PARC's Augmented Social Cognition researchers -- who come from cognitive psychology, computer science, HCI, CSCW, and other disciplines -- focus on understanding how to "enhance a group of people's ability to remember, think, and reason". Through Social Web systems like social bookmarking sites, blogs, Wikis, and more, we can finally study, in detail, these types of enhancements on a very large scale.
Here we summarize recent work and early findings such as: (1) how conflict and coordination have played out in Wikipedia, and how social transparency might affect reader trust; (2) how decreasing interaction costs might change participation in social tagging systems; and (3) how computation can help organize user-generated content and metadata.}, acmid = {1559959}, address = {New York, NY, USA}, author = {Chi, Ed H.}, booktitle = {Proceedings of the 2009 ACM SIGMOD International Conference on Management of data}, doi = {10.1145/1559845.1559959}, interhash = {d24a64ce5e95bae4de9329a467342dee}, intrahash = {d09b484b1036ca8273743cac1992dd7f}, isbn = {978-1-60558-551-2}, location = {Providence, Rhode Island, USA}, numpages = {12}, pages = {973--984}, publisher = {ACM}, title = {Augmented social cognition: using social web technology to enhance the ability of groups to remember, think, and reason}, url = {http://doi.acm.org/10.1145/1559845.1559959}, year = 2009 } @inproceedings{doerfel2012leveraging, abstract = {The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.}, address = {New York, NY, USA}, author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, doi = {10.1145/2365934.2365937}, interhash = {beb2c81daf975eeed6e01e1b412196b1}, intrahash = {64bf590675a833770b7d284871435a8d}, isbn = {978-1-4503-1638-5}, location = {Dublin, Ireland}, month = sep, pages = {9--16}, publisher = {ACM}, title = {Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation }, url = {http://doi.acm.org/10.1145/2365934.2365937}, year = 2012 } @inproceedings{landia2012extending, abstract = {Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags. Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.}, address = {New York, NY, USA}, author = {Landia, Nikolas and Anand, Sarabjot Singh and Hotho, Andreas and Jäschke, Robert and Doerfel, Stephan and Mitzlaff, Folke}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, doi = {10.1145/2365934.2365936}, interhash = {7400e35f8d412d15722fe3399aba14a3}, intrahash = {b16dabcd7e17b673c34608ac820ce3c7}, isbn = {978-1-4503-1638-5}, location = {Dublin, Ireland}, month = sep, pages = {1--8}, publisher = {ACM}, title = {Extending FolkRank with Content Data}, url = {http://doi.acm.org/10.1145/2365934.2365936}, year = 2012 } @mastersthesis{flohr2011extraktion, abstract = {Informationen so aufzubereiten, dass sie für eine bestimmte Situation nützlich sind, ist eine große Herausforderung. In solchen Situationen soll ein Benutzer, wenn er sich an einem fremden Ort befindet, mit Hilfe des Android Smartphone interessante und wis- senswerte Informationen anzeigen lassen. Um dies bewerkstelligen zu können, muss es eine georeferenzierte Informationsquelle geben. Außerdem muss ein Konzept vor- handen sein, um diese Daten zu sammeln und so aufzubereiten, dass der Benutzer diese auch nützlich findet. Es muss eine Visualisierung dieser Daten geben, da der Platz zur Anzeige auf Smartphones sehr begrenzt ist. Als georeferenzierte Informationsquelle wird die Online-Enzyklopädie Wikipedia ge- nutzt, diese ist frei zugänglich und auch sehr umfassend. In dieser Arbeit wird das Konzept zur Sammlung und Aufbereitung von relevanten Daten behandelt. Zur In- formationsvisualisierung wird die Methode der Schlagwortwolke (engl. Tag-Cloud) verwendet. It is a major challenge to prepare useful information for a particular situation. In this situation an Android smartphone user wants to display interesting and important facts about an unknown place. To manage this task existence of a geo-referenced source of information has to be ensured. In order to collect and prepare this data a creation of concept is needed. Due to limited display space, it is necessary to construct a suitable visualization of this data. Wikipedia is used as a geo-referenced information resource, because it has open-access and it offers global geo-referenced information. This thesis covers the concept of col- lecting and preparing relevant data. To visualize information a tag cloud is used. }, author = {Flohr, Oliver}, interhash = {5d1f4da4964062ed6598fe8d8be8b591}, intrahash = {a28959724af1907e7fc67a68e648c14c}, month = aug, school = {Gottfried Wilhelm Leibniz Universität Hannover}, title = {Extraktion und Visualisierung ortsbezogener Informationen mit Tag-Clouds}, type = {bachelor thesis}, url = {http://www.se.uni-hannover.de/pub/File/pdfpapers/Flohr2011a.pdf}, year = 2011 } @inproceedings{gemmell2009improving, abstract = {Collaborative tagging applications allow users to annotate online resources. The result is a complex tapestry of interrelated users, resources and tags