BibSonomyburst/user/hotho/olBibSonomy RSS feed for /user/hotho/ol2012-02-15T16:27:34+01:00http://www.bibsonomy.org/bibtex/2323c8bdedc8a4643232a498ac03d6407/hothohotho2011-02-09T15:32:47+01:00learning ol ontology <span class="authorEditorList"><a href="/author/Hazman">Maryam Hazman</a>, <a href="/author/El-Beltagy">Samhaa R. El-Beltagy</a>, and <a href="/author/Rafea">Ahmed Rafea</a> </span><em>International Journal of Metadata, Semantics and Ontologies</em> (<em>2009</em>)Wed Feb 09 15:32:47 CET 2011International Journal of Metadata, Semantics and Ontologies24-33(10)Ontology learning from domain specific web documents42009learning ol ontology Ontologies play a vital role in many web- and internet-related applications. This work presents a system for accelerating the ontology building process via semi-automatically learning a hierarchal ontology given a set of domain-specific web documents and a set of seed concepts. The methods are tested with web documents in the domain of agriculture. The ontology is constructed through the use of two complementary approaches. The presented system has been used to build an ontology in the agricultural domain using a set of Arabic extension documents and evaluated against a modified version of the AGROVOC ontology.IngentaConnect Ontology learning from domain specific web documentshttp://www.bibsonomy.org/bibtex/227c1c374a750725824118ac02ba5f2c6/hothohotho2011-01-17T12:48:19+01:00cross information lingual ol ontology <span class="authorEditorList"><a href="/author/Sorg">Philipp Sorg</a>, and <a href="/author/Cimiano">Philipp Cimiano</a> </span><em>Working Notes for the CLEF 2008 Workshop, </em>(<em>2008</em>)Mon Jan 17 12:48:19 CET 2011Working Notes for the CLEF 2008 WorkshopCross-lingual Information Retrieval with Explicit Semantic AnalysisInproceedings2008cross information lingual ol ontology http://www.bibsonomy.org/bibtex/2813903a333a40ecf9a59ded552acb323/hothohotho2011-01-17T10:31:12+01:00antrag learning multilingual ol ontology <span class="authorEditorList"><a href="/author/Hjelm">Hans Hjelm</a>, and <a href="/author/Buitelaar">Paul Buitelaar</a> </span><em>ECAI, </em><em>volume 178 of Frontiers in Artificial Intelligence and Applications, </em><em>page 288-292. </em><em>IOS Press, </em>(<em>2008</em>)Mon Jan 17 10:31:12 CET 2011ECAIconf/ecai/2008288-292Frontiers in Artificial Intelligence and ApplicationsMultilingual Evidence Improves Clustering-based Taxonomy Extraction.1782008antrag learning multilingual ol ontology dblphttp://www.bibsonomy.org/bibtex/2ec3c256e7d1f24cd9d407d3ce7e41d96/hothohotho2010-08-12T19:29:06+02:00analysis folksonomy ol semantic taxonomy toread <span class="authorEditorList"><a href="/author/Eda">Takeharu Eda</a>, <a href="/author/Yoshikawa">Masatoshi Yoshikawa</a>, <a href="/author/Uchiyama">Toshio Uchiyama</a>, and <a href="/author/Uchiyama">Tadasu Uchiyama</a> </span><em>World Wide Web</em> <em>12(4):421-440</em> (<em>2009</em>)Thu Aug 12 19:29:06 CEST 2010World Wide Web4421-440The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags.122009analysis folksonomy ol semantic taxonomy toread dblphttp://www.bibsonomy.org/bibtex/2ba43b0db4b8f7cb091fd55d59e170477/hothohotho2010-06-17T20:42:17+02:002010 myown ol ontology semantics websci websci10 <span class="authorEditorList"><a href="/author/Benz">Dominik Benz</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proceedings of the 2nd Web Science Conference WebSci10, </em><em>Raleigh, NC, USA, </em>(<em>2010</em>)Thu Jun 17 20:42:17 CEST 2010Raleigh, NC, USAProceedings of the 2nd Web Science Conference (WebSci10)Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge20102010 myown ol ontology semantics websci websci10 http://www.bibsonomy.org/bibtex/28d57c1e57c7aba60acb767e3d5b0fa13/hothohotho2010-03-20T22:02:36+01:00approach folksonomies integrated ol ontologies tagging taggingsurvey toread turning <span class="authorEditorList"><a href="/author/Damme">Céline Van Damme</a>, <a href="/author/Hepp">Martin Hepp</a>, and <a href="/author/Siorpaes">Katharina Siorpaes</a> </span><em>Bridging the Gap between Semantic Web and Web 2.0 SemNet 2007, </em><em>page 57--70. </em><em>Innsbruck, </em>(<em>2007</em>)Sat Mar 20 22:02:36 CET 2010InnsbruckBridging the Gap between Semantic Web and Web 2.0 (SemNet 2007)57--70FolksOntology: An Integrated Approach for Turning Folksonomies into Ontologies2007approach folksonomies integrated ol ontologies tagging taggingsurvey toread turning http://www.bibsonomy.org/bibtex/2355fcbb32255f3ba5f41819c00c520ba/hothohotho2010-03-20T21:45:30+01:00folksonomy ol semantic tagging taggingsurvey <span class="authorEditorList"><a href="/author/Zhou">Mianwei Zhou</a>, <a href="/author/Bao">Shenghua Bao</a>, <a href="/author/Wu">Xian Wu</a>, and <a href="/author/Yu">Yong Yu</a> </span><em>Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference ISWC/ASWC2007, Busan, South Korea, </em><em>volume 4825 of LNCS, </em><em>page 673--686. </em><em>Berlin, Heidelberg, </em><em>Springer Verlag, </em>(<em>November 2007</em>)Sat Mar 20 21:45:30 CET 2010Berlin, HeidelbergProceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Koreahttp://data.semanticweb.org/conference/iswc-aswc/2007/proceedingsNovember673--686LNCSAn Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations48252007folksonomy ol semantic tagging taggingsurvey This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotation services have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largely lowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might become a key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations, for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervised model to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.us as example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We further apply our model on another data set from Flickr to testify our model's applicability on different environments. The experimental results demonstrate our model's effciency.http://www.bibsonomy.org/bibtex/2559ee9d48f1a510f56765b2357aa8ea5/hothohotho2010-03-20T21:36:19+01:00folksonomy learning ol relation tagging taggingsurvey <span class="authorEditorList"><a href="/author/Plangprasopchok">A. Plangprasopchok</a>, and <a href="/author/Lerman">K. Lerman</a> </span><em>WWW &#039;09: Proceedings of the 18th international conference on World wide web, </em><em>page 781--790. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2009</em>)Sat Mar 20 21:36:19 CET 2010New York, NY, USAWWW '09: Proceedings of the 18th international conference on World wide web781--790Constructing folksonomies from user-specified relations on flickr2009folksonomy learning ol relation tagging 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 user-specified 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.http://www.bibsonomy.org/bibtex/28c910a2d3f6708b23e03e06ff843c8a8/hothohotho2010-02-11T10:53:26+01:00evaluation ol ontology survey <span class="authorEditorList"><a href="/author/Brank">Janez Brank</a>, <a href="/author/Grobelnik">Marko Grobelnik</a>, and <a href="/author/Mladenić">Dunja Mladenić</a> </span><em>Proc. of 8th Int. multi-conf. Information Society, </em><em>page 166--169. </em>(<em>2005</em>)Thu Feb 11 10:53:26 CET 2010Proc. of 8th Int. multi-conf. Information Society166--169A Survey of Ontology Evaluation Techniques2005evaluation ol ontology survey A nice survey of ontology evaluation methods, easy to read.http://www.bibsonomy.org/bibtex/2f80af45b8659db1a4327a5ce1df3f267/hothohotho2010-02-10T11:26:14+01:00axiom learning ol ontology <span class="authorEditorList"><a href="/author/Völker">J. Völker</a>, <a