Matches in ScholarlyData for { ?s ?p ?o. }
- 10 creator catherine-dolbear.
- 10 creator glen-hart.
- 10 creator martina-johnson.
- 10 type InProceedings.
- 10 label "Rabbit: Developing a Control Natural Language for Authoring Ontologies".
- 10 sameAs 10.
- 10 abstract "The mathematical nature of description logics has meant that domain experts find it hard to understand. This forms a significant impediment to the creation and adoption of ontologies. This paper describes Rabbit, a Controlled Natural Language that can be translated into OWL with the aim of achieving both comprehension by domain experts and computational preciseness. We see Rabbit as complementary to OWL, extending its reach to those who need to author and understand domain ontologies but for whom OWL is difficult to comprehend even when expressed in more user-friendly forms such as the Manchester Syntax. The paper outlines the main grammatical aspects of Rabbit, which can be broadly classified into declarations, concept descriptions and definitions, and elements to support interoperability between ontologies. The paper also describes the human subject testing that has been performed to date and indicates the changes currently being made to the language following this testing. Further modifications have been based on practical experience result from the application of Rabbit for the development of operational ontologies in the domain of topography.".
- 10 hasAuthorList authorList.
- 10 hasTopic Formal_languages.
- 10 hasTopic Inference.
- 10 hasTopic Logic.
- 10 hasTopic Natural_language_processing.
- 10 hasTopic Ontology_%28computer_science%29.
- 10 hasTopic Semantic_Web.
- 10 hasTopic Web_Ontology_Language.
- 10 isPartOf proceedings.
- 10 keyword "authoring".
- 10 keyword "cnl".
- 10 keyword "controlled natural language".
- 10 keyword "ontology".
- 10 keyword "owl syntax".
- 10 title "Rabbit: Developing a Control Natural Language for Authoring Ontologies".
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- item-1 hasContent glen-hart.
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- item-2 hasContent martina-johnson.
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- item-3 hasContent catherine-dolbear.
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- 105 creator eero-hyvoenen.
- 105 creator jari-vaeaetaeinen.
- 105 creator tomi-kauppinen.
- 105 type InProceedings.
- 105 label "Creating and Using Geospatial Ontology Time Series in a Semantic Cultural Heritage Portal".
- 105 sameAs 105.
- 105 abstract "Content annotations in semantic cultural heritage portals commonly make spatiotemporal references to historical regions and places using names whose meanings are different in different times. For example, historical administrational regions such as countries, municipalities, and cities have been renamed, merged together, split into parts, and annexed or moved to and from other regions. Even if the names of the regions remain the same (e.g., Germany), the underlying regions and their relationships to other regions may change (e.g., the regional coverage of Germany at different times). As a result, representing and finding the right ontological meanings for historical geographical names on the semantic web creates severe problems both when annotating contents and during information retrieval. This paper presents a model for representing the meaning of changing geospatial resources. Our aim is to enable precise annotation with temporal geospatial resources and to enable semantic search and browsing using related names from other historical time periods. A simple model and metadata schema is presented for representing and maintaining geospatial changes from which an explicit time series of temporal part-of ontologies can be created automatically. The model has been applied successfully to representing the complete change history of municipalities in Finland during 1865–2007, and the resulting ontology time series is used in the semantic cultural heritage portal CULTURESAMPO to support faceted semantic search of contents and to visualizing historical regions on overlaying maps originating from different historical eras.".
- 105 hasAuthorList authorList.
- 105 hasTopic Ontology_%28computer_science%29.
- 105 hasTopic Ontology_alignment.
- 105 hasTopic Search_engine.
- 105 hasTopic Semantic_Web.
- 105 hasTopic Web_Ontology_Language.
- 105 isPartOf proceedings.
- 105 keyword "annotation".
- 105 keyword "change".
- 105 keyword "ontology mapping".
- 105 keyword "query".
- 105 keyword "spatio-temporal".
- 105 title "Creating and Using Geospatial Ontology Time Series in a Semantic Cultural Heritage Portal".
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- item-1 hasContent tomi-kauppinen.
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- item-2 hasContent jari-vaeaetaeinen.
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- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent eero-hyvoenen.
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- 107 creator alexandros-valarakos.
- 107 creator george-vouros.
- 107 creator vassilis-spiliopoulos.
- 107 type InProceedings.
- 107 label "CSR: Discovering Subsumption Relations for the Alignment of Ontologies".
- 107 sameAs 107.
- 107 abstract "For the effective alignment of ontologies, the computation of equivalence relations between elements of ontologies is not enough: Subsumption relations play a crucial role as well. In this paper we propose the "Classification-Based Learning of Subsumption Relations for the Alignment of Ontologies" (CSR) method. Given a pair of concepts from two ontologies, the objective of CSR is to identify patterns of concepts' features that provide evidence for the subsumption relation among them. This is achieved by means of a classification task, using state of the art supervised machine learning methods. For the learning of the classifiers, CSR generates training datasets from the source ontologies', considering each ontology in isolation: This allows the method to tune itself to the idiosyncrasies of each of the source ontologies. The paper describes thoroughly the method, provides experimental results over an extended version of benchmarking series and discusses the potential of the method.".
- 107 hasAuthorList authorList.
- 107 hasTopic Data_integration.
- 107 hasTopic Machine_learning.
- 107 hasTopic Ontology_%28computer_science%29.
- 107 hasTopic Ontology_alignment.
- 107 hasTopic Semantic_Web.
- 107 isPartOf proceedings.
- 107 keyword "binary classification".
- 107 keyword "ontology alignment".
- 107 keyword "subsumption".
- 107 keyword "supervised machine learning".
- 107 title "CSR: Discovering Subsumption Relations for the Alignment of Ontologies".
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