BibSonomyburst/user/dbenz/ontology_learningBibSonomy RSS feed for /user/dbenz/ontology_learning2012-02-17T00:01:03+01:00http://www.bibsonomy.org/bibtex/2603161eb4c5b2f87f3d3a50f87015337/dbenzdbenz2011-09-26T08:22:22+02:002012 evaluation folksonomies myown ontology_learning <span class="authorEditorList"><a href="/author/Strohmaier">Markus Strohmaier</a>, <a href="/author/Helic">Denis Helic</a>, <a href="/author/Benz">Dominik Benz</a>, <a href="/author/Körner">Christian Körner</a>, and <a href="/author/Kern">Roman Kern</a> </span><em>Transactions on Intelligent Systems and Technology</em> (<em>2012</em>)Mon Sep 26 08:22:22 CEST 2011Transactions on Intelligent Systems and TechnologyEvaluation of Folksonomy Induction Algorithms20122012 evaluation folksonomies myown ontology_learning to appearhttp://www.bibsonomy.org/bibtex/23081beee709710cd12ca402a00526ef2/dbenzdbenz2011-08-30T08:39:27+02:00handbook ontology_learning <span class="authorEditorList"><a href="/author/Cimiano">Philipp Cimiano</a>, <a href="/author/Mädche">Alexander Mädche</a>, <a href="/author/Staab">Steffen Staab</a>, and <a href="/author/Völker">Johanna Völker</a> </span><em>Handbook on Ontologies, </em><em>Springer Berlin Heidelberg, </em>(<em>2009</em>)Tue Aug 30 08:39:27 CEST 2011Handbook on Ontologies245-267International Handbooks Information SystemOntology Learning2009handbook ontology_learning http://www.bibsonomy.org/bibtex/25f5f2584d7313b47172a3eab121d0069/dbenzdbenz2011-08-23T17:12:05+02:00definition knowledge_engineering ontology_learning <span class="authorEditorList"><a href="/author/Studer">Rudi Studer</a>, <a href="/author/Benjamins">Richard R. Benjamins</a>, and <a href="/author/Fensel">Dieter Fensel</a> </span><em>Data Knowledge Engineering</em> <em>25(1-2):161--197</em> (<em>1998</em>)Tue Aug 23 17:12:05 CEST 2011Data Knowledge Engineering1-2161--197Knowledge {E}ngineering: {P}rinciples and {M}ethods251998definition knowledge_engineering ontology_learning This paper gives an overview about the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in the last years we describe three modeling frameworks: CommonKADS, MIKE, and PROTÉGÉ-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods, and ontologies. We conclude with outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.sdasdahttp://www.bibsonomy.org/bibtex/209ab696de72e68b0b2aaf21ae3b0b613/dbenzdbenz2011-08-23T16:20:52+02:00book ontology_learning <span class="authorEditorList"><a href="/author/Cimiano">Philipp Cimiano</a> </span><em>Springer, </em>(<em>2006</em>)Tue Aug 23 16:20:52 CEST 2011I-XXVIII, 1-347Ontology learning and population from text - algorithms, evaluation and applications.2006book ontology_learning http://www.bibsonomy.org/bibtex/29f6febd6e835d24edb2547ad39bf36f4/dbenzdbenz2011-07-29T09:40:41+02:00ontology_learning text_mining social_evidences <span class="authorEditorList"><a href="/author/Weichselbraun">Albert Weichselbraun</a> </span>(<em>2011</em>)<em>Presentation Slides only . </em>Fri Jul 29 09:40:41 CEST 2011Presentation Slides onlyOntology Learning based on Text Mining and Social Evidence Sources2011ontology_learning text_mining social_evidences http://www.bibsonomy.org/bibtex/25e7a5d5d2ff00af9a914b3a547ca3c48/dbenzdbenz2011-07-29T09:40:39+02:00ontology_learning folksonomies <span class="authorEditorList"><a href="/author/Keller">Christine Keller</a> </span><em>Universität Stuttgart, </em>(<em>2010</em>)Fri Jul 29 09:40:39 CEST 2011Theoretical and Practical Perspectives on Ontology Learning from Folksonomies2010ontology_learning folksonomies http://www.bibsonomy.org/bibtex/27ef2f23103d4c0ed0ad344f9ead8db9d/dbenzdbenz2011-07-20T11:26:46+02:00ontology_learning ol_web2.0 thesis berlin <span class="authorEditorList"><a href="/author/Meder">Michael Meder</a> </span><em>Technische Universität Berlin, </em>(<em>2010</em>)Wed Jul 20 11:26:46 CEST 2011Multi-Domain