href="/author/Haase">P. Haase</a>, and <a href="/author/Hitzler">P. Hitzler</a> </span><em>Ontology Learning and Population: Bridging the Gap between Text and Knowledge, </em><em>IOS Press, </em>(<em>2008</em>)Wed Feb 10 11:26:14 CET 2010Ontology Learning and Population: Bridging the Gap between Text and KnowledgeFrontiers in Artificial Intelligence and ApplicationsLearning Expressive Ontologies2008axiom learning ol ontology My thesis referenceshttp://www.bibsonomy.org/bibtex/266bec053541e521fbe68c0119806ae49/hothohotho2010-02-10T11:04:51+01:00learning nlp ol ontology wikipedia <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>)Wed Feb 10 11:04:51 CET 2010Applications 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 nlp ol ontology wikipedia 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/2bd70c98a41d8cc01464dd022dfd118b6/hothohotho2010-02-07T19:50:36+01:00learning ol ontology survey <span class="authorEditorList"><a href="/author/Cimiano">Philipp Cimiano</a>, <a href="/author/Völker">Johanna Völker</a>, and <a href="/author/Studer">Rudi Studer</a> </span><em>Information, Wissenschaft und Praxis</em> <em>57(6-7):315-320</em> (<em>2006</em>)Sun Feb 07 19:50:36 CET 2010Information, Wissenschaft und Praxis6-7315-320Ontologies on Demand? -A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text Information572006learning ol ontology survey Ontologies on Demand? - A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Texthttp://www.bibsonomy.org/bibtex/248f029a926318103b1a6e24426e9f2c7/hothohotho2010-02-07T17:39:37+01:00knowledge ol ontology <span class="authorEditorList"><a href="/author/Brewster">Christopher Brewster</a> </span><em>Department of Computer Science, University of Sheffield, </em>(<em>2008</em>)Sun Feb 07 17:39:37 CET 2010Mind the Gap: Bridging from Text to Ontological Knowledge2008knowledge ol ontology http://www.bibsonomy.org/bibtex/2e9a83a729df52557d560ad98404774c3/hothohotho2010-02-07T17:27:16+01:00learning ol ontology <span class="authorEditorList"><a href="/author/Brewster"> Brewster</a>, <a href="/author/Jupp">S Jupp</a>, <a href="/author/Luciano">J Luciano</a>, <a href="/author/Shotton">D Shotton</a>, <a href="/author/Stevens">R D Stevens</a>, and <a href="/author/Zhang">Z Zhang</a> </span><em>BMC Bioinformatics</em> (<em>2009</em>)Sun Feb 07 17:27:16 CET 2010BMC BioinformaticsIssues in learning an ontology from text10 Suppl 52009learning ol ontology BACKGROUND: Ontology construction for any domain is a labour intensive and complex process. Any methodology that can reduce the cost and increase efficiency has the potential to make a major impact in the life sciences. This paper describes an experiment in ontology construction from text for the animal behaviour domain. Our objective was to see how much could be done in a simple and relatively rapid manner using a corpus of journal papers. We used a sequence of pre-existing text processing steps, and here describe the different choices made to clean the input, to derive a set of terms and to structure those terms in a number of hierarchies. We describe some of the challenges, especially that of focusing the ontology appropriately given a starting point of a heterogeneous corpus. RESULTS: Using mainly automated techniques, we were able to construct an 18055 term ontology-like structure with 73% recall of animal behaviour terms, but a precision of only 26%. We were able to clean unwanted terms from the nascent ontology using lexico-syntactic patterns that tested the validity of term inclusion within the ontology. We used the same technique to test for subsumption relationships between the remaining terms to add structure to the initially broad and shallow structure we generated. All outputs are available at http://thirlmere.aston.ac.uk/\~kiffer/animalbehaviour/. CONCLUSION: We present a