]> BibSonomy publications for /author/Cimiano http://www.bibsonomy.org/burst/author/Cimiano BibSonomy BuRST Feed for /author/Cimiano 2008-07-20T23:35:37+02:00 Towards the self-annotating web http://www.bibsonomy.org/bibtex/22a4489379b3af53fc7fa6bfe3121974d/enterldestodes enterldestodes 2008-07-16T11:22:10+02:00 ontology ontologylearning
Towards the self-annotating web
P. Cimiano and S. Handschuh and S. Staab (2004)
to ontology ontologylearning by enterldestodes on 2008-07-16 11:22:10 ]]> Towards the self-annotating web2004ontology ontologylearning 2008-07-16 11:22:10.0 Automatic Acquisition of Taxonomies from Text: FCA meets NLP http://www.bibsonomy.org/bibtex/2573ac0e71d6b1c369cf881ddda8c7841/summerkopi summerkopi 2008-07-09T13:32:34+02:00 TaxonomyConstruction
Automatic Acquisition of Taxonomies from Text: FCA meets NLP
Philipp Cimiano and Steffen Staab and Julien Tane Proceedings of the ECML / PKDD Workshop on Adaptive Text Extraction and Mining 10-17 (2003)
to TaxonomyConstruction by summerkopi on 2008-07-09 13:32:34 ]]> Cavtat-Dubrovnik, CroatiaProceedings of the ECML / PKDD Workshop on Adaptive Text Extraction and Mining10-17Automatic Acquisition of Taxonomies from Text: FCA meets NLP2003TaxonomyConstruction 2008-07-09 13:32:34.0 Automatic Acquisition of Taxonomies from Text: FCA meets NLP http://www.bibsonomy.org/bibtex/2573ac0e71d6b1c369cf881ddda8c7841/steff83 steff83 2008-07-07T16:31:34+02:00 evaluation master_thesis ontology_learning taxonomic_overlap
Automatic Acquisition of Taxonomies from Text: FCA meets NLP
Philipp Cimiano and Steffen Staab and Julien Tane Proceedings of the ECML / PKDD Workshop on Adaptive Text Extraction and Mining 10--17 (2003)
to evaluation master_thesis ontology_learning taxonomic_overlap by steff83 on 2008-07-07 16:31:34 ]]> Cavtat-Dubrovnik, CroatiaProceedings of the ECML / PKDD Workshop on Adaptive Text Extraction and Mining10--17Automatic Acquisition of Taxonomies from Text: FCA meets NLP2003evaluation master_thesis ontology_learning taxonomic_overlap 2008-07-07 16:31:34.0 Automatic Acquisition of Taxonomies from Text: FCA meets NLP http://www.bibsonomy.org/bibtex/2573ac0e71d6b1c369cf881ddda8c7841/dbenz dbenz 2008-07-07T15:50:15+02:00 ontology_learning taxonomic_overlap
Automatic Acquisition of Taxonomies from Text: FCA meets NLP
Philipp Cimiano and Steffen Staab and Julien Tane Proceedings of the ECML / PKDD Workshop on Adaptive Text Extraction and Mining 10--17 (2003)
to ontology_learning taxonomic_overlap by dbenz on 2008-07-07 15:50:15 ]]> Cavtat-Dubrovnik, CroatiaProceedings of the ECML / PKDD Workshop on Adaptive Text Extraction and Mining10--17Automatic Acquisition of Taxonomies from Text: FCA meets NLP2003ontology_learning taxonomic_overlap 2008-07-07 15:50:15.0 Ontology Learning from Text: Methods, Evaluation and Applications http://www.bibsonomy.org/bibtex/20e71ddd52894af0e681b9d9411f7944f/flint63 flint63 2008-07-07T13:47:29+02:00 ai analysis book language learn ontology processing semantic text v0805 web
Ontology Learning from Text: Methods, Evaluation and Applications
Paul Buitelaar and Philipp Cimiano and Bernardo Magnini (eds.). (2005)
to ai analysis book language learn ontology processing semantic text v0805 web by flint63 on 2008-07-07 13:47:29 ]]> AmsterdamFrontiers in Artificial Intelligence and ApplicationsOntology Learning from Text: Methods, Evaluation and Applications1232005ai analysis book language learn ontology processing semantic text v0805 web 2008-07-07 13:47:29.0This volume brings together ontology learning, knowledge acquisition and other related topics. It presents current research in ontology learning, addressing three perspectives. The first perspective looks at methodologies that have been proposed to automatically extract information from texts and to give a structured organization to such knowledge, including approaches based on machine learning techniques. Then there are evaluation methods for ontology learning, aiming at defining procedures and metrics for a quantitative evaluation of the ontology learning