a b s t r a c tRural communities face a range of challenges associated with accessibility and connectivity which apply in both the physical and virtual sphere. Constraints in rural transport infrastructure and services are often compounded by limitations in the development and resilience of technological infrastructures. In this context there is significant disparity between urban and rural communities. This paper will examine the context for accessibility and connectivity in rural communities highlighting key transport and technology challenges. It also explores barriers and opportunities to bringing together transport and technology solutions to enhance rural accessibility and connectivity. This is an area where current understanding is weak as most research has been focussed on urban environments. The paper focuses specifically on two issues of current research; firstly, the role of information and associated technologies in supporting rural passengers on public transport, secondly, the use of technologies to support flexible and demand responsive transport services in rural areas.

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Abstract. The original Semantic Web vision was explicit in the need for intelligent autonomous agents that would represent users and help them navigate the Semantic Web. We argue that an essential feature for such agents is the capability to analyse data and learn. In this paper we outline the challenges and issues surrounding the application of clustering algorithms to Semantic Web data. We present several ways to extract instances from a large RDF graph and computing the distance between these. We evaluate our approaches on three different data-sets, one representing a typical relational database to RDF conversion, one based on data from a ontologically rich Semantic Web enabled application, and one consisting of a crawl of FOAF documents; applying both supervised and unsupervised evaluation metrics. Our evaluation did not support choosing a single combination of instance extraction method and similarity metric as superior in all cases, and as expected the behaviour depends greatly on the data being clustered. Instead, we attempt to identify characteristics of data that make particular methods more suitable.

We argue that in a distributed context, such as the Semantic Web, ontology engineers and data creators often cannot control (or even imagine) the possible uses their data or ontologies might have. Therefore ontologies are unlikely to identify every useful or interesting classification possible in a problem domain, for example these might be of a personalised nature and only appropriate for a certain user in a certain context, or they might be of a different granularity than the initial scope of the ontology. We argue that machine learning techniques will be essential within the Semantic Web context to allow these unspecified classifications to be identified. In this paper we explore the application of machine learning methods to FOAF, highlighting the challenges posed by the characteristics of such data. Specifically, we use clustering to identify classes of people and inductive logic programming (ILP) to learn descriptions of these groups. We argue that these descriptions constitute re-usable, first class knowledge that is neither explicitly stated nor deducible from the input data. These new descriptions can be represented as simple OWL class restrictions or more sophisticated descriptions using SWRL. These are then suitable either for incorporation into future versions of ontologies or for on-the-fly use for personalisation tasks.