often called a folksonomy. Folksonomies present an attractive target for data mining applications such as tag recommenders. A challenge of tag recommendation remains the adaptation of traditional recommendation techniques originally designed to work with two dimensional data. To date the most successful recommenders have been graph based approaches which explicitly connects all three components of the folksonomy. In this paper we speculate that graph based tag recommendation can be improved by coupling it with item-based collaborative filtering. We motive this hypothesis with a discussion of informational channels in folksonomies and provide a theoretical explanation of the additive potential for item-based collaborative filtering. We then provided experimental results on hybrid tag recommenders built from graph models and other techniques based on popularity, user-based collaborative filtering and item-based collaborative filtering. We demonstrate that a hybrid recommender built from a graph based model and item-based collaborative filtering outperforms its constituent recommenders. furthermore the inability of the other recommenders to improve upon the graph-based approach suggests that they offer information already included in the graph based model. These results confirm our conjecture. We provide extensive evaluation of the hybrids using data collected from three real world collaborative tagging applications.}, author = {Gemmell, Jonathan and Schimoler, Thomas R. and Christiansen, Laura and Mobasher, Bamshad}, booktitle = {ACM RecSys'09 Workshop on Recommender Systems and the Social Web}, editor = {Jannach, Dietmar and Geyer, Werner and Freyne, Jill and Anand, Sarabjot Singh and Dugan, Casey and Mobasher, Bamshad and Kobsa, Alfred}, interhash = {0900f921d87c5ee19a4ed2c70e5a71df}, intrahash = {6b1ff3b7b691b84288fb7122968134c4}, issn = {1613-0073}, month = oct, pages = {17--24}, series = {CEUR-WS.org}, title = {Improving Folkrank With Item-Based Collaborative Filtering}, url = {http://ceur-ws.org/Vol-532/paper3.pdf}, volume = 532, year = 2009 } @article{hotho2010publikationsmanagement, abstract = {Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer größerer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenwärtigkeit, die ständige Verfügbarkeit, aber auch die Möglichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gründe für ihren gegenwärtigen Erfolg. Der Artikel führt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente wie Browsing und Suche am Beispiel von BibSonomy anhand typischer Arbeitsabläufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schließt mit Querbezügen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.}, author = {Hotho, Andreas and Benz, Dominik and Eisterlehner, Folke and Jäschke, Robert and Krause, Beate and Schmitz, Christoph and Stumme, Gerd}, editor = {Hengartner, Urs and Meier, Andreas}, interhash = {c19880489182c86e1573a2ac983c7cff}, intrahash = {422096948d4de38a725b428be3222d60}, issn = {1436-3011}, journal = {HMD - Praxis der Wirtschaftsinformatik}, month = feb, pages = {47--58}, publisher = {dpunkt.verlag}, title = {Publikationsmanagement mit BibSonomy - ein Social-Bookmarking-System für Wissenschaftler}, url = {http://hmd.dpunkt.de/271/05.php}, volume = 271, year = 2010 } @article{borrego2012measuring, abstract = {This paper explores the possibility of using data from social bookmarking services to measure the use of information by academic researchers. Social bookmarking data can be used to augment participative methods (e.g. interviews and surveys) and other, non-participative methods (e.g. citation analysis and transaction logs) to measure the use of scholarly information. We use BibSonomy, a free resource-sharing system, as a case study. Results show that published journal articles are by far the most popular type of source bookmarked, followed by conference proceedings and books. Commercial journal publisher platforms are the most popular type of information resource bookmarked, followed by websites, records in databases and digital repositories. Usage of open access information resources is low in comparison with toll access journals. In the case of open access repositories, there is a marked preference for the use of subject-based repositories over institutional repositories. The results are consistent with those observed in related studies based on surveys and citation analysis, confirming the possible use of bookmarking data in studies of information behaviour in academic settings. The main advantages of using social bookmarking data are that is an unobtrusive approach, it captures the reading habits of researchers who are not necessarily authors, and data are readily available. The main limitation is that a significant amount of human resources is required in cleaning and standardizing the data.