systematic method for the initial steps of ontology or structured vocabulary construction for scientific domains that requires limited human effort and can make a contribution both to ontology learning and maintenance. The method is useful both for the exploration of a scientific domain and as a stepping stone towards formally rigourous ontologies. The filtering of recognised terms from a heterogeneous corpus to focus upon those that are the topic of the ontology is identified to be one of the main challenges for research in ontology learning.Issues in learning an ontology from text. [BMC Bioinformatics. 2009] - PubMed resulthttp://www.bibsonomy.org/bibtex/28213d4d08414fd60fe86e59d41895d4e/hothohotho2010-02-07T16:50:10+01:00nlp2rdf ol ontology <span class="authorEditorList"><a href="/author/Buitelaar">Paul Buitelaar</a>, <a href="/author/Cimiano">Philipp Cimiano</a>, <a href="/author/Haase">Peter Haase</a>, and <a href="/author/Sintek">Michael Sintek</a> </span><em>6th Annual European Semantic Web Conference ESWC2009, </em><em>page 111-125. </em>(<em>June 2009</em>)Sun Feb 07 16:50:10 CET 20106th Annual European Semantic Web Conference (ESWC2009)June111-125Towards Linguistically Grounded Ontologies2009nlp2rdf ol ontology In this paper we argue why it is necessary to associate linguistic information with ontologies and why more expressive models, beyond RDFS, OWL and SKOS, are needed to capture the relation between natural language constructs on the one hand and ontological entities on the other. We argue that in the light of tasks such as ontology-based information extraction, ontology learning and population from text and natural language generation from ontologies, currently available datamodels are not sufficient as they only allow to associate atomic terms without linguistic grounding or structure to ontology elements. Towards realizing a more expressive model for associating linguistic information to ontology elements, we base our work presented here on previously developed models (LingInfo, LexOnto, LMF) and present a new joint model for linguistic grounding of ontologies called LexInfo. LexInfo combines essential design aspects of LingInfo and LexOnto and builds on a sound model for representing computational lexica called LMF which has been recently approved as a standard under ISO.http://www.bibsonomy.org/bibtex/2504c0b73b391933fb0536b135144ae1d/hothohotho2010-02-07T14:37:37+01:00learning ol ontology <span class="authorEditorList"><a href="/author/Sánchez">David Sánchez</a> </span><em>Saarbrücken, </em>(<em>2007</em>)Sun Feb 07 14:37:37 CET 2010Saarbrücken--Domain ontology learning from the web an unsupervised, automatic and domain independent approach2007learning ol ontology http://www.bibsonomy.org/bibtex/2e44d2fd55b85cc56b08afd134f437012/hothohotho2010-02-07T14:28:59+01:00ol <span class="authorEditorList"><a href="/author/Jiang">Xing Jiang</a>, and <a href="/author/Tan">Ah-Hwee Tan</a> </span><em>J. Am. Soc. Inf. Sci. Technol.</em> <em>61(1):150--168</em> (<em>2010</em>)Sun Feb 07 14:28:59 CET 2010New York, NY, USAJ. Am. Soc. Inf. Sci. Technol.1150--168CRCTOL: A semantic-based domain ontology learning system612010ol Domain ontologies play an important role in supporting knowledge-based applications in the Semantic Web. To facilitate the building of ontologies, text mining techniques have been used to perform ontology learning from texts. However, traditional systems employ shallow natural language processing techniques and focus only on concept and taxonomic relation extraction. In this paper we present a system, known as Concept-Relation-Concept Tuple-based Ontology Learning (CRCTOL), for mining ontologies automatically from domain-specific documents. Specifically, CRCTOL adopts a full text parsing technique and employs a combination of statistical