Klassifikation basierend auf nutzergenerierten Metadaten2010ontology_learning ol_web2.0 thesis berlin http://www.bibsonomy.org/bibtex/2a16325d6196b3adb8e68851f4f4eff84/dbenzdbenz2011-06-06T08:39:59+02:00disambiguation ontology_learning tag unsupervised <span class="authorEditorList"><a href="/author/Widdows">Dominic Widdows</a>, and <a href="/author/Dorow">Beate Dorow</a> </span><em>COLING, </em>(<em>2002</em>)Mon Jun 06 08:39:59 CEST 2011COLINGA Graph Model for Unsupervised Lexical Acquisition2002disambiguation ontology_learning tag unsupervised DBLP Record 'conf/coling/WiddowsD02'http://www.bibsonomy.org/bibtex/295b0f4f7c9c628e032d8bb4c69b432ed/dbenzdbenz2011-02-17T17:43:27+01:00ol_web2.0 ontology ontology_learning semantic semanticweb toread toread_dbe overview <span class="authorEditorList"><a href="/author/Zhou">Lina Zhou</a> </span><em>Information Technology and Management</em> <em>8(3):241--252</em> (<em>2007</em>)Thu Feb 17 17:43:27 CET 2011Information Technology and Management3241--252Ontology learning: state of the art and open issues82007ol_web2.0 ontology ontology_learning semantic semanticweb toread toread_dbe overview Abstract\&nbsp;\&nbsp;Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the past decade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology development and a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learning approaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domain characteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insights about this fast-growing field.http://www.bibsonomy.org/bibtex/2b7eb173bc2c3dd1311a24ae9a96e5c2c/dbenzdbenz2011-02-17T17:43:27+01:00ol_web2.0 ontology_learning toread toread_dbe methods_from_text <span class="authorEditorList"><a href="/author/Zhou">GuoDong Zhou</a>, <a href="/author/Zhang">Min Zhang</a>, <a href="/author/Ji">DongHong Ji</a>, and <a href="/author/Zhu">QiaoMing Zhu</a> </span><em>Information Process Managegement</em> <em>44(3):1008--1021</em> (<em>2008</em>)Thu Feb 17 17:43:27 CET 2011Tarrytown, NY, USAInformation Process Managegement31008--1021Hierarchical learning strategy in semantic relation extraction442008ol_web2.0 ontology_learning toread toread_dbe methods_from_text This paper proposes a novel tree kernel-based method with rich syntactic and semantic information for the extraction of semantic relations between named entities. With a parse tree and an entity pair, we first construct a rich semantic relation tree structure to integrate both syntactic and semantic information. And then we propose a context-sensitive convolution tree kernel, which enumerates both context-free and context-sensitive sub-trees by considering the paths of their ancestor nodes as their contexts to capture structural information in the tree structure. An evaluation on the Automatic Content Extraction/Relation Detection and Characterization (ACE RDC) corpora shows that the proposed tree kernelbased method outperforms other state-of-the-art methods.http://www.bibsonomy.org/bibtex/2dd698b5ee4d93496d11627cbe1615514/dbenzdbenz2011-02-17T17:43:19+01:00disambiguation ol_web2.0 ontology_learning tag_concept_mapping taggingsurvey toread toread_dbe <span class="authorEditorList"><a href="/author/Tesconi">Maurizio Tesconi</a>, <a href="/author/Ronzano">Francesco Ronzano</a>, <a href="/author/Marchetti">Andrea Marchetti</a>, and <a href="/author/Minutoli">Salvatore Minutoli</a> </span><em>Proceedings of the Workshop Social Data on the Web SDoW2008, </em>(<em>2008</em>)Thu Feb 17 17:43:19 CET 2011Proceedings of the Workshop Social Data on the Web (SDoW2008)CEUR-WS.org/Vol-405Semantify del.icio.us: Automatically Turn your Tags into Senses2008disambiguation ol_web2.0 ontology_learning tag_concept_mapping taggingsurvey toread toread_dbe At present tagging is experimenting a great diffusion as the most adopted way to collaboratively classify resources over the Web. In this paper, after a detailed analysis of the attempts made to improve the organization and structure of tagging systems as well as the usefulness of this kind of social data, we propose and evaluate the Tag Disambiguation Algorithm, mining del.icio.us data. It allows to easily semantify the tags of the users of a tagging service: it automatically finds out for each tag the related concept of Wikipedia in order to describe Web resources through senses. On the basis of a set of evaluation tests, we analyze all the advantages of our sense-based way of tagging, proposing new methods to keep the set of users tags more consistent or to classify the tagged resources on the basis of Wikipedia categories, YAGO classes or Wordnet synsets. We discuss also how our semanitified social tagging data are strongly linked to DBPedia and the datasets of the Linked Data community. 1In this paper, after a detailed analysis of the attempts made to improve the organization and structure of tagging systems as well as the usefulness of this kind of social data, we propose and evaluate the Tag Disambiguation Algorithm, mining del.icio.us data.http://www.bibsonomy.org/bibtex/223b133bc2e6a4e00ab243efa98a02a12/dbenzdbenz2011-02-17T17:43:15+01:00master_thesis ol_web2.0 ontology_learning <span class="authorEditorList"><a href="/author/Stützer">Stefan Stützer</a> </span><em>University of Kassel, </em><em>Kassel, </em><em>Master Thesis, </em>(<em>2009</em>)Thu Feb 17 17:43:15 CET 2011KasselLernen von Ontologien aus kollaborativen Tagging-SystemenMaster Thesis2009master_thesis ol_web2.0 ontology_learning http://www.bibsonomy.org/bibtex/28f39e7ac43a97719c5a746da02dbd964/dbenzdbenz2011-02-17T17:43:08+01:00ol ol_web2.0 ontology_learning taxonomies toread toread_dbe methods_concepthierarchy <span class="authorEditorList"><a href="/author/Snow">Rion Snow</a>, <a href="/author/Jurafsky">Daniel Jurafsky</a>, and <a href="/author/Ng">Andrew Y. Ng</a> </span><em>ACL, </em><em>The Association for Computer Linguistics, </em>(<em>2006</em>)Thu Feb 17 17:43:08 CET 2011ACLconf/acl/2006Semantic Taxonomy Induction from Heterogenous Evidence.2006ol ol_web2.0 ontology_learning taxonomies toread toread_dbe methods_concepthierarchy We propose a novel algorithm for inducing semantic taxonomies. Previous algorithms for taxonomy induction have typically focused on independent classifiers for discovering new single relationships based on hand-constructed or automatically discovered textual patterns. By contrast, our algorithm flexibly incorporates evidence from multiple classifiers over heterogenous relationships to optimize the entire structure of the taxonomy, using knowledge of a word’s coordinate terms to help in determining its hypernyms, and vice versa. We apply our algorithm on the problem of sense-disambiguated noun hyponym acquisition, where we combine the predictions of hypernym and coordinate term classifiers with the knowledge in a preexisting semantic taxonomy (WordNet 2.1). We add 10; 000 novel synsets to WordNet 2.1 at 84% precision, a relative error reduction of 70% over a non-joint algorithm using the same component classifiers. Finally, we show that a taxonomy built using our algorithm shows a 23% relative F-score improvement over WordNet 2.1 on an independent testset of hypernym pairs.dblphttp://www.bibsonomy.org/bibtex/2b5f12aecb395b0e5bf4b03b816a46c03/dbenzdbenz2011-02-17T17:43:07+01:00games mwa ol_web2.0 ontology_learning semantic_web widely_related <span class="authorEditorList"><a href="/author/Siorpaes">Katharina Siorpaes</a>, and <a href="/author/Hepp">Martin Hepp</a> </span><em>IEEE Intelligent Systems</em> <em>23(3):50-60</em> (<em>2008</em>)Thu Feb 17 17:43:07 CET 2011Los Alamitos, CA, USAIEEE Intelligent Systems350-60Games with a Purpose for the Semantic Web232008games mwa ol_web2.0 ontology_learning semantic_web