The semantic web aims to make the meaning of data on the web explicit and machine processable. Harking back to Leibniz in its vision, it imagines a world of interlinked information that computers `understand' and `know' how to process based on its meaning. Spearheaded by the World Wide Web Consortium, ontology languages OWL and RDF form the core of the current technical offerings. RDF has successfully enabled the construction of virtually unlimited webs of data, whilst OWL gives the ability to express complex relationships between RDF data triples. However, the formal semantics of these languages limit themselves to that aspect of meaning that can be captured by mechanical inference rules, leaving many open questions as to other aspects of meaning and how they might be made machine processable. The Semantic Web has faced a number of problems that are addressed by the included publications. Its germination within academia, and logical semantics has seen it struggle to become familiar, accessible and implementable for the general IT population, so an overview of semantic technologies is provided. Faced with competing `semantic' languages, such as the ISO's Topic Map standards, a method for building ISO-compliant Topic Maps in the OWL DL language has been provided, enabling them to take advantage of the more mature OWL language and tools. Supplementation with rules is needed to deal with many real-world scenarios and this is explored as a practical exercise. The available syntaxes for OWL have hindered domain experts in ontology building, so a natural language syntax for OWL designed for use by non-logicians is offered and compared with similar offerings. In recent years, proliferation of ontologies has resulted in far more than are needed in any given domain space, so a mechanism is proposed to facilitate the reuse of existing ontologies by giving contextual information and leveraging social factors to encourage wider adoption of common ontologies and achieve interoperability. Lastly, the question of meaning is addressed in relation to the need to define one's terms and to ground one's symbols by anchoring them effectively, ultimately providing the foundation for evolving a `Pragmatic Web' of action.

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In the emerging Web of Data, publishing stable and unique identifiers promises great potential in using the web as common platform to discover and enrich data in the ecologic sciences. With our collaborative e-Science platform “BEFdata”, we generated and published unique identifiers for the data repository of the Biodiversity – Ecosystem Functioning Research Unit of the German Research Foundation (BEF-China; DFG: FOR 891). We linked part of the identifiers to two external data providers, thus creating a virtual common platform including several ecological repositories. We used the Global Biodiversity Facility (GBIF) as well the International Plant Name Index (IPNI) to enrich the data from our own field observations. We conclude in discussing other potential providers for identifiers for the ecological research domain. We demonstrate the ease of making use of existing decentralized and unsupervised identifiers for a data repository, which opens new avenues to collaborative data discovery for learning, teaching, and research in ecology.

In the emerging Web of Data, publishing stable and unique identifiers promises great potential in using the web as common platform to discover and enrich data in the ecologic sciences. With our collaborative e-Science platform “BEFdata”, we generated and published unique identifiers for the data repository of the Biodiversity – Ecosystem Functioning Research Unit of the German Research Foundation (BEF-China; DFG: FOR 891). We linked part of the identifiers to two external data providers, thus creating a virtual common platform including several ecological repositories. We used the Global Biodiversity Facility (GBIF) as well the International Plant Name Index (IPNI) to enrich the data from our own field observations. We conclude in discussing other potential providers for identifiers for the ecological research domain. We demonstrate the ease of making use of existing decentralized and unsupervised identifiers for a data repository, which opens new avenues to collaborative data discovery for learning, teaching, and research in ecology.