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- 12 creator annette-morales-gonzalez-quevedo.
- 12 creator c-maria-keet.
- 12 creator francis-de-la-c-fernandez-reyes.
- 12 type InProceedings.
- 12 label "Representing mereotopological relations in OWL ontologies with OntoPartS".
- 12 sameAs 12.
- 12 abstract "Representing and reasoning over mereotopological relations (parthood and location) in an ontology is a well-known challenge, because there are many relations to choose from and OWL has limited expressiveness in this regard. To address these issues, we structure mereotopological relations based on the KGEMT mereotopological theory. A correctly chosen relation counterbalances some weaknesses in OWL's representation and reasoning services. To achieve effortless selection of the appropriate relation, we hide the complexities of the underlying theory through automation of modelling guidelines in the new tool OntoPartS. It uses, mainly, the categories from DOLCE, which avoids lengthy question sessions, and it includes examples and verbalizations. OntoPartS was experimentally evaluated, which demonstrated that selecting and representing the desired relation was done efficiently and more accurately with OntoPartS.".
- 12 hasAuthorList authorList.
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- 12 keyword "OWL".
- 12 keyword "mereotopology".
- 12 keyword "ontologies".
- 12 keyword "ontology development method".
- 12 title "Representing mereotopological relations in OWL ontologies with OntoPartS".
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- 124 creator carlo-allocca.
- 124 creator enrico-motta.
- 124 creator mathieu-daquin.
- 124 type InProceedings.
- 124 label "Using Relationships Between Ontologies To Enhance Ontology Search".
- 124 sameAs 124.
- 124 abstract "Using semantic web search engines, such as Watson, Swoogle or Sindice, to find ontologies is a complex process as it is often an exploratory activity. It generally requires formulating multiple queries, browsing many pages of results and assessing the returned ontologies against each other to obtain a relevant and adequate subset of ontologies for the intended use. Our hypothesis is that part of the difficulty related to searching ontologies comes from the lack of structure in the search results, where ontologies that are implicitly related to each other are presented as disconnected and shown on different result pages. In a previous work, to overcome this situation, we devised a software framework, Kannel, that detects and makes explicit relationships between ontologies in large ontology repositories. In this paper, we present a study that compares the use of the Watson ontology search engine with the use of its extension, Watson+Kannel, that provides explicit information regarding the various types of relationships between the result ontologies. We evaluate the benefit of Watson+Kannel by measuring through various indicators how these explicit relationships between ontologies are used to improve the user’s efficiency in ontology search, thus validating our hypothesis.".
- 124 hasAuthorList authorList.
- 124 isPartOf proceedings.
- 124 keyword "Ontology Relationships".
- 124 keyword "Ontology Reuse".
- 124 keyword "Ontology Search".
- 124 keyword "Semantic Web Search Engine".
- 124 title "Using Relationships Between Ontologies To Enhance Ontology Search".
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- item-1 hasContent carlo-allocca.
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- 132 creator achim-rettinger.
- 132 creator stephan-bloehdorn.
- 132 creator uta-loesch.
- 132 type InProceedings.
- 132 label "Graph Kernels for RDF data".
- 132 sameAs 132.
- 132 abstract "The increasing availability of structured data in Resource Description Framework (RDF) format poses new challenges and opportunities for data mining. Existing approaches to mining RDF have focused on one specific data representation, one specific machine learning algorithm or one specific task, only. Kernels, however, promise a more flexible approach by providing a powerful framework for decoupling the data representation from the learning task. This paper focuses on how the well established family of kernel-based machine learning algorithms can be readily applied to instances represented as RDF graphs. We first review the problems that arise when conventional graph kernels are used for RDF graphs. We then introduce two versatile families of RDF graph kernels based on intersection graphs and intersection trees. These kernels can better exploit the inherent properties of RDF, while providing an easy to use interface between any RDF graph (including vocabulary extensions such as RDFS and OWL) and any kernel-based learning algorithm (which are available for solving many machine learning tasks). The flexibility of the approach is demonstrated on two common relational learning tasks: entity classification and link prediction. The results show that our novel RDF graph kernels with standard SVMs achieve competitive predictive performance when compared to specialized techniques for both tasks.".
- 132 hasAuthorList authorList.
- 132 isPartOf proceedings.
- 132 keyword "Classification".
- 132 keyword "Kernel Methods".
- 132 keyword "Link Prediction".
- 132 keyword "RDF".
- 132 title "Graph Kernels for RDF data".
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- item-1 hasContent uta-loesch.
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