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Matches in ScholarlyData for { ?s ?p ?o. }

• 72 hasTopic Web_2.0.
• 72 isPartOf proceedings.
• 72 keyword "adaptaion of rich internet applications".
• 72 keyword "data mining".
• 72 keyword "ontologies".
• 72 keyword "semantic web rule language".
• 72 keyword "user modeling".
• 72 title "An User Interface Adaptation Architecture for Rich Internet Applications".
• authorList type List.
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• authorList hasItem item-6.
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• item-1 type ListItem.
• item-1 hasContent kay-uwe-schmidt.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent joerg-doerflinger.
• item-2 next item-3.
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• item-3 type ListItem.
• item-3 hasContent tirdad-rahmani.
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• item-4 type ListItem.
• item-4 hasContent mehdi-sahbi.
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• item-5 hasContent susan-thomas.
• item-5 next item-6.
• item-5 previous item-4.
• item-6 type ListItem.
• item-6 hasContent ljiljana-stojanovic.
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• 90 creator alfio-ferrara.
• 90 creator davide-lorusso.
• 90 creator ralf-moeller.
• 90 creator silvana-castano.
• 90 creator tobias-henrik-naeth.
• 90 type InProceedings.
• 90 label "Mapping Validation by Probabilistic Reasoning".
• 90 sameAs 90.
• 90 abstract "In the semantic web environment, where two or more independent ontologies can be used in order to describe knowledge and data, ontologies have to be aligned by defining mappings among the elements of one ontology and the elements of another ontology. Very often, mappings are not derived by the semantics of the ontologies that are compared, but, rather, by an evaluation of the similarity of the terminology used in the two ontologies or of their syntactic structure. Moreover, ontology mappings can be inaccurate, because ontology matching tools derive such mappings from inaccurate terminology or even because they are not specifically tailored for the domain at hand. In this paper, we propose a new mapping validation approach for interpreting similarity-based mappings as semantic relations, by coping also with inaccuracy situations. The idea is to see two independent ontologies as a unique distributed knowledge base and to assume a semantic interpretation of ontology mappings as probabilistic and hypothetical relations among ontology elements. We present and use a probabilistic reasoning tool in order to validate mappings and to possibly infer new relations among the ontologies.".
• 90 hasAuthorList authorList.
• 90 hasTopic Data_integration.
• 90 hasTopic Formal_languages.
• 90 hasTopic Inference.
• 90 hasTopic Logic.
• 90 hasTopic Ontology_%28computer_science%29.
• 90 hasTopic Ontology_alignment.
• 90 hasTopic Semantic_Web.
• 90 isPartOf proceedings.
• 90 keyword "ontology mapping".
• 90 keyword "ontology matching".
• 90 keyword "probabilistic description logics".
• 90 keyword "probabilistic reasoning".
• 90 title "Mapping Validation by Probabilistic Reasoning".
• 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 hasLastItem item-5.
• item-1 type ListItem.
• item-1 hasContent silvana-castano.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent alfio-ferrara.
• item-2 next item-3.
• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent davide-lorusso.
• item-3 next item-4.
• item-3 previous item-2.
• item-4 type ListItem.
• item-4 hasContent tobias-henrik-naeth.
• item-4 next item-5.
• item-4 previous item-3.
• item-5 type ListItem.
• item-5 hasContent ralf-moeller.
• item-5 previous item-4.
• 1 creator huairen-lin.
• 1 creator joseph-davis.
• 1 creator ying-zhou.
• 1 type InProceedings.
• 1 label "An Integrated Approach to Extracting Ontological Structures from Folksonomies".
• 1 sameAs 1.
• 1 abstract "Collaborative tagging systems have recently emerged as one of the rapidly growing web 2.0 applications. The informal social classification structure in these systems, also known as folksonomy, provides a convenient way to annotate resources by allowing users to use any keyword or tag that they find relevant. In turn, the flat and non-hierarchical structure with unsupervised vocabularies leads to low search precision and poor resource navigation and retrieval. This drawback has created the need for ontological structures, which provide shared vocabularies and semantic relations for translating and integrating the different sources. In this paper, we propose an integrated approach for extracting ontological structure from folksonomies that exploits the power of low support association rule mining supplemented by an upper ontology such as WordNet.".
• 1 hasAuthorList authorList.
• 1 hasTopic Language_technology.
• 1 hasTopic Machine_learning.
• 1 hasTopic Natural_language_processing.
• 1 hasTopic Ontology_%28computer_science%29.

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