task; and finally application scenarios that make ontology learning a challenging area in the context of real applications such as bio-informatics. According to the three perspectives mentioned above, the book is divided into three sections, each including a selection of papers addressing respectively the methods, the applications and the evaluation of ontology learning approaches. Ontology Learning and Population: Bridging the Gap between Text and Knowledge http://www.bibsonomy.org/bibtex/26c49dae9157532a01415c35abc7091d1/flint63 flint63 2008-07-07T13:47:29+02:00 ai analysis book language learn ontology processing semantic text v0805 web
Ontology Learning and Population: Bridging the Gap between Text and Knowledge
Paul Buitelaar and Philipp Cimiano (eds.). (2008)
to ai analysis book language learn ontology processing semantic text v0805 web by flint63 on 2008-07-07 13:47:29 ]]> AmsterdamFrontiers in Artificial Intelligence and ApplicationsOntology Learning and Population: Bridging the Gap between Text and Knowledge1672008ai analysis book language learn ontology processing semantic text v0805 web 2008-07-07 13:47:29.0The promise of the Semantic Web is that future web pages will be annotated not only with bright colors and fancy fonts as they are now, but with annotation extracted from large domain ontologies that specify, to a computer in a way that it can exploit, what information is contained on the given web page. The presence of this information will allow software agents to examine pages and to make decisions about content as humans are able to do now. The classic method of building an ontology is to gather a committee of experts in the domain to be modeled by the ontology, and to have this committee agree on which concepts cover the domain, on which terms describe which concepts, on what relations exist between each concept and what the possible attributes of each concept are. All ontology learning systems begin with an ontology structure, which may just be an empty logical structure, and a collection of texts in the domain to be modeled. An ontology learning system can be seen as an interplay between three things: an existing ontology, a collection of texts, and lexical syntactic patterns. The Semantic Web will only be a reality if we can create structured, unambiguous ontologies that model domain knowledge that computers can handle. The creation of vast arrays of such ontologies, to be used to mark-up web pages for the Semantic Web, can only be accomplished by computer tools that can extract and build large parts of these ontologies automatically. This book provides the state-of-art of many automatic extraction and modeling techniques for ontology building. The maturation of these techniques will lead to the creation of the Semantic Web. DOLCE ergo SUMO: On foundational and domain models in the SmartWeb Integrated Ontology (SWIntO) http://www.bibsonomy.org/bibtex/21991c7632acb5822084fc962d9740eac/flint63 flint63 2008-07-07T13:47:29+02:00 ai design dialog multimodal ontology paper semantic smartweb v0805 web
DOLCE ergo SUMO: On foundational and domain models in the SmartWeb Integrated Ontology (SWIntO)
Daniel Oberle and Anupriya Ankolekar and Pascal Hitzler and Philipp Cimiano and Michael Sintek and Malte Kiesel and Babak Mougouie and Stephan Baumann and Shankar Vembu and Massimo Romanelli and Paul Buitelaar and Ralf Engel and Daniel Sonntag and Norbert Reithinger and Berenike Loos and Hans-Peter Zorn and Vanessa Micelli and Robert Porzel and Christian Schmidt and Moritz Weiten and Felix Burkhardt and Jianshen Zhou Web Semantics 5 156-174 (2007)
