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- 207 creator lihua-zhao.
- 207 creator ryutaro-ichise.
- 207 type InProceedings.
- 207 label "Instance-based ontological knowledge acquisition".
- 207 sameAs 207.
- 207 abstract "The Linked Open Data (LOD) cloud contains tremendous amounts of interlinked instances, from where we can retrieve abundant knowledge. However, because of the heterogeneous and big ontologies, it is time consuming to learn all the ontologies manually and it is difficult to observe which properties are important for describing instances of a specific class. In order to construct an ontology that can help users easily access various data sets, we propose a semi-automatic ontology integration framework that can reduce the heterogeneity of ontologies and retrieve frequently used core properties for each class. The framework consists of three main components: graph-based ontology integration, machine learning based ontology schema extraction, and an ontology merger. By analyzing instances of the linked data sets, this framework acquires ontological knowledge and construct a high-quality integrated ontology, which is easily understandable and effective in knowledge acquisition from various data sets using simple SPARQL queries.".
- 207 hasAuthorList authorList.
- 207 isPartOf proceedings.
- 207 keyword "Semantic Web".
- 207 keyword "knowledge acquisition".
- 207 keyword "linked data".
- 207 keyword "machine learning".
- 207 keyword "ontology integration".
- 207 title "Instance-based ontological knowledge acquisition".