Titlebook: Data Integration in the Life Sciences; 7th International Co Patrick Lambrix,Graham Kemp Conference proceedings 2010 Springer-Verlag Berlin

有害处

陶瓷

https://doi.org/10.1007/978-3-658-20967-4tained output pages of the related data sources, by query probing using . identified input instances. Then, using a hierarchical representation of schemas and by applying clustering techniques, we are able to generate schema matches. We show the effectiveness of our technique while integrating 24 query interfaces.

拒绝

Christian Dremel,Matthias Herteriching, or at least minimizing the manual effort required during, creation of quantitative models from qualitative models and experimental data. Automating the process makes model construction more systematic, supports good practice at all stages in the pipeline, and allows timely integration of high throughput experimental results into models.

headway

悲痛

千篇一律

Discovering Evolving Regions in Life Science Ontologiesns impossible. We therefore propose an approach to automatically discover evolving or stable ontology regions. We evaluate the approach by studying evolving regions in the Gene Ontology and the NCI Thesaurus.

宽容

On Matching Large Life Science Ontologies in Paralleld intra-matcher parallelism as well as the parallel execution of element- and structure-level matching. We implemented a distributed infrastructure for parallel ontology matching and evaluate different approaches for parallel matching of large life science ontologies in the field of anatomy and molecular biology.

谎言

venous-leak

Instance Discovery and Schema Matching with Applications to Biological Deep Web Data Integrationtained output pages of the related data sources, by query probing using . identified input instances. Then, using a hierarchical representation of schemas and by applying clustering techniques, we are able to generate schema matches. We show the effectiveness of our technique while integrating 24 query interfaces.

恃强凌弱的人