BibSonomy publications for /tag/ontology_learningSat Dec 17 17:39:37 CET 2011Bridging the Gep between Semantic Web and Web 2.0 (SemNet 2007)57-70FolksOntology: An Integrated Approach for Turning Folksonomies into Ontologies2007folksonomy ontology ontology_learning semantics We can observe that the amount of non-toy domain ontologies is stillvery limited for many areas of interest. In contrast, folksonomies are widely inuse for (1) tagging Web pages (e.g. del.icio.us), (2) annotating pictures (e.g.flickr), or (3) classifying scholarly publications (e.g. bibsonomy). However,such folksonomies cannot offer the expressivity of ontologies, and therespective tags often lack a context-independent and intersubjective definitionof meaning. Also, folksonomies and other unsupervised vocabularies frequentlysuffer from inconsistencies and redundancies. In this paper, we argue that thesocial interaction manifested in folksonomies and in their usage should beexploited for building and maintaining ontologies. Then, we sketch acomprehensive approach for deriving ontologies from folksonomies byintegrating multiple resources and techniques. In detail, we suggest combining(1) the statistical analysis of folksonomies, associated usage data, and theirimplicit social networks, (2) online lexical resources like dictionaries, Wordnet,Google and Wikipedia, (3) ontologies and Semantic Web resources, (4)ontology mapping and matching approaches, and (5) functionality that helpshuman actors in achieving and maintaining consensus over ontology elementsuggestions resulting from the preceding steps.Wed Oct 26 01:10:43 CEST 2011Piscataway, NJ, USAIEEE Intelligent SystemsMarch72--79Ontology Learning for the Semantic Web162001ontology_learning semantic_web Sun Oct 02 22:34:36 CEST 2011BostonOntology Learning for the Semantic Web2002ontology_learning phd Mon Sep 26 08:22:22 CEST 2011Transactions on Intelligent Systems and TechnologyEvaluation of Folksonomy Induction Algorithms20122012 evaluation folksonomies myown ontology_learning Tue Aug 30 08:39:27 CEST 2011Handbook on Ontologies245-267International Handbooks Information SystemOntology Learning2009handbook ontology_learning 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.Tue Aug 23 16:20:52 CEST 2011I-XXVIII, 1-347Ontology learning and population from text - algorithms, evaluation and applications.2006book ontology_learning Fri Jul 29 09:40:41 CEST 2011Presentation Slides onlyOntology Learning based on Text Mining and Social Evidence Sources2011ontology_learning text_mining social_evidences Fri Jul 29 09:40:39 CEST 2011Theoretical and Practical Perspectives on Ontology Learning from Folksonomies2010ontology_learning folksonomies Wed Jul 20 11:26:46 CEST 2011Multi-Domain Klassifikation basierend auf nutzergenerierten Metadaten2010ontology_learning ol_web2.0 thesis berlin Fri Jun 10 09:04:03 CEST 2011Handbook on Ontologies245-267International Handbooks Information SystemOntology Learning2009my_thesis ontology_learning Mon Jun 06 08:39:59 CEST 2011COLINGA Graph Model for Unsupervised Lexical Acquisition2002disambiguation ontology_learning tag unsupervised Fri Feb 25 11:22:12 CET 2011EKAW381-390Lecture Notes in Computer ScienceUsing Machine Learning to Support Continuous Ontology Development.63172010my_thesis ontology_learning ontology_maturing myown 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\ \ 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.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.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. 1Thu Feb 17 17:43:15 CET 2011KasselLernen von Ontologien aus kollaborativen Tagging-SystemenMaster Thesis2009master_thesis ol_web2.0 ontology_learning 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.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.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.ontology_learningCommunity for tag(s) ontology_learning