring techniques from bibliometrics and scientometrics into the world of digital libraries to explore mecha- nisms which underlie community development. In this paper we use the DBLP data to investigate the author’s scientific career, and analyze some of the computer science communities. We compare them in terms of pro- ductivity and population stability, and use these features to compare the sets of top-ranked conferences with their lower ranked counterparts.1 Keywords: bibliographic databases, author profiling, scientific commu- nities, bibliometrics. 1 Introduction Computer science is a broad and constantly growing field. It comprises various subareas each of which has its own specialization and characteristic features. While different in size and granularity, research areas and conferences can be thought of as scientific communities that bring together specialists sharing simi- lar interests. What is specific about conferences is that in addition to scope, par- ticipating scientists and regularity, they are also characterized by level. In each area there is a certain number of commonly agreed upon top ranked venues, and many others – with the lower rank or unranked. In this work we aim at finding out how the communities represented by different research fields and conferences are evolving and communicating to each other. To answer this question we sur- vey the development of the author career, compare various research areas to each other, and finally, try to identify features that would allow to distinguish between venues of different rank. -
Chaudron: Extending Dbpedia with Measurement Julien Subercaze
Chaudron: Extending DBpedia with measurement Julien Subercaze To cite this version: Julien Subercaze. Chaudron: Extending DBpedia with measurement. 14th European Semantic Web Conference, Eva Blomqvist, Diana Maynard, Aldo Gangemi, May 2017, Portoroz, Slovenia. hal- 01477214 HAL Id: hal-01477214 https://hal.archives-ouvertes.fr/hal-01477214 Submitted on 27 Feb 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Chaudron: Extending DBpedia with measurement Julien Subercaze1 Univ Lyon, UJM-Saint-Etienne, CNRS Laboratoire Hubert Curien UMR 5516, F-42023, SAINT-ETIENNE, France [email protected] Abstract. Wikipedia is the largest collaborative encyclopedia and is used as the source for DBpedia, a central dataset of the LOD cloud. Wikipedia contains numerous numerical measures on the entities it describes, as per the general character of the data it encompasses. The DBpedia In- formation Extraction Framework transforms semi-structured data from Wikipedia into structured RDF. However this extraction framework of- fers a limited support to handle measurement in Wikipedia. In this paper, we describe the automated process that enables the creation of the Chaudron dataset. We propose an alternative extraction to the tra- ditional mapping creation from Wikipedia dump, by also using the ren- dered HTML to avoid the template transclusion issue. -
Wiki-Metasemantik: a Wikipedia-Derived Query Expansion Approach Based on Network Properties
Wiki-MetaSemantik: A Wikipedia-derived Query Expansion Approach based on Network Properties D. Puspitaningrum1, G. Yulianti2, I.S.W.B. Prasetya3 1,2Department of Computer Science, The University of Bengkulu WR Supratman St., Kandang Limun, Bengkulu 38371, Indonesia 3Department of Information and Computing Sciences, Utrecht University PO Box 80.089, 3508 TB Utrecht, The Netherlands E-mails: [email protected], [email protected], [email protected] Pseudo-Relevance Feedback (PRF) query expansions suffer Abstract- This paper discusses the use of Wikipedia for building from several drawbacks such as query-topic drift [8][10] and semantic ontologies to do Query Expansion (QE) in order to inefficiency [21]. Al-Shboul and Myaeng [1] proposed a improve the search results of search engines. In this technique, technique to alleviate topic drift caused by words ambiguity selecting related Wikipedia concepts becomes important. We and synonymous uses of words by utilizing semantic propose the use of network properties (degree, closeness, and pageRank) to build an ontology graph of user query concept annotations in Wikipedia pages, and enrich queries with which is derived directly from Wikipedia structures. The context disambiguating phrases. Also, in order to avoid resulting expansion system is called Wiki-MetaSemantik. We expansion of mistranslated words, a query expansion method tested this system against other online thesauruses and ontology using link texts of a Wikipedia page has been proposed [9]. based QE in both individual and meta-search engines setups. Furthermore, since not all hyperlinks are helpful for QE task Despite that our system has to build a Wikipedia ontology graph though (e.g. -
How Do Computer Scientists Use Google Scholar?: a Survey of User Interest in Elements on Serps and Author Profile Pages
BIR 2019 Workshop on Bibliometric-enhanced Information Retrieval How do Computer Scientists Use Google Scholar?: A Survey of User Interest in Elements on SERPs and Author Profile Pages Jaewon Kim, Johanne R. Trippas, Mark Sanderson, Zhifeng Bao, and W. Bruce Croft RMIT University, Melbourne, Australia fjaewon.kim, johanne.trippas, mark.sanderson, zhifeng.bao, [email protected] Abstract. In this paper, we explore user interest in elements on Google Scholar's search engine result pages (SERPs) and author profile pages (APPs) through a survey in order to predict search behavior. We inves- tigate the effects of different query intents (keyword and title) on SERPs and research area familiarities (familiar and less-familiar) on SERPs and APPs. Our findings show that user interest is affected by the re- spondents' research area familiarity, whereas there is no effect due to the different query intents, and that users tend to distribute different levels of interest to elements on SERPs and APPs. We believe that this study provides the basis for understanding search behavior in using Google Scholar. Keywords: Academic search · Google Scholar · Survey study 1 Introduction Academic search engines (ASEs) such as Google Scholar1 and Microsoft Aca- demic2, which are specialized for searching scholarly literature, are now com- monly used to find relevant papers. Among the ASEs, Google Scholar covers about 80-90% of the publications in the world [7] and also provides individual profiles with three types of citation namely, total citation number, h-index, and h10-index. Although the Google Scholar website3 states that the ranking is de- cided by authors, publishers, numbers and times of citation, and even considers the full text of each document, there is a phenomenon called the Google Scholar effect [11].