}, author = {Borrego, Ángel and Fry, Jenny}, doi = {10.1177/0165551512438353}, eprint = {http://jis.sagepub.com/content/38/3/297.full.pdf+html}, interhash = {71ddfdd5b3d99b1a2986b4ded5e02b3c}, intrahash = {e5ccbb3378eeb88e7288d8ce59539812}, journal = {Journal of Information Science}, number = 3, pages = {297--308}, title = {Measuring researchers' use of scholarly information through social bookmarking data: A case study of BibSonomy}, url = {http://jis.sagepub.com/content/38/3/297.abstract}, volume = 38, year = 2012 } @inproceedings{navarrobullock2011tagging, abstract = {Learning-to-rank methods automatically generate ranking functions which can be used for ordering unknown resources according to their relevance for a specific search query. The training data to construct such a model consists of features describing a document-query-pair as well as relevance scores indicating how important the document is for the query. In general, these relevance scores are derived by asking experts to manually assess search results or by exploiting user search behaviour such as click data. The human evaluation of ranking results gives explicit relevance scores, but it is expensive to obtain. Clickdata can be logged from the user interaction with a search engine, but the feedback is noisy. In this paper, we want to explore a novel source of implicit feedback for web search: tagging data. Creating relevance feedback from tagging data leads to a further source of implicit relevance feedback which helps improve the reliability of automatically generated relevance scores and therefore the quality of learning-to-rank models.}, address = {New York, NY, USA}, author = {Navarro Bullock, Beate and Jäschke, Robert and Hotho, Andreas}, booktitle = {Proceedings of the ACM WebSci Conference}, interhash = {7afaa67dfeb07f7e0b85abf2be61aff1}, intrahash = {e5a4b67ed6173e9645aab321019efd74}, location = {Koblenz, Germany}, month = jun, organization = {ACM}, pages = {1--4}, title = {Tagging data as implicit feedback for learning-to-rank}, url = {http://journal.webscience.org/463/}, vgwort = {14,8}, year = 2011 } @book{balbymarinho2012recommender, abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.}, author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.}, doi = {10.1007/978-1-4614-1894-8}, interhash = {0bb7f0588cd690d67cc73e219a3a24fa}, intrahash = {87d6883ebd98e8810be45d7e7e4ade96}, isbn = {978-1-4614-1893-1}, month = feb, publisher = {Springer}, series = {SpringerBriefs in Electrical and Computer Engineering}, title = {Recommender Systems for Social Tagging Systems}, url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8}, year = 2012 } @incollection{jaeschke2012challenges, abstract = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.}, address = {Berlin/Heidelberg}, affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany}, author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, booktitle = {Recommender Systems for the Social Web}, doi = {10.1007/978-3-642-25694-3_3}, editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.}, interhash = {75b1a6f54ef54d0126d0616b5bf77563}, intrahash = {7d41d332cccc3e7ba8e7dadfb7996337}, isbn = {978-3-642-25694-3}, pages = {65--87}, publisher = {Springer}, series = {Intelligent Systems Reference Library}, title = {Challenges in Tag Recommendations for Collaborative Tagging Systems}, url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3}, volume = 32, year = 2012 } @inproceedings{kim2011personalized, abstract = {This paper looks inside FolkRank, one of the well-known folksonomy-based algorithms, to present its fundamental properties and promising possibilities for improving performance in tag recommendations. Moreover, we introduce a new way to compute a differential approach in FolkRank by representing it as a linear combination of the personalized PageRank vectors. By the linear combination, we present FolkRank's probabilistic interpretation that grasps how FolkRank works on a folksonomy graph in terms of the random surfer model. We also propose new FolkRank-like methods for tag recommendations to efficiently compute tags' rankings and thus reduce expensive computational cost of FolkRank. We show that the FolkRank approaches are feasible to recommend tags in real-time scenarios as well. The experimental evaluations show that the proposed methods provide fast tag recommendations with reasonable quality, as compared to FolkRank. Additionally, we discuss the diversity of the top n tags recommended by FolkRank and its variants.}, acmid = {2043945}, address = {New York, NY, USA}, author = {Kim, Heung-Nam and El Saddik, Abdulmotaleb}, booktitle = {Proceedings of the fifth ACM conference on Recommender systems}, doi = {10.1145/2043932.2043945}, interhash = {1004b267b14d0abde0f8ac3a7ceadd38}, intrahash = {f022e60c5928e01c701d7ec539ec221b}, isbn = {978-1-4503-0683-6}, location = {Chicago, Illinois, USA}, numpages = {8}, pages = {45--52}, publisher = {ACM}, title = {Personalized PageRank vectors for tag recommendations: inside FolkRank}, url = {http://doi.acm.org/10.1145/2043932.2043945}, year = 2011 } @inproceedings{hjss06bibsonomy, address = {Aalborg, Denmark}, author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {Proceedings of the Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures}, editor = {de Moor, Aldo and Polovina, Simon and Delugach, Harry}, interhash = {d28c9f535d0f24eadb9d342168836199}, intrahash = {2cbd8e3236adea7c54779605a5aa4fd6}, isbn = {87-7307-769-0}, month = jul, publisher = {Aalborg University Press}, title = {{BibSonomy}: A Social Bookmark and Publication Sharing System}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/hotho06bibsonomy.pdf}, vgwort = {27}, year = 2006 } @phdthesis{bogers2009recommender, abstract = {Recommender systems belong to a class of personalized information filtering technologies that aim to identify which items in a collection might be of interest to a particular user. Recommendations can be made using a variety of information sources related to both the user and the items: past user preferences, demographic information, item popularity, the metadata characteristics of the products, etc. Social bookmarking websites, with their emphasis on open collaborative information access, offer an ideal scenario for the application of recommender systems technology. They allow users to manage their favorite bookmarks online through a web interface and, in many cases, allow their users to tag the content they have added to the system with keywords. The underlying application then makes all information sharable among users. Examples of social bookmarking services include Delicious, Diigo, Furl, CiteULike, and BibSonomy. In my Ph.D. thesis I describe the work I have done on item recommendation for social bookmarking, i.e., recommending interesting bookmarks to users based on the content they bookmarked in the past. In my experiments I distinguish between two types of information sources. The first one is usage data contained in the folksonomy, which represents the past selections and transactions of all users, i.e., who added which items, and with what tags. The second information source is the metadata describing the bookmarks or articles on a social bookmarking website, such as title, description, authorship, tags, and temporal and publication-related metadata. I compare and combine the content-based aspect with the more common usage-based approaches. I evaluate my approaches on four data sets constructed from three different social bookmarking websites: BibSonomy, CiteULike, and Delicious. In addition, I investigate different combination methods for combining different algorithms and show which of those methods can successfully improve recommendation performance. Finally, I consider two growing pains that accompany the maturation of social bookmarking websites: spam and duplicate content. I examine how widespread each of these problems are for social bookmarking and how to develop effective automatic methods for detecting such unwanted content. Finally, I investigate the influence spam and duplicate content can have on item recommendation. }, address = {Tilburg, The Netherlands}, author = {Bogers, Toine}, interhash = {65b74dcabaa583a48469f3dec2ec1f62}, intrahash = {b02daac1201473600b7c8d2553865b4a}, month = dec, school = {Tilburg University}, title = {Recommender Systems for Social Bookmarking}, url = {http://ilk.uvt.nl/~toine/phd-thesis/}, year = 2009 } @article{mytkowicz2007understandnavig, abstract = {Given the rise in popularity of social tagging systems, it seems only natural to ask how efficient is the organically evolved vocabulary in describing any underlying document objects? Does this distributed process really provide a way to circumnavigate the traditional categorization problem with ontologies? We analyze a social tagging site, namely del.icio.us, with information theory in order to evaluate the efficiency of this social tagging site for navigation to information sources. We show that over time, del.icio.us is becoming harder and harder to navigate and provide an evaluation metric, namely entropy, that can be used to evaluate and drive system design choices. }, author = {Chi, Ed H. and Mytkowicz, Todd}, interhash = {304a9bcd66c9b221ed77fd478692b828}, intrahash = {5a09a3657d30b8f1119e42a8a5da1ff7}, journal = {In proceedings of the SIGCHI conference on Human Factors in Computing Systems (CHI'07)}, title = {Understanding Navigability of Social Tagging Systems}, url = {http://www.viktoria.se/altchi/index.php?action=showsubmission&id=39}, year = 2007 } @inproceedings{chi2008understanding, abstract = {Given the rise in popularity of social tagging systems, it seems only natural to ask how efficient is the organically evolved tagging vocabulary in describing underlying document objects? Does this distributed process really provide a way to circumnavigate the traditional "vocabulary problem" with ontology? We analyze a social tagging site, namely del.icio.us, with information theory in order to evaluate the efficiency of this social tagging site for encoding navigation paths to information sources. We show that information theory provides a natural and interesting way to understand this efficiency - or the descriptive, encoding power of tags. Our results indicate the efficiency of tags appears to be waning. We discuss the implications of our findings and provide insight into how our methods can be used to design more usable social tagging software.}, address = {New York, NY, USA}, author = {Chi, Ed H. and Mytkowicz, Todd}, booktitle = {HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia}, doi = {10.1145/1379092.1379110}, interhash = {81c80283290d396a41015d0df11822c7}, intrahash = {dfa880e6d3e33d0aeb357396fb1833cd}, isbn = {978-1-59593-985-2}, location = {Pittsburgh, PA, USA}, pages = {81--88}, publisher = {ACM}, title = {Understanding the efficiency of social tagging systems using information theory}, url = {http://portal.acm.org/citation.cfm?id=1379110&coll=GUIDE&dl=GUIDE&CFID=37458772&CFTOKEN=13998061&ret=1}, year = 2008 } @article{millen2005social, abstract = {One of the greatest challenges facing people who use large information spaces is to remember and retrieve items that they have previously found and thought to be interesting. One approach to this problem is to allow individuals to save particular search strings to re-create the search in the future. Another approach has been to allow people to create personal collections of material—for example, the use of electronic citation bundles (called binders) in the ACM Digital Library. Collections of citations can be created manually by readers or through execution of (and alerting to) a saved search. }, address = {New York, NY, USA}, author = {Millen, David and Feinberg, Jonathan and Kerr, Bernard}, doi = {http://doi.acm.org/10.1145/1105664.1105676}, interhash = {b40410a542f48202c52b6fa9408bca79}, intrahash = {dbc6366c82bbdb25c9865083b528f748}, issn = {1542-7730}, journal = {Queue}, number = 9, pages = {28--35}, publisher = {ACM}, title = {Social Bookmarking in the Enterprise}, url = {http://portal.acm.org/citation.cfm?id=1105676#}, volume = 3, year = 2005 } @incollection{hotho2008social, abstract = {BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung der Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und ähnlichen Systemen enthaltenen Daten ein.}, address = {Berlin, Heidelberg}, author = {Hotho, Andreas and Jäschke, Robert and Benz, Dominik and Grahl, Miranda and Krause, Beate and Schmitz, Christoph and Stumme, Gerd}, booktitle = {Social Semantic Web}, chapter = 18, doi = {10.1007/978-3-540-72216-8}, editor = {Blumauer, Andreas and Pellegrini, Tassilo}, interhash = {79dbca4289cfe913aa7f7eb7e0dccea7}, intrahash = {5ccf05a86e7f1a089ae83dd47568e6de}, isbn = {978-3-540-72215-1}, issn = {1439-3107}, pages = {363--391}, publisher = {Springer}, series = {X.media.press}, title = {Social Bookmarking am Beispiel BibSonomy}, url = {http://dx.doi.org/10.1007/978-3-540-72216-8_18}, vgwort = {49}, year = 2009 } @article{lhfh05social, author = {Lund, Ben and Hammond, Tony and Flack, Martin and Hannay, Timo}, interhash = {46c0a98ab6ccb96ff4722f35781807de}, intrahash = {13958ef5da2d2133b9b84e9a3cb40da1}, journal = {D-Lib Magazine}, month = {April}, number = 4, organization = {{N}ature {P}ublishing {G}roup}, title = {{S}ocial {B}ookmarking {T}ools ({II}): {A} {C}ase {S}tudy - {C}onnotea}, url = {http://www.dlib.org/dlib/april05/lund/04lund.html}, volume = 11, year = 2005 } @article{hammond2005social, abstract = {This paper reviews some current initiatives, as of early 2005, in providing public link management applications on the Web � utilities that are often referred to under the general moniker of 'social bookmarking tools'. There are a couple of things going on here: 1 server-side software aimed specifically at managing links with, crucially, a strong, social networking flavour, and 2 an unabashedly open and unstructured approach to tagging, or user classification, of those links. A number of such utilities are presented here, together with an emergent new class of tools that caters more to the academic communities and that stores not only user-supplied tags, but also structured citation metadata terms wherever it is possible to glean this information from service providers. This provision of rich, structured metadata means that the user is provided with an accurate third-party identification of a document, which could be used to retrieve that document, but is also free to search on user-supplied terms so that documents of interest or rather, references to documents can be made discoverable and aggregated with other similar descriptions either recorded by the user or by other users.}, author = {Hammond, Tony and Hannay, Timo and Lund, Ben and Scott, Joanna}, interhash = {c7457d9dc07545a061de119d96ca4e47}, intrahash = {89c6c43ad692ccfbe4c09d31926ab8a7}, issn = {1082-9873}, journal = {D-Lib Magazine}, month = apr, number = 4, organization = {Nature Publishing Group}, title = {Social Bookmarking Tools (I): A General Review}, url = {http://www.dlib.org/dlib/april05/hammond/04hammond.html}, volume = 11, year = 2005 }