to ai design dialog multimodal ontology paper semantic smartweb v0805 web by flint63 on 2008-07-07 13:47:29 ]]> Web Semantics3156-174{DOLCE} ergo {SUMO}: On foundational and domain models in the {SmartWeb} Integrated Ontology ({SWIntO})52007ai design dialog multimodal ontology paper semantic smartweb v0805 web 2008-07-07 13:47:29.0Increased availability of mobile computing, such as personal digital assistants (PDAs), creates the potential for constant and intelligent access to up-to-date, integrated and detailed information from the Web, regardless of one's actual geographical position. Intelligent question-answering requires the representation of knowledge from various domains, such as the navigational and discourse context of the user, potential user questions, the information provided by Web services and so on, for example in the form of ontologies. Within the context of the SmartWeb project, we have developed a number of domain-specific ontologies that are relevant for mobile and intelligent user interfaces to open-domain question-answering and information services on the Web. To integrate the various domain-specific ontologies, we have developed a foundational ontology, the SmartSUMO ontology, on the basis of the DOLCE and SUMO ontologies. This allows us to combine all the developed ontologies into a single SmartWeb Integrated Ontology (SWIntO) having a common modeling basis with conceptual clarity and the provision of ontology design patterns for modeling consistency. In this paper, we present SWIntO, describe the design choices we made in its construction, illustrate the use of the ontology through a number of applications, and discuss some of the lessons learned from our experiences. Porting Natural Language Interfaces between Domains: An Experimental User Study with the ORAKEL System http://www.bibsonomy.org/bibtex/23081fc05e0924c378eba9805f937c8bf/flint63 flint63 2008-07-07T13:47:29+02:00 ai interaction interface knowledge language paper processing user v0805
Porting Natural Language Interfaces between Domains: An Experimental User Study with the ORAKEL System
Philipp Cimiano and Peter Haase and Jörg Heizmann IUI '07: Proceedings of the 12th International Conference on Intelligent User Interfaces 180-189 (2007)
to ai interaction interface knowledge language paper processing user v0805 by flint63 on 2008-07-07 13:47:29 ]]> New York, USA{IUI '07:} Proceedings of the 12th International Conference on Intelligent User Interfaces180-189Porting Natural Language Interfaces between Domains: An Experimental User Study with the {ORAKEL} System2007ai interaction interface knowledge language paper processing user v0805 2008-07-07 13:47:29.0We present a user-centered model for porting natural language interfaces (NLIs) between domains efficiently. The model assumes that domain experts without any background knowledge about computational linguistics will perform the customization of the NLI to a specific domain. In fact, it merely requires familiarity with the underlying knowledge base as well as with a few basic subcategorization types. Our model is iterative in the sense that the adaption of the NLI is performed in several cycles on the basis of the questions which the NLI failed to answer, thus iteratively increasing the coverage of the system. We provide experimental evidence in form of a user study as well as a case study involving a real-world application corroborating that our model is indeed a feasible way of customizing the interface to a certain domain. Automatic Acquisition of Ranked Qualia Structures from the Web. dblphttp://www.bibsonomy.org/bibtex/2bc5f8385543648ec74c2d9db20943a7d/dblp dblp 2008-07-03T00:00:00+02:00 dblp
Automatic Acquisition of Ranked Qualia Structures from the Web.
Philipp Cimiano and Johanna Wenderoth ACL (2007)
to dblp by dblp on 2008-07-03 00:00:00 ]]> ACLconf/acl/2007Automatic Acquisition of Ranked Qualia Structures from the Web.2007dblp 2008-07-03 00:00:00.0 Intensional Question Answering Using ILP: What Does an Answer Mean?. dblphttp://www.bibsonomy.org/bibtex/2d0f777c32dd79945b109ce7af6acd3ca/dblp dblp 2008-06-27T00:00:00+02:00 dblp
Intensional Question Answering Using ILP: What Does an Answer Mean?.
Philipp Cimiano and Helena Hartfiel and Sebastian Rudolph NLDB 151-162 (2008)
to dblp by dblp on 2008-06-27 00:00:00 ]]> NLDBconf/nldb/2008151-162Lecture Notes in Computer ScienceIntensional Question Answering Using ILP: What Does an Answer Mean?.50392008dblp 2008-06-27 00:00:00.0