PUMA publications for /user/stumme/folksonomy%20myownMon Dec 16 17:19:49 CET 2013Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedingsaccepted for publicationNew York, NY, USATag Recommendations for SensorFolkSonomies20132013 RecSys everyaware folksonomy iteg itegpub l3s myown recommendation rsweb sensor sitc tag widenoise With the rising popularity of smart mobile devices, sensor data-based
applications have become more and more popular. Their users record
data during their daily routine or specifically for certain events.
The application WideNoise Plus allows users to record sound samples
and to annotate them with perceptions and tags. The app is being
used to document and map the soundscape all over the world. The procedure
of recording, including the assignment of tags, has to be as easy-to-use
as possible. We therefore discuss the application of tag recommender
algorithms in this particular scenario. We show, that this task is
fundamentally different from the well-known tag recommendation problem
in folksonomies as users do no longer tag fix resources but rather
sensory data and impressions. The scenario requires efficient recommender
algorithms that are able to run on the mobile device, since Internet
connectivity cannot be assumed to be available. Therefore, we evaluate
the performance of several tag recommendation algorithms and discuss
their applicability in the mobile sensing use-case.Mon Mar 18 14:06:44 CET 2013febSpringerBriefs in Electrical and Computer EngineeringRecommender Systems for Social Tagging Systems20122012 bookmarking collaborative folksonomy info20 itegpub l3s myown recommender social tagging tagging,2012 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.Mon Feb 06 14:59:32 CET 2012Berlin/HeidelbergRecommender Systems for the Social Web65--87Intelligent Systems Reference LibraryChallenges in Tag Recommendations for Collaborative Tagging Systems3220122012 bookmarking challenge collaborative dc09 discovery folksonomy info20 itegpub l3s myown recommender rsdc08 social tagging 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.Fri Nov 25 12:41:06 CET 2011Berlin/HeidelbergKnowledge Processing and Data Analysis136--149Lecture Notes in Computer ScienceA Comparison of Content-Based Tag Recommendations in Folksonomy Systems658120112011 content folksonomy info20 itegpub l3s myown recommendations recommender tag tagorapub Recommendation algorithms and multi-class classifiers can support
users of social bookmarking systems in assigning tags to their
bookmarks. Content based recommenders are the usual approach for
facing the cold start problem, i.e., when a bookmark is uploaded for
the first time and no information from other users can be exploited.
In this paper, we evaluate several recommendation algorithms in a
cold-start scenario on a large real-world dataset.
Wed Dec 15 11:43:59 CET 2010Datenbank-Spektrumjun115--24Query Logs as Folksonomies1020102010 folksonomies folksonomy info20 itegpub l3s log logs logsonomy myown Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well. Wed May 19 11:55:51 CEST 2010New York, NY, USAHT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia157--166Logsonomy - Social Information Retrieval with Logdata20082.0 2008 analysis folksonomy information itegpub logsonomy myown network retrieval search social tagorapub web web2.0 web20 Social bookmarking systems constitute an established
part of the Web 2.0. In such systems
users describe bookmarks by keywords
called tags. The structure behind these social
systems, called folksonomies, can be viewed
as a tripartite hypergraph of user, tag and resource
nodes. This underlying network shows
specific structural properties that explain its
growth and the possibility of serendipitous
exploration.
Today’s search engines represent the gateway
to retrieve information from the World Wide
Web. Short queries typically consisting of
two to three words describe a user’s information
need. In response to the displayed
results of the search engine, users click on
the links of the result page as they expect
the answer to be of relevance.
This clickdata can be represented as a folksonomy
in which queries are descriptions of
clicked URLs. The resulting network structure,
which we will term logsonomy is very
similar to the one of folksonomies. In order
to find out about its properties, we analyze
the topological characteristics of the tripartite
hypergraph of queries, users and bookmarks
on a large snapshot of del.icio.us and
on query logs of two large search engines.
All of the three datasets show small world
properties. The tagging behavior of users,
which is explained by preferential attachment
of the tags in social bookmark systems, is
reflected in the distribution of single query
words in search engines. We can conclude
that the clicking behaviour of search engine
users based on the displayed search results
and the tagging behaviour of social bookmarking
users is driven by similar dynamics.Wed Apr 07 13:54:41 CEST 2010HeidelbergAdvances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008101-113LNAIA Comparison of Social Bookmarking with Traditional Search495620082008 bookmarking comparison folksonomies folksonomy itegpub logsonomies myown search social tagorapub Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s
data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users.
In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings.
Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems
to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space
retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.Wed Apr 07 13:54:41 CEST 2010AalborgProceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures87-102{BibSonomy}: A Social Bookmark and Publication Sharing System20062006 FCA OntologyHandbook bibsonomy bookmarking folksonomy iccs l3s myown nepomuk social tagorapub Social bookmark tools are rapidly emerging on the Web. In such
systems users are setting up lightweight conceptual structures
called folksonomies. The reason for their immediate success is the
fact that no specific skills are needed for participating. In this
paper we specify a formal model for folksonomies and briefly describe
our own system BibSonomy, which allows for sharing both bookmarks
and publication references in a kind of personal library.Wed Apr 07 13:54:41 CEST 2010HeidelbergData Science and Classification. Proceedings of the 10th IFCS Conf.July261--270Studies in Classification, Data Analysis, and Knowledge OrganizationMining Association Rules in Folksonomies20062006 analysis fca folksonomies folksonomy l3s myown nepomuk network semantic Social bookmark tools are rapidly emerging on the Web. In such
systems users are setting up lightweight conceptual structures
called folksonomies. These systems provide currently relatively few
structure. We discuss in this paper, how association rule mining
can be adopted to analyze and structure folksonomies, and how the results can be used
for ontology learning and supporting emergent semantics. We
demonstrate our approach on a large scale dataset stemming from an
online system.Wed Apr 07 13:54:41 CEST 2010BanffProc. WWW2007 Workshop ``Tagging and Metadata for Social Information Organization''mayNetwork Properties of Folksonomies20072007 emergent fca folksonomy folksononomies itegpub l3s myown semantics smallworld sna socialnetwork 8Wed Apr 07 13:54:41 CEST 2010Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)sep13-20Tag Recommendations in Folksonomies20072007 bookmarking collaborative filtering folksonomy itegpub l3s myown recommender social Wed Apr 07 13:54:41 CEST 2010Graz, Austria7th International Conference on Knowledge Management (I-KNOW '07)sep356-364Conceptual Clustering of Social Bookmarking Sites20072007 folksonomies folksonomy itegpub myown sites social tagging tagorapub Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the
system, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.Wed Apr 07 13:54:41 CEST 2010Patras, GreeceProceedings of the 3rd Workshop on Ontology Learning and Population (OLP3)JulySemantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems20082.0 2008 collaborative folksonomies folksonomy itegpub myown semantic systems tagging web web2.0 Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.Wed Apr 07 13:54:41 CEST 2010Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the WebThe Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems20082.0 2008 bookmarking folksonomies folksonomy itegpub myown social spam systems tagger tagorapub web web2.0 Wed Apr 07 13:54:41 CEST 2010Berlin, HeidelbergData Science and Classification: Proc. of the 10th IFCS Conf.261--270Studies in Classification, Data Analysis, and Knowledge OrganizationMining Association Rules in Folksonomies20062006 FCA OntologyHandbook association folksonomy itegpub myown rule Wed Apr 07 13:54:41 CEST 2010Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)Logsonomy -- A Search Engine Folksonomy20082008 engine folksonomies folksonomy itegpub logsonomies logsonomy myown search tagorapub In social bookmarking systems users describe bookmarks
by keywords called tags. The structure behind
these social systems, called folksonomies, can be
viewed as a tripartite hypergraph of user, tag and resource
nodes. This underlying network shows specific
structural properties that explain its growth and the possibility
of serendipitous exploration.
Search engines filter the vast information of the web.
Queries describe a user’s information need. In response
to the displayed results of the search engine, users click
on the links of the result page as they expect the answer
to be of relevance. The clickdata can be represented as a
folksonomy in which queries are descriptions of clicked
URLs. This poster analyzes the topological characteristics
of the resulting tripartite hypergraph of queries,
users and bookmarks of two query logs and compares it
two a snapshot of the folksonomy del.icio.us.Wed Apr 07 13:54:41 CEST 2010AI Communications Journal, Special Issue on ``Network Analysis in Natural Sciences and Engineering''4245-262Network Properties of Folksonomies2020072007 emergent fca folksonomies folksonomy itegpub l3s myown network semantics seminar2009 tagorapub Wed Apr 07 13:54:41 CEST 2010Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007)sep50-54Conceptual Clustering of Social Bookmark Sites20072007 Social bookmark bookmarking clustering collaborative conceptual folksonomies folksonomy itegpub myown social tagging tagorapub Wed Apr 07 13:54:41 CEST 2010BonnInformatik 2006 -- Informatik für Menschen. Band 2octProc. Workshop on Applications of Semantic Technologies, Informatik 2006Lecture Notes in InformaticsEmergent Semantics in BibSonomyP-9420062006 UniK bibsonomy emergence emergent folksonomy hotho itegpub jaeschke l3s myown nepomuk schmitz semantics stumme tagorapub Social bookmark tools are rapidly emerging on the Web. In such
systems users are setting up lightweight conceptual structures
called folksonomies. The reason for their immediate success is the
fact that no specific skills are needed for participating. In this
paper we specify a formal model for folksonomies, briefly describe
our own system BibSonomy,
which allows for sharing both bookmarks and
publication references,
and discuss first steps towards emergent semantics.Thu Jul 02 00:08:08 CEST 2009HeidelbergThe Semantic Web: Research and ApplicationsJune411-426LNAIInformation Retrieval in Folksonomies: Search and Ranking401120062006 FCA IR OntologyHandbook folkrank folksonomy information informationretrieval itegpub mimose myown pagerank ranking retrieval