Titlebook: Data Integration in the Life Sciences; 6th International Wo Norman W. Paton,Paolo Missier,Cornelia Hedeler Conference proceedings 2009 Spri

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https://doi.org/10.1007/978-3-642-18436-9f these databases contain overlapping, related or conflicting information. Data integration methods are being developed to address these issues by providing a consolidated view over multiple databases. However, a key challenge for data integration is the identification of links between closely relat

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https://doi.org/10.1007/978-3-642-12636-9 generally not tailored to specific biological functions or processes. This problem may be overcome by extracting process-specific sub-networks, but this approach discards useful information and is of limited use in poorly annotated areas of the network. Here we describe an extension to existing int

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Dimiter R. Avresky,Michel Diaz,Eliezer Dekelbject properties. Such annotation mappings are utilized in different applications and analysis studies whose results strongly depend on the quality of the used annotations. To study the quality of annotations we propose a generic evaluation approach considering the annotation generation methods (pro

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https://doi.org/10.1007/978-3-642-12636-9ction techniques focus on learning wrappers based on examples from one class of Web pages, i.e. from Web pages that are all similar in structure and content. Thereby, traditional wrapper induction targets the understanding of Web pages generated from a database using the same generation template as

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The Genealogy of Cloud Computing,ds on a user’s domain knowledge and the knowledge of the search technology. In this paper we present a system that helps users formulate queries and search the scientific literature. The system coordinates ontologies, knowledge representation, text mining and NLP techniques to generate relevant quer

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