Scalable Quality Assessment of Linked Data

In a world where the information economy is booming, poor data quality can lead to adverse consequences, including social and economical problems such as decrease in revenue. Furthermore, data-driven indus- tries are not just relying on their own (proprietary) data silos, but are also continuously aggregating data from different sources. This aggregation could then be re-distributed back to “data lakes”. However, this data (including Linked Data) is not necessarily checked for its quality prior to its use. Large volumes of data are being exchanged in a standard and interoperable format between organisations and published as Linked Data to facilitate their re-use. Some organisations, such as government institutions, take a step further and open their data. The Linked Open Data Cloud is a witness to this. However, similar to data in data lakes, it is challenging to determine the quality of this heterogeneous data, and subsequently to make this information explicit to data consumers. Despite the availability of a number of tools and frameworks to assess Linked Data quality, the current solutions do not aggregate a holistic approach that enables both the assessment of datasets and also provides consumers with quality results that can then be used to find, compare and rank datasets’ fitness for use. In this thesis we investigate methods to assess the quality of (possibly large) linked datasets with the intent that data consumers can then use the assessment results to find datasets that are fit for use, that is; finding the right dataset for the task at hand. Moreover, the benefits of quality assessment are two-fold: (1) data consumers do not need to blindly rely on subjective measures to choose a dataset, but base their choice on multiple factors such as the intrinsic structure of the dataset, therefore fostering trust and reputation between the publishers and consumers on more objective foundations; and (2) data publishers can be encouraged to improve their datasets so that they can be re-used more. Furthermore, our approach scales for large datasets. In this regard, we also look into improving the efficiency of quality metrics using various approximation techniques. However the trade-off is that consumers will not get the exact quality value, but a very close estimate which anyway provides the required guidance towards fitness for use. The central point of this thesis is not on data quality improvement, nonetheless, we still need to understand what data quality means to the consumers who are searching for potential datasets. This thesis looks into the challenges faced to detect quality problems in linked datasets presenting quality results in a standardised machine-readable and interoperable format for which agents can make sense out of to help human consumers identifying the fitness for use dataset. Our proposed approach is more consumer-centric where it looks into (1) making the assessment of quality as easy as possible, that is, allowing stakeholders, possibly non-experts, to identify and easily define quality metrics and to initiate the assessment; and (2) making results (quality metadata and quality reports) easy for stakeholders to understand, or at least interoperable with other systems to facilitate a possible data quality pipeline. Finally, our framework is used to assess the quality of a number of heterogeneous (large) linked datasets, where each assessment returns a quality metadata graph that can be consumed by agents as Linked Data. In turn, these agents can intelligently interpret a dataset’s quality with regard to multiple dimensions and observations, and thus provide further insight to consumers regarding its fitness for use

The Measurement of Quality of Semantic Standards: the Application of a Quality Model on the SETU standard for eGovernment

eGovernment interoperability should be dealt with using high-quality standards. A quality model for standards is presented based on knowledge from the software engineering domain. In the tradition of action research the model is used on the SETU standard, a standard that is mandatory in the public sector of the Netherlands in order to achieve eGovernment interoperability. This results in improvement suggestions for the SETU standards, just as improvement suggestions for the quality model have been identified. Most importantly it shows that a quality model can be used for several purposes, including selecting standards for eGovernment interoperability

A new paradigm for teacher education: supported, open teaching and learning at the Open University

In this paper we draw on our experience over the last twelve years with three large scale distance education programmes for UK teachers to suggest factors which need to be considered by those embarking on large scale distance learning teacher education programmes. We focus on three programmes: a pre-service programme in initial teacher education, the Postgraduate Certificate in Education (PGCE); and two in-service programmes, the Learning Schools Programme (LSP) and TeachandLearn.net. which have made been significant in promoting access, entitlement and diversity. We suggest that in each case the programme structure and design was influenced by the interplay of a number of factors: the nature of teacher professionalism; current policies and priorities; financial constructs; technological tools and the regulatory framework. A number of themes emerge from analysis of participant data together with evaluation evidence back from institutions and individuals participating in these programmes. These can be identified as: (1) linear versus modular structures; (2) the importance of broking between the university and the school settings; (3) interactions of programme elements; (4) the role played by contemporary forms of ICTs. We draw together our experiences and research data for these programmes to suggest characteristics of the next generation of teacher education programmes