The availability of real-time passenger information (RTPI) is a key factor in making public transport both accessible and attractive to users. Unfortunately, rural areas often lack the infrastructure necessary to provide such information, and the cost of deploying and maintaining the required technologies outside of urban areas is seen as prohibitive. In this paper we present the GetThere system developed to overcome such issues and to provide public transport users in rural areas with RTPI. An ontological framework for representing mobility information is described, along with the Linked Data approach used to integrate heterogeneous data from multiple sources including government, transport operators, and the public. To mitigate possible issues with the veracity of this data, a quality assessment framework was developed that utilises data provenance. We also discuss our experiences working with Semantic Web technologies in this domain, and present results from both a user trial and a performance evaluation of the system.

The availability of real-time passenger information (RTPI) is a key factor in making public transport both accessible and attractive to users. Unfortunately, rural areas often lack the infrastructure necessary to provide such information, and the cost of deploying and maintaining the required technologies outside of urban areas is seen as prohibitive. In this paper we present the GetThere system developed to overcome such issues and to provide public transport users in rural areas with RTPI. An ontological framework for representing mobility information is described, along with the Linked Data approach used to integrate heterogeneous data from multiple sources including government, transport operators, and the public. To mitigate possible issues with the veracity of this data, a quality assessment framework was developed that utilises data provenance. We also discuss our experiences working with Semantic Web technologies in this domain, and present results from both a user trial and a performance evaluation of the system.

Assessing the quality of data published on the Web has been identified as an essential step in selecting reliable information for use in tasks such as decision making. This paper discusses a quality assessment framework based on semantic web technologies and outlines a role for provenance in supporting and documenting such assessments.

In this article, we present a model for quality assessment over linked data. This model has been designed to align with emerging standards for provenance on the Web to enable agents to reason about data provenance when performing quality assessment. The model also enables quality assessment provenance to be represented, thus allowing agents to make decisions about reuse of existing assessments. We also discuss the development of an OWL ontology as part of a software framework to support reasoning about data quality and assessment reuse. Finally, we evaluate this framework using two real-world case studies derived from transport and invasive-species monitoring applications.

Assessing the quality of sensor data available on the Web is essential in order to identify reliable information for decision-making. This paper discusses how provenance of sensor observations and previous quality ratings can influence quality assessment decisions.