排序方式: 共有2条查询结果,搜索用时 0 毫秒
1
1.
An ontology database is a basic relational database management system that models an ontology plus its instances. To reason
over the transitive closure of instances in the subsumption hierarchy, for example, an ontology database can either unfold
views at query time or propagate assertions using triggers at load time. In this paper, we use existing benchmarks to evaluate
our method—using triggers—and we demonstrate that by forward computing inferences, we not only improve query time, but the
improvement appears to cost only more space (not time). However, we go on to show that the true penalties were simply opaque
to the benchmark, i.e., the benchmark inadequately captures load-time costs. We have applied our methods to two case studies
in biomedicine, using ontologies and data from genetics and neuroscience to illustrate two important applications: first,
ontology databases answer ontology-based queries effectively; second, using triggers, ontology databases detect instance-based
inconsistencies—something not possible using views. Finally, we demonstrate how to extend our methods to perform data integration
across multiple, distributed ontology databases. 相似文献
2.
Clement Jonquet Paea LePendu Sean Falconer Adrien Coulet Natalya F. Noy Mark A. Musen Nigam H. Shah 《Journal of Web Semantics》2011,9(3):316-324
The volume of publicly available data in biomedicine is constantly increasing. However, these data are stored in different formats and on different platforms. Integrating these data will enable us to facilitate the pace of medical discoveries by providing scientists with a unified view of this diverse information. Under the auspices of the National Center for Biomedical Ontology (NCBO), we have developed the Resource Index – a growing, large-scale ontology-based index of more than twenty heterogeneous biomedical resources. The resources come from a variety of repositories maintained by organizations from around the world. We use a set of over 200 publicly available ontologies contributed by researchers in various domains to annotate the elements in these resources. We use the semantics that the ontologies encode, such as different properties of classes, the class hierarchies, and the mappings between ontologies, in order to improve the search experience for the Resource Index user. Our user interface enables scientists to search the multiple resources quickly and efficiently using domain terms, without even being aware that there is semantics “under the hood.” 相似文献
1