widely_related Weaving the Semantic Web requires that humans contribute their labor and judgment for creating, extending, and updating formal knowledge structures. Hiding such tasks behind online multiplayer games presents the tasks as fun and intellectually challenging entertainment.Games with a Purpose for the Semantic Webhttp://www.bibsonomy.org/bibtex/266bec053541e521fbe68c0119806ae49/dbenzdbenz2011-02-17T17:43:07+01:00learning ol_web2.0 ontology ontology_learning semi_automatic wikipedia data_wikis <span class="authorEditorList"><a href="/author/Silva">L. De Silva</a>, and <a href="/author/Jayaratne">L. Jayaratne</a> </span><em>Applications of Digital Information and Web Technologies, 2009. ICADIWT &#039;09. Second International Conference on the, </em><em>page 446-451. </em>(<em>August 2009</em>)Thu Feb 17 17:43:07 CET 2011Applications of Digital Information and Web Technologies, 2009. ICADIWT '09. Second International Conference on theAug.446-451Semi-automatic extraction and modeling of ontologies using Wikipedia XML Corpus2009learning ol_web2.0 ontology ontology_learning semi_automatic wikipedia data_wikis This paper introduces WikiOnto: a system that assists in the extraction and modeling of topic ontologies in a semi-automatic manner using a preprocessed document corpus derived from Wikipedia. Based on the Wikipedia XML Corpus, we present a three-tiered framework for extracting topic ontologies in quick time and a modeling environment to refine these ontologies. Using natural language processing (NLP) and other machine learning (ML) techniques along with a very rich document corpus, this system proposes a solution to a task that is generally considered extremely cumbersome. The initial results of the prototype suggest strong potential of the system to become highly successful in ontology extraction and modeling and also inspire further research on extracting ontologies from other semi-structured document corpora as well.Welcome to IEEE Xplore 2.0: Semi-automatic extraction and modeling of ontologies using Wikipedia XML Corpushttp://www.bibsonomy.org/bibtex/20a42603ea1375e9ed6efe1bbda6302da/dbenzdbenz2011-02-17T17:42:55+01:00folksonomies methods_concepthierarchy ol_web2.0 ontology_learning taggingsurvey <span class="authorEditorList"><a href="/author/Plangprasopchok">Anon Plangprasopchok</a>, and <a href="/author/Lerman">Kristina Lerman</a> </span><em>WWW, </em><em>page 781-790. </em><em>ACM, </em>(<em>2009</em>)Thu Feb 17 17:42:55 CET 2011WWWconf/www/2009781-790Constructing folksonomies from user-specified relations on flickr.2009folksonomies methods_concepthierarchy ol_web2.0 ontology_learning taggingsurvey Automatic folksonomy construction from tags has attracted much attention recently. However, inferring hierarchical relations between concepts from tags has a drawback in that it is difficult to distinguish between more popular and more general concepts. Instead of tags we propose to use userspecified relations for learning folksonomy. We explore two statistical frameworks for aggregating many shallow individual hierarchies, expressed through the collection/set relations on the social photosharing site Flickr, into a common deeper folksonomy that reflects how a community organizes knowledge. Our approach addresses a number of challenges that arise while aggregating information from diverse users, namely noisy vocabulary, and variations in the granularity level of the concepts expressed. Our second contribution is a method for automatically evaluating learned folksonomy by comparing it to a reference taxonomy, e.g., the Web directory created by the Open Directory Project. Our empirical results suggest that user-specified relations are a good source of evidence for learning folksonomies.dblphttp://www.bibsonomy.org/bibtex/23edf80da8b39eefeea46379581628adf/dbenzdbenz2011-02-17T17:42:53+01:00ol_web2.0 ontology_learning overview web <span class="authorEditorList"><a href="/author/Omelayenko">Borys Omelayenko</a> </span><em>Proceedings of the International Workshop on Web Dynamics, held in conj. with the 8th International Conference on Database Theory ICDT’01, London, UK, </em>(<em>2001</em>)Thu Feb 17 17:42:53 CET 2011Proceedings of the International Workshop on Web Dynamics, held in conj. with the 8th International Conference on Database Theory (ICDT’01), London, UKLearning of Ontologies for the Web: the Analysis of Existent Approaches2001ol_web2.0 ontology_learning overview web The next generation of the Web, called Semantic Web, has to improve the Web with semantic (ontological) page annotations to enable knowledge-level querying and searches. Manual construction of these ontologies will require tremendous efforts that force future integration of machine learning with knowledge acquisition to enable highly automated ontology learning. In the paper we present the state of the-art in the field of ontology learning from the Web to see how it can contribute to the task of semantic Web querying. We consider three components of the query processing system: natural language ontologies, domain ontologies and ontology instances. We discuss the requirements for machine learning algorithms to be applied for the learning of the ontologies of each type from the Web documents, and survey the existent ontology learning and other closely related approaches.http://www.bibsonomy.org/bibtex/2c3cca9801ab1e6d2598be1041c19618c/dbenzdbenz2011-02-17T17:42:52+01:00ol_web2.0 ontology_learning wikipedia data_wikis <span class="authorEditorList"><a href="/author/Nazir">F. Nazir</a>, and <a href="/author/Takeda">H. Takeda</a> </span><em>IEEE International Symposium on Technology and Society, </em><em>page 1--13. </em><em>IEEE, </em>(<em>June 2008</em>)Thu Feb 17 17:42:52 CET 2011IEEE International Symposium on Technology and Societyjun1--13Extraction and analysis of tripartite relationships from Wikipedia2008ol_web2.0 ontology_learning wikipedia data_wikis Social aspects are critical in the decision making process for social actors (human beings). Social aspects can be categorized into social interaction, social communities, social groups or any kind of behavior that emerges from interlinking, overlapping or similarities between interests of a society. These social aspects are dynamic and emergent. Therefore, interlinking them in a social structure, based on bipartite affiliation network, may result in isolated graphs. The major reason is that as these correspondences are dynamic and emergent, they should be coupled with more than a single affiliation in order to sustain the interconnections during interest evolutions. In this paper we propose to interlink actors using multiple tripartite graphs rather than a bipartite graph which was the focus of most of the previous social network building techniques. The utmost benefit of using tripartite graphs is that we can have multiple and hierarchical links between social actors. Therefore in this paper we discuss the extraction, plotting and analysis methods of tripartite relations between authors, articles and categories from Wikipedia. Furthermore, we also discuss the advantages of tripartite relationships over bipartite relationships. As a conclusion of this study we argue based on our results that to build useful, robust and dynamic social networks, actors should be interlinked in one or more tripartite networks.http://www.bibsonomy.org/bibtex/2cfa4c4520d4cf02e03dd3b84bb5c9578/dbenzdbenz2011-02-17T17:42:46+01:00collabulary enrichment folksonomy learning ol_web2.0 ontology_learning taggingsurvey <span class="authorEditorList"><a href="/author/Marinho">Leandro Balby Marinho</a>, <a href="/author/Buza">Krisztian Buza</a>, and <a href="/author/Schmidt-Thieme">Lars Schmidt-Thieme</a> </span><em>International Semantic Web Conference, </em><em>volume 5318 of Lecture Notes in Computer Science, </em><em>page 261-276. </em><em>Springer, </em>(<em>2008</em>)Thu Feb 17 17:42:46 CET 2011International Semantic Web Conferenceconf/semweb/2008261-276Lecture Notes in Computer ScienceFolksonomy-Based Collabulary Learning.53182008collabulary enrichment folksonomy learning ol_web2.0 ontology_learning taggingsurvey The growing popularity of social tagging systems promises to alleviate the knowledge bottleneck that slows down the full materialization of the SemanticWeb since these systems allow ordinary users to create and share knowledge in a simple, cheap, and scalable representation, usually known as folksonomy. However, for the sake of knowledge workflow, one needs to find a compromise between the uncontrolled nature of folksonomies and the controlled and more systematic vocabulary of domain experts. In this paper we propose to address this concern by devising a method that automatically enriches a folksonomy with domain expert knowledge and by introducing a novel algorithm based on frequent itemset mining techniques to efficiently learn an ontology over the enriched folksonomy. In order to quantitatively assess our method, we propose a new benchmark for task-based ontology evaluation where the quality of the ontologies is measured based on how helpful they are for the task of personalized information finding. We conduct experiments on real data and empirically show the effectiveness of our approach.dblphttp://www.bibsonomy.org/bibtex/27069480c43ba5d41396e075307cd1af1/dbenzdbenz2011-02-17T17:42:35+01:00flickr ol_web2.0 ontology_learning emergentsemantics_evidence <span class="authorEditorList"><a href="/author/Kennedy">Lyndon Kennedy</a>, <a href="/author/Naaman">Mor Naaman</a>, <a href="/author/Ahern">Shane Ahern</a>, <a href="/author/Nair">Rahul Nair</a>, and <a href="/author/Rattenbury">Tye Rattenbury</a> </span><em>MULTIMEDIA &#039;07: Proceedings of the 15th international conference on Multimedia, </em><em>page 631--640. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Thu Feb 17 17:42:35 CET 2011New York, NY, USAMULTIMEDIA '07: Proceedings of the 15th international conference on Multimedia631--640How flickr helps us make sense of the world: context and content in community-contributed media collections2007flickr ol_web2.0 ontology_learning emergentsemantics_evidence The advent of media-sharing sites like Flickr and YouTube has drastically increased the volume of community-contributed multimedia resources available on the web. These collections have a previously unimagined depth and breadth, and have generated new opportunities – and new challenges – to multimedia research. How do we analyze, understand and extract patterns from these new collections? How can we use these unstructured, unrestricted community contributions of media (and annotation) to generate “knowledge�?? As a test case, we study Flickr – a popular photo sharing website. Flickr supports photo, time and location metadata, as well as a light-weight annotation model. We extract information from this dataset using two different approaches. First, we employ a location-driven approach to generate aggregate knowledge in the form of “representative tags�? for arbitrary areas in the world. Second, we use a tag-driven approach to automatically extract place and event semantics for Flickr tags, based on each tag’s metadata patterns. With the patterns we extract from tags and metadata, vision algorithms can be employed with greater precision. In particular, we demonstrate a location-tag-vision-based approach to retrieving images of geography-related landmarks and features from the Flickr dataset. The results suggest that community-contributed media and annotation can enhance and improve our access to multimedia resources – and our understanding of the world.