BibSonomy publications for /user/dbenzMon Jul 28 15:48:11 CEST 2008International Journal of Knowledge and Learning (IJKL)ISSN (Online): 1741-1017 ISSN (Print): 1741-10094/5515-528From Folksonomies to Ontologies: Employing Wisdom of the Crowds to Serve Learning Purposes32007toread folksonomy ontology Is Web 2.0 just hype or just a buzzword, which might disappear in the near future? One way to find answers to these questions is to investigate the actual benefit of the Web 2.0 for real use cases. Within this contribution we study a very special aspect of the Web 2.0 – the folksonomy – and its use within self-directed learning. Guided by conceptual principles of emergent computing we point out methods, which might be able to let semantics emerge from folksonomies and discuss the effect of the results in self-directed learning.Wed Jul 23 11:47:29 CEST 2008Information, Wissenschaft und PraxisOCTsee the special issue for more contributions related to the Semantic Web6-7315-320Ontologies on Demand? - A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text572006overview ontology learning Thu Jul 17 10:36:27 CEST 2008International Semantic Web ConferenceDBLP:conf/semweb/2006487-500Extracting Relations in Social Networks from the Web Using Similarity Between Collective Contexts2006resources extract toread relations folksonomy social-networks Mon Jul 07 15:50:15 CEST 2008Cavtat-Dubrovnik, CroatiaProceedings of the ECML / PKDD Workshop on Adaptive Text Extraction and Mining10--17Automatic Acquisition of Taxonomies from Text: FCA meets NLP2003taxonomic_overlap ontology_learning Mon Jun 09 13:45:04 CEST 2008Patras, GreeceProceedings of the 3rd Workshop on Ontology Learning and Population (OLP3)JulySemantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems20082007 tag_relatedness tagorapub olp3 myown 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.Mon Apr 14 09:52:08 CEST 2008Proceedings of the European Conference of Complex Systems (ECCS'07)Octoberas posterEngineering Comparators for Graph Clusterings2007toread clustering evaluation Mon Apr 14 09:50:46 CEST 2008Proceedings of the European Conference of Complex Systems (ECCS'07)Octoberas posterEngineering the Evaluation of Clustering Techniques2007toread evaluation clustering Mon Apr 14 09:45:34 CEST 2008Scientometrics#apr#137--50Bottom-up scientific field detection for dynamical and hierarchical science mapping, methodology and case study752008toread scientific_disciplines taxonomy_learning Abstract  We propose new methods to detect paradigmatic fields through simple statistics over a scientific content database. We propose an asymmetric paradigmatic proximity metric between terms which provide insight into hierarchical structure of scientific activity and test our methods on a case studywith a database made of several millions of resources. We also propose overlapping categorization to describe paradigmaticfields as sets of terms that may have several different usages. Terms can also be dynamically clustered providing a high-leveldescription of the evolution of the paradigmatic fields.Mon Apr 14 09:40:21 CEST 2008Proceedings of the European Conference of Complex Systems (ECCS'07)Centrality properties of directed module members in social networks2007community_detection toread network centrality Mon Apr 14 09:35:08 CEST 2008Vocabulary growth in collaborative tagging systems2007vocabulary folksonomy analysis toread We analyze a large-scale snapshot of del.icio.us and investigate how the number of different tags in the system grows as a function of a suitably defined notion of time. We study the temporal evolution of the global vocabulary size, i.e. the number of distinct tags in the entire system, as well as the evolution of local vocabularies, that is the growth of the number of distinct tags used in the context of a given resource or user. In both cases, we find power-law behaviors with exponents smaller than one. Surprisingly, the observed growth behaviors are remarkably regular throughout the entire history of the system and across very different resources being bookmarked. Similar sub-linear laws of growth have been observed in written text, and this qualitative universality calls for an explanation and points in the direction of non-trivial cognitive processes in the complex interaction patterns characterizing collaborative tagging.Mon Apr 14 09:33:09 CEST 2008Dresden, GermanyProceedings of the European Confeence on Complex SystemsOctoberEmergent Community Structure in Social Tagging Systems2007clustering community_detection folksonomies Mon Apr 14 09:27:59 CEST 2008Taxonomy and clustering in collaborative systems: the case of the on-line encyclopedia Wikipedia2007toread comparison wikipedia clustering taxonomy In this paper we investigate the nature and structure of the relation between imposed classifications and real clustering in a particular case of a scale-free network given by the on-line encyclopedia Wikipedia. We find a statistical similarity in the distributions of community sizes both by using the top-down approach of the categories division present in the archive and in the bottom-up procedure of community detection given by an algorithm based on the spectral properties of the graph. Regardless the statistically similar behaviour the two methods provide a rather different division of the articles, thereby signaling that the nature and presence of power laws is a general feature for these systems and cannot be used as a benchmark to evaluate the suitability of a clustering method.Thu Apr 03 14:49:06 CEST 20081neue Auflage kommt im Frühjahr 2008Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition (Prentice Hall Series in Artificial Intelligence)2000nlp science natural processing lecture dspp computer language linguistics computational Fri Mar 07 17:57:51 CET 2008D-LibJanuary1Folksonomies: Tidying Up Tags?122006folksonomy_background improve_tags flickr tagging folksonomy diploma_thesis delicious A folksonomy is a type of distributed classification