Context-awareness is a popular subject in ubiquitous computing, wearable computing and information retrieval. Location, conditions, activity, task and affect are all viewed as useful elements of context-awareness. Devices equipped with appropriate sensors may collect contextual information directly through infrastructural services or indirectly, through inference from observed conditions. This thesis discusses approaches to representing and sharing such data, such as the selection, development or use of semantic ontologies able to support context-awareness. The problem of selecting an appropriate knowledge representation for activities requiring information about context and the cost of developing, accessing and maintaining ontologies suggests that an approach that automates some part of this activity would be beneficial. We suggest that inspiration could and should be drawn from the mechanisms that govern natural language and classification. We explore and extend an existing approach to unsupervised labelling of contexts. We identify failure modes and potential mitigating strategies for an unsupervised approach depending solely on local interactions. A third source of information in context-awareness is the social sensor. Information shared on the social web-Flickr, Facebook or Twitter, for example-is a rich source of localised information both directly and as a data source for unsupervised learning. We examine data taken from social sensors such as social tagging services, Facebook and Twitter, identifying significant variation in content and encoding which we suggest may be of benefit to the primary user populations of the site. We explore tools intended to support access to and reuse of compound terms. We adapt a well-known experiment in socially shared cognition to a crowdsourcing platform, finding support for the view that the context of message production affects message comprehension. We demonstrate examples of the use of crowdsourced data to ground ontology labels in observation. Finally, we use data taken from a number of social networks to demonstrate the tracking of a single concept across time and space, identifying localised variation in its interpretation and prevalence.