Knowledge Organization Systems (KOS) in the Semantic Web: A Multi-Dimensional Review

Since the Simple Knowledge Organization System (SKOS) specification and its SKOS eXtension for Labels (SKOS-XL) became formal W3C recommendations in 2009 a significant number of conventional knowledge organization systems (KOS) (including thesauri, classification schemes, name authorities, and lists of codes and terms, produced before the arrival of the ontology-wave) have made their journeys to join the Semantic Web mainstream. This paper uses "LOD KOS" as an umbrella term to refer to all of the value vocabularies and lightweight ontologies within the Semantic Web framework. The paper provides an overview of what the LOD KOS movement has brought to various communities and users. These are not limited to the colonies of the value vocabulary constructors and providers, nor the catalogers and indexers who have a long history of applying the vocabularies to their products. The LOD dataset producers and LOD service providers, the information architects and interface designers, and researchers in sciences and humanities, are also direct beneficiaries of LOD KOS. The paper examines a set of the collected cases (experimental or in real applications) and aims to find the usages of LOD KOS in order to share the practices and ideas among communities and users. Through the viewpoints of a number of different user groups, the functions of LOD KOS are examined from multiple dimensions. This paper focuses on the LOD dataset producers, vocabulary producers, and researchers (as end-users of KOS).Comment: 31 pages, 12 figures, accepted paper in International Journal on Digital Librarie

Every student counts: promoting numeracy and enhancing employability

This three-year project investigated factors that influence the development of undergraduates’ numeracy skills, with a view to identifying ways to improve them and thereby enhance student employability. Its aims and objectives were to ascertain: the generic numeracy skills in which employers expect their graduate recruits to be competent and the extent to which employers are using numeracy tests as part of graduate recruitment processes; the numeracy skills developed within a diversity of academic disciplines; the prevalence of factors that influence undergraduates’ development of their numeracy skills; how the development of numeracy skills might be better supported within undergraduate curricula; and the extra-curricular support necessary to enhance undergraduates’ numeracy skills

Quantifying the impact and relevance of scientific research

Qualitative and quantitative methods are being developed to measure the impacts of research on society, but they suffer from serious drawbacks associated with linking a piece of research to its subsequent impacts. We have developed a method to derive impact scores for individual research publications according to their contribution to answering questions of quantified importance to end users of research. To demonstrate the approach, here we evaluate the impacts of research into means of conserving wild bee populations in the UK. For published papers, there is a weak positive correlation between our impact score and the impact factor of the journal. The process identifies publications that provide high quality evidence relating to issues of strong concern. It can also be used to set future research agendas

Enhancing the Communication and Speaking Skills of Mathematics Undergraduates

In June 2011, the University of Lancaster delivered a substantially-enhanced course in Communication and Presentation Skills to 108 second-year undergraduate mathematicians. The course was delivered jointly by staff in the Department of Mathematics and Statistics and CETAD, the Centre for Training and Development at Lancaster. Funding for the course and its increased staffing requirement came from an MSOR HE Curriculum Innovation Fund grant of £5,000. CETAD is a specialist unit which focuses on providing training programmes in the North West of England. This project was the first time that CETAD had worked with mathematics undergraduates. Students were divided into 24 small groups. During the course, students prepared and delivered two group presentations, the first for formative assessment and the second for summative assessment. The final session focused on a codebreaking exercise. Feedback to students on their formative and summative assessments was given by a group of peers and by tutors. Participants were encouraged to reflect on their performances and their feedback, identifying development points for them to work on. The response from students was very encouraging

Operationalizing Individual Fairness with Pairwise Fair Representations

We revisit the notion of individual fairness proposed by Dwork et al. A central challenge in operationalizing their approach is the difficulty in eliciting a human specification of a similarity metric. In this paper, we propose an operationalization of individual fairness that does not rely on a human specification of a distance metric. Instead, we propose novel approaches to elicit and leverage side-information on equally deserving individuals to counter subordination between social groups. We model this knowledge as a fairness graph, and learn a unified Pairwise Fair Representation (PFR) of the data that captures both data-driven similarity between individuals and the pairwise side-information in fairness graph. We elicit fairness judgments from a variety of sources, including human judgments for two real-world datasets on recidivism prediction (COMPAS) and violent neighborhood prediction (Crime & Communities). Our experiments show that the PFR model for operationalizing individual fairness is practically viable.Comment: To be published in the proceedings of the VLDB Endowment, Vol. 13, Issue.

Mapping Current and Potential Sources of Routine Data Capture on New Psychoactive Substances in Scotland

The paper maps the data currently being captured on NPS, and provides a starting point for exploring the strengths and weaknesses of a number of existing data systems in Scotland, and opportunities for data sharing

Ultra-Scalable Spectral Clustering and Ensemble Clustering

This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representative selection strategy and a fast approximation method for K-nearest representatives are proposed for the construction of a sparse affinity sub-matrix. By interpreting the sparse sub-matrix as a bipartite graph, the transfer cut is then utilized to efficiently partition the graph and obtain the clustering result. In U-SENC, multiple U-SPEC clusterers are further integrated into an ensemble clustering framework to enhance the robustness of U-SPEC while maintaining high efficiency. Based on the ensemble generation via multiple U-SEPC's, a new bipartite graph is constructed between objects and base clusters and then efficiently partitioned to achieve the consensus clustering result. It is noteworthy that both U-SPEC and U-SENC have nearly linear time and space complexity, and are capable of robustly and efficiently partitioning ten-million-level nonlinearly-separable datasets on a PC with 64GB memