Janette Lehmann

From WikiPapers
Jump to: navigation, search

Janette Lehmann is an author.


Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Reader preferences and behavior on Wikipedia Article quality
Human factors
Reading behavior
Reading interest
HT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media English 2014 Wikipedia is a collaboratively-edited online encyclopaedia that relies on thousands of editors to both contribute articles and maintain their quality. Over the last years, research has extensively investigated this group of users while another group of Wikipedia users, the readers, their preferences and their behavior have not been much studied. This paper makes this group and its %their activities visible and valuable to Wikipedia's editor community. We carried out a study on two datasets covering a 13-months period to obtain insights on users preferences and reading behavior in Wikipedia. We show that the most read articles do not necessarily correspond to those frequently edited, suggesting some degree of non-alignment between user reading preferences and author editing preferences. We also identified that popular and often edited articles are read according to four main patterns, and that how an article is read may change over time. We illustrate how this information can provide valuable insights to Wikipedia's editor community. 0 0
DBpedia and the live extraction of structured data from Wikipedia Data management
Knowledge Extraction
Knowledge management
Program English 2012 Purpose: DBpedia extracts structured information from Wikipedia, interlinks it with other knowledge bases and freely publishes the results on the web using Linked Data and SPARQL. However, the DBpedia release process is heavyweight and releases are sometimes based on several months old data. DBpedia-Live solves this problem by providing a live synchronization method based on the update stream of Wikipedia. This paper seeks to address these issues. Design/methodology/approach: Wikipedia provides DBpedia with a continuous stream of updates, i.e. a stream of articles, which were recently updated. DBpedia-Live processes that stream on the fly to obtain RDF data and stores the extracted data back to DBpedia. DBpedia-Live publishes the newly added/deleted triples in files, in order to enable synchronization between the DBpedia endpoint and other DBpedia mirrors. Findings: During the realization of DBpedia-Live the authors learned that it is crucial to process Wikipedia updates in a priority queue. Recently-updated Wikipedia articles should have the highest priority, over mapping-changes and unmodified pages. An overall finding is that there are plenty of opportunities arising from the emerging Web of Data for librarians. Practical implications: DBpedia had and has a great effect on the Web of Data and became a crystallization point for it. Many companies and researchers use DBpedia and its public services to improve their applications and research approaches. The DBpedia-Live framework improves DBpedia further by timely synchronizing it with Wikipedia, which is relevant for many use cases requiring up-to-date information. Originality/value: The new DBpedia-Live framework adds new features to the old DBpedia-Live framework, e.g. abstract extraction, ontology changes, and changesets publication. 0 0
Seeing similarity in the face of difference: enabling comparison of online production systems Social Network Analysis and Mining 2010 0 0
Update strategies for DBpedia live CEUR Workshop Proceedings English 2010 Wikipedia is one of the largest public information spaces with a huge user community, which collaboratively works on the largest online encyclopedia. Their users add or edit up to 150 thousand wiki pages per day. The DBpedia project extracts RDF from Wikipedia and interlinks it with other knowledge bases. In the DBpedia live extraction mode, Wikipedia edits are instantly processed to update information in DBpedia. Due to the high number of edits and the growth of Wikipedia, the update process has to be very efficient and scalable. In this paper, we present different strategies to tackle this challenging problem and describe how we modified the DBpedia live extraction algorithm to work more efficiently. 0 0
A composite calculation for author activity in Wikis: Accuracy needed Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009 English 2009 Researchers of computer science and social science are increasingly interested in the Social Web and its applications. To improve existing infrastructures, to evaluate the success of available services, and to build new virtual communities and their applications, an understanding of dynamics and evolution of inherent social and informational structures is essential. One key question is how communities which exist in these applications are structured in terms of author contributions. Are there similar contribution patterns in different applications? For example, does the so called onion model revealed from open source software communities apply to Social Web applications as well? In this study, author contributions in the open content project Wikipedia are investigated. Previous studies to evaluate author contributions mainly concentrate on editing activities. Extending this approach, the added significant content and investigation of which author groups contribute the majority of content in terms of activity and significance are considered. Furthermore, the social information space is described by a dynamic collaboration network and the topic coverage of authors is analyzed. In contrast to existing approaches, the position of an author in a social network is incorporated. Finally, a new composite calculation to evaluate author contributions in Wikis is proposed. The action, the content contribution, and the connectedness of an author are integrated into one equation in order to evaluate author activity. 0 0
DBpedia live extraction Lecture Notes in Computer Science English 2009 The DBpedia project extracts information from Wikipedia, interlinks it with other knowledge bases, and makes this data available as RDF. So far the DBpedia project has succeeded in creating one of the largest knowledge bases on the Data Web, which is used in many applications and research prototypes. However, the heavy-weight extraction process has been a drawback. It requires manual effort to produce a new release and the extracted information is not up-to-date. We extended DBpedia with a live extraction framework, which is capable of processing tens of thousands of changes per day in order to consume the constant stream of Wikipedia updates. This allows direct modifications of the knowledge base and closer interaction of users with DBpedia. We also show how the Wikipedia community itself is now able to take part in the DBpedia ontology engineering process and that an interactive roundtrip engineering between Wikipedia and DBpedia is made possible. 0 0
DBpedia: A nucleus for a Web of open data Lecture Notes in Computer Science English 2007 DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human- and machine-consumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data. 0 2
Discovering unknown connections - The DBpedia relationship finder The Social Semantic Web 2007 - Proceedings of the 1st Conference on Social Semantic Web, CSSW 2007 English 2007 The Relationship Finder is a tool for exploring connections between objects in a Semantic Web knowledge base. It offers a new way to get insights about elements in an ontology, in particular for large amounts of instance data. For this reason, we applied the idea to the DBpedia data set, which contains an enormous amount of knowledge extracted from Wikipedia. We describe the workings of the Relationship Finder algorithm and present some interesting statistical discoveries about DBpedia and Wikipedia. 0 0
Onto wiki: A tool for social, semantic collaboration Semantic collaboration
Semantic wiki
Social software
CEUR Workshop Proceedings English 2007 We present OntoWiki, a tool providing support for agile, distributed knowledge engineering scenarios. OntoWiki facilitates the visual presentation of a knowledge base as an information map, with different views on instance data. It enables intuitive authoring of semantic content, with an inline editing mode for editing RDF content, similar to WYSIWYG for text documents. It fosters social collaboration aspects by keeping track of changes, allowing comments and discussion on every single part of a knowledge base, enabling to rate and measure the popularity of content and honoring the activity of users. OntoWiki enhances the browsing and retrieval by offering semantic enhanced search strategies. All these techniques are applied with the ultimate goal of decreasing the entrance barrier for projects and domain experts to collaborate using semantic technologies. In the spirit of the Web 2.0 OntoWiki implements an "architecture of participation" that allows users to add value to the application as they use it. It is available as open-source software and a demonstration platform can be accessed at http://3ba.se. 0 0
What have innsbruck and Leipzig in common? Extracting semantics from wiki content Lecture Notes in Computer Science English 2007 Wikis are established means for the collaborative authoring, versioning and publishing of textual articles. The Wikipedia project, for example, succeeded in creating the by far largest encyclopedia just on the basis of a wiki. Recently, several approaches have been proposed on how to extend wikis to allow the creation of structured and semantically enriched content. However, the means for creating semantically enriched structured content are already available and are, although unconsciously, even used by Wikipedia authors. In this article, we present a method for revealing this structured content by extracting information from template instances. We suggest ways to efficiently query the vast amount of extracted information (e.g. more than 8 million RDF statements for the English Wikipedia version alone), leading to astonishing query answering possibilities (such as for the title question). We analyze the quality of the extracted content, and propose strategies for quality improvements with just minor modifications of the wiki systems being currently used. 0 0