system. It is usually created by a group of individuals, typically the resource users. Users add tags to online items, such as images, videos, bookmarks and text. These tags are then shared and sometimes refined. In this article we look at what makes folksonomies work. We agree with the premise that tags are no replacement for formal systems, but we see this as being the core quality that makes folksonomy tagging so useful.Thu Feb 14 11:18:56 CET 2008Edinburgh, ScotlandProceedings of the 2nd Workshop in Innovations in Web Infrastructure (IWI2) at WWW2006Mayisbn = {085432853X}Automatic Bookmark Classification - A Collaborative Approach2006www2006 2006 iwi myown studienarbeit Bookmarks (or Favorites, Hotlists) are a popular strategy to relocate interesting websites on the WWW by creating a personalized local URL repository. Most current browsers offer a facility to store and manage bookmarks in a hierarchy of folders; though, with growing size, users reportedly have trouble to create and maintain a stable taxonomy. This paper presents a novel collaborative approach to ease bookmark management, especially the “classification” of new bookmarks into a folder. We propose a methodology to realize the collaborative classification idea of considering how similar users have classified a bookmark. A combination of nearest-neighbour-classifiers is used to derive a recommendation from similar users on where to store a new bookmark. Additionally, a procedure to generate keyword recommendations is proposed to ease the annotation of new bookmarks. A prototype system called CariBo has been implemented as a plugin of the central bookmark server software SiteBar. A case study conducted with real user data supports the validity of the approach.Thu Feb 07 17:19:04 CET 2008IEEE Transactions on Knowledge and Data Engineering370The Google Similarity Distance192007relatedness_measures google imported web_based Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers the equivalent of `society' is `database,' and the equivalent of `use' is `way to search the database.' We present a new theory of similarity between words and phrases based on information distance and Kolmogorov complexity. To fix thoughts we use the world-wide-web as database, and Google as search engine. The method is also applicable to other search engines and databases. This theory is then applied to construct a method to automatically extract similarity, the Google similarity distance, of words and phrases from the world-wide-web using Google page counts. The world-wide-web is the largest database on earth, and the context information entered by millions of independent users averages out to provide automatic semantics of useful quality. We give applications in hierarchical clustering, classification, and language translation. We give examples to distinguish between colors and numbers, cluster names of paintings by 17th century Dutch masters and names of books by English novelists, the ability to understand emergencies, and primes, and we demonstrate the ability to do a simple automatic English-Spanish translation. Finally, we use the WordNet database as an objective baseline against which to judge the performance of our method. We conduct a massive randomized trial in binary classification using support vector machines to learn categories based on our Google distance, resulting in an a mean agreement of 87% with the expert crafted WordNet categories.Fri Jan 04 17:47:15 CET 2008Begriffssysteme2003toread Begriffssysteme tauchen als Thesauri, Klassifikationen, Nachschlagewerke, Begriffsnetze, Ontologien etc. in verschiedenen Fachwissenschaften zur Ordnung und Repräsentation von Wissen auf. Diese Arbeit gibt einen Überblick über die verschiedenen Arten von Begriffssystemen und ihre Bestandteile. Dies sind im wesentlichen Begriffe, Bezeichnungen und Relationen. Die häufigsten Bestandteile und ihre Anwendungsfälle werden erklärt, darunter die Terminologische Kontrolle, Begriffskombination, Notationen, die wichtigsten Relationsarten und Regeln sowie natürlich sprachliche Anteile. Es werden verschiedene Datenformate für Begriffssysteme vorgestellt. Die gemeinsamen Strukturen und Bestandteile verschiedener Begriffssysteme werden in dem gemeinsamen Datenmodell „Thema“ zusammengefasst, das anhand einer XML-Repräsentation des Modells erläutert wird.Mon Dec 10 09:30:22 CET 2007Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)sep109--112Position Paper: Ontology Learning from Folksonomies20072007 ontology_learning tagorapub diploma_thesis myown Mon Dec 10 09:27:11 CET 2007Special Issue of the Computer Networks journal on Innovations in Web Communications Infrastructure164574--4585Suppporting Collaborative Hierarchical Classification: Bookmarks as an Example5120072007 myown studienarbeit Bookmarks (or favorites, hotlists) are popular strategies to relocate interesting websites on the WWW by creating a personalized URL repository. Most current browsers offer a facility to locally store and manage bookmarks in a hierarchy of folders; though, with growing size, users reportedly have trouble to create and maintain a stable organization structure. This paper presents a novel collaborative approach to ease bookmark management, especially the “classification” of new bookmarks into a folder. We propose a methodology to realize the collaborative classification idea of considering how similar users have classified a bookmark. A combination of nearest-neighbor-classifiers is used to derive a recommendation from similar users on where to store a new bookmark. A prototype system called CariBo has been implemented as a plugin for the central bookmark server software SiteBar. All findings have been evaluated on a reasonably large scale, real user dataset with promising results, and possible implications for shared and social bookmarking systems are discussed.Thu Oct 25 07:59:24 CEST 2007Innovations in Information Technology, 2006Nov. 1--5Measuring the Semantic Value of Folksonomies2006jabref:noKeywordAssigned