Innovations in localisation have focused on the collection and leverage of language resources. However, smaller localisation clients and Language Service Providers are poorly positioned to exploit the benefits of language resource reuse in comparison to larger companies. Their low throughput of localised content means they have little opportunity to amass significant resources, such as Translation memories and Terminology databases, to reuse between jobs or to train statistical machine translation engines tailored to their domain specialisms and language pairs. We propose addressing this disadvantage via the sharing and pooling of language resources. However, the current localisation standards do not support multiparty sharing, are not well integrated with emerging language resource standards and do not address key requirements in determining ownership and license terms for resources. We survey standards and research in the area of Localisation, Language Resources and Language Technologies to leverage existing localisation standards via Linked Data methodologies. This points to the potential of using semantic representation of existing data models for localisation workflow metadata, terminology, parallel text, provenance and access control, which we illustrate with an RDF example.