Matches in ScholarlyData for { ?s ?p ?o. }
- item-4 previous item-3.
- item-5 type ListItem.
- item-5 hasContent anupam-joshi.
- item-5 previous item-4.
- 224 creator anthony-g-cohn.
- 224 creator catherine-dolbear.
- 224 creator glen-hart.
- 224 creator ian-holt.
- 224 creator ronald-denaux.
- 224 creator vania-dimitrova.
- 224 type InProceedings.
- 224 label "Involving Domain Experts in Authoring OWL Ontologies".
- 224 sameAs 224.
- 224 abstract "The process of authoring ontologies requires the active involvement of domain experts who should lead the process, as well as providing the relevant conceptual knowledge. However, most domain experts lack knowledge modelling skills and find it hard to follow logical notations in OWL. This paper presents ROO, a tool that facilitates domain experts' definition of ontologies in OWL by allowing them to author the ontology in a controlled natural language called Rabbit. ROO guides users through the ontology construction process by following a methodology geared towards domain experts’ involvement in ontology authoring, and exploiting intelligent user interfaces techniques. An evaluation study has been conducted comparing ROO against another popular ontology authoring tool. Participants were asked to create ontologies based on hydrology and environment modelling scenarios related to real tasks at the mapping agency of Great Britain. The study is discussed, focusing on the usability and usefulness of the tool, and the quality of the resultant ontologies.".
- 224 hasAuthorList authorList.
- 224 hasTopic Semantic_Web.
- 224 isPartOf proceedings.
- 224 keyword "Controlled Natural Language interfaces".
- 224 keyword "Evaluation of Ontology Building Tools".
- 224 keyword "Geographical ontologies".
- 224 keyword "Ontology authoring".
- 224 title "Involving Domain Experts in Authoring OWL Ontologies".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasItem item-5.
- authorList hasItem item-6.
- authorList hasLastItem item-6.
- item-1 type ListItem.
- item-1 hasContent vania-dimitrova.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent ronald-denaux.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent glen-hart.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent catherine-dolbear.
- item-4 next item-5.
- item-4 previous item-3.
- item-5 type ListItem.
- item-5 hasContent ian-holt.
- item-5 next item-6.
- item-5 previous item-4.
- item-6 type ListItem.
- item-6 hasContent anthony-g-cohn.
- item-6 previous item-5.
- 229 creator gwenn-englebienne.
- 229 creator shenghui-wang.
- 229 creator stefan-schlobach.
- 229 type InProceedings.
- 229 label "Learning Concept Mappings from Instance Similarity".
- 229 sameAs 229.
- 229 abstract "Finding mappings between compatible ontologies is an important but difficult open problem. Instance-based methods for solving this problem have the advantage of focusing on the most active parts of the ontologies and reflect concept semantics as they are used in the real world. However such methods have not at present been widely investigated in ontology mapping, compared to linguistic and structural techniques. In this paper we approach the mapping problem as a classification problem based on the similarity between instances of concepts. We evaluate the resulting classifier on three different real-world data sets.".
- 229 hasAuthorList authorList.
- 229 hasTopic Semantic_Web.
- 229 isPartOf proceedings.
- 229 keyword "Classification".
- 229 keyword "Machine learning".
- 229 keyword "Ontology".
- 229 keyword "Similarity".
- 229 keyword "Thesaurus mapping".
- 229 title "Learning Concept Mappings from Instance Similarity".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasLastItem item-3.
- item-1 type ListItem.
- item-1 hasContent shenghui-wang.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent gwenn-englebienne.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent stefan-schlobach.
- item-3 previous item-2.
- 232 creator christian-wartena.
- 232 creator rogier-brussee.
- 232 type InProceedings.
- 232 label "Instanced-based Mapping between Thesauri and Folksonomies".
- 232 sameAs 232.
- 232 abstract "The emergence of web based systems in which users can annotate items, raises the question of the semantic interoperability between vocabularies originating from collaborative annotation processes, often called folksonomies, and keywords assigned in a more traditional way. If collections are annotated according to two systems, e.g. with tags and keywords, the annotated data can be used for instanced based mapping between the vocabularies. The basis for this kind of matching is an appropriate similarity measure between concepts, based on their distribution as annotations. In this paper we propose a new similarity measure that can take advantage of some special properties of user generated metadata. We have evaluated this measure with a set of articles from Wikipedia which are classified according to the topic structure of Wikipedia and that are annotated by users of the bookmarking service del.icio.us as well. The results using the new measure are significantly better than those obtained using standard similarity measures proposed for this task in the literature. We argue that the measure also has benefits for instance based mapping of more traditionally developed vocabularies. Finally, this method opens possibilities for instance based mapping of annotation data in the absence of a collection described according to both systems.".
- 232 hasAuthorList authorList.
- 232 hasTopic Semantic_Web.
- 232 isPartOf proceedings.
- 232 keyword "Co-occurence".
- 232 keyword "Distributions".
- 232 keyword "Keyword".
- 232 keyword "Matching".
- 232 keyword "Tag".
- 232 title "Instanced-based Mapping between Thesauri and Folksonomies".