The Bio2RDF project aims to transform silos of bioinformatics data into a distributed platform for biological knowledge discovery. Initial work focused on building a public database of open-linked data with web-resolvable identifiers that provides information about named entities. This involved a syntactic normalization to convert open data represented in a variety of formats (flatfile, tab, xml, web services) to RDF-based linked data with normalized names (HTTP URIs) and basic typing from source databases. Bio2RDF entities also make reference to other open linked data networks (e.g. dbPedia) thus facilitating traversal across information spaces. However, a significant problem arises when attempting to undertake more sophisticated knowledge discovery approaches such as question answering or symbolic data mining. This is because knowledge is represented in a fundamentally different manner, requiring one to know the underlying data model and reconcile the artefactual differences when they arise. In this talk, we describe our data integration strategy that makes use of both syntactic and semantic normalization to consistently marshal knowledge to a common data model while leveraging explicit logic-based mappings with community ontologies to further enhance the biological knowledgescope.