and lexico-syntactic methods, including a statistical algorithm that extracts key concepts from a document collection, a word sense disambiguation algorithm that disambiguates words in the key concepts, a rule-based algorithm that extracts relations between the key concepts, and a modified generalized association rule mining algorithm that prunes unimportant relations for ontology learning. As a result, the ontologies learned by CRCTOL are more concise and contain a richer semantics in terms of the range and number of semantic relations compared with alternative systems. We present two case studies where CRCTOL is used to build a terrorism domain ontology and a sport event domain ontology. At the component level, quantitative evaluation by comparing with Text-To-Onto and its successor Text2Onto has shown that CRCTOL is able to extract concepts and semantic relations with a significantly higher level of accuracy. At the ontology level, the quality of the learned ontologies is evaluated by either employing a set of quantitative and qualitative methods including analyzing the graph structural property, comparison to WordNet, and expert rating, or directly comparing with a human-edited benchmark ontology, demonstrating the high quality of the ontologies learned. © 2010 Wiley Periodicals, Inc.CRCTOL: A semantic-based domain ontology learning systemhttp://www.bibsonomy.org/bibtex/2d70653a1a21b9e84904def9d2fdb5151/hothohotho2010-02-07T14:21:16+01:00learning ol ontology <span class="authorEditorList"><a href="/author/Navigli">R. Navigli</a>, <a href="/author/Velardi">P. Velardi</a>, and <a href="/author/Gangemi">A. Gangemi</a> </span><em>Intelligent Systems, IEEE</em> <em>18(1):22-31</em> (<em>January 2003</em>)Sun Feb 07 14:21:16 CET 2010Intelligent Systems, IEEEJan-Feb1 22-31Ontology learning and its application to automated terminology translation182003learning ol ontology Our OntoLearn system is an infrastructure for automated ontology learning from domain text. It is the only system, as far as we know, that uses natural language processing and machine learning techniques, and is part of a more general ontology engineering architecture. We describe the system and an experiment in which we used a machine-learned tourism ontology to automatically translate multiword terms from English to Italian. The method can apply to other domains without manual adaptation.IEEE Xplore# Wrapper Resulthttp://www.bibsonomy.org/bibtex/2331564c7d3891041c1024591532a45ec/hothohotho2010-02-07T14:20:04+01:00learning ol ontology <span class="authorEditorList"><a href="/author/Brewster">Christopher Brewster</a>, <a href="/author/Ciravegna">Fabio Ciravegna</a>, and <a href="/author/Wilks">Yorick Wilks</a> </span><em>NLDB &#039;02: Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems-Revised Papers, </em><em>page 203--207. </em><em>London, UK, </em><em>Springer-Verlag, </em>(<em>2002</em>)Sun Feb 07 14:20:04 CET 2010London, UKNLDB '02: Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems-Revised Papers203--207User-Centred Ontology Learning for Knowledge Management2002learning ol ontology User-Centred Ontology Learning for Knowledge Managementhttp://www.bibsonomy.org/bibtex/2f113eb70fed0141d87672429cb27bba3/hothohotho2010-02-07T14:14:13+01:00learning ol ontology <span class="authorEditorList"><a href="/author/Lenci">Alessandro Lenci</a>, <a href="/author/Montemagni">Simonetta Montemagni</a>, <a href="/author/Pirrelli">Vito Pirrelli</a>, and <a href="/author/Venturi">Giulia Venturi</a> </span><em>LOAIT, </em><em>volume 321 of CEUR Workshop Proceedings, </em><em>page 113-129. </em><em>CEUR-WS.org, </em>(<em>2007</em>)Sun Feb 07 14:14:13 CET 2010LOAITconf/icail/2007loait113-129CEUR Workshop ProceedingsNLP-based Ontology Learning from Legal Texts. A Case Study.3212007learning ol ontology dblp