This thesis investigates the introduction of time as a first-class citizen to RDF-based knowledge bases as used by the Linked Data movement. By presenting EvoOnt, a use-case scenario from the field of software comprehension we demonstrate a particular field that (1) benefits from the Semantic Web’s tools and techniques, (2) has a high update rate and (3) is a candidate-dataset for Linked Data. EvoOnt is a set of OWL ontologies that cover three aspects of the software development process: A source code ontology that abstracts the elements of object-oriented code, a defect tracker ontology that models the contents of a defect database (a.k.a. bug tracker) and finally a version ontology that allows the expression of multiple versions of a source code file. In multiple experiment we demonstrate how Semantic Web tools and techniques can be used to perform common tasks known from software comprehension. Derived from this use case we show how the temporal dimension can be leveraged in RDF data. Firstly, we present a representation format for the annotation of RDF triples with temporal validity intervals. We propose a special usage of named graphs in order to encode temporal triples. Secondly, we demonstrate how such a knowledge base can be queried using a temporal syntax extension of the SPARQL query language. Next, we present two indexing structures that speed up the processing and querying time of temporally annotated data. Furthermore, we demonstrate how additional knowledge can be extracted from the temporal dimension by matching patterns that contain temporal constraints. All those elements put together outlines a method that can be used to make the datasets published as Linked Data more robust to possible invalidations through updates of liked datasets. Additionally, processing and querying can be improved through sophisticated index structures while deriving additional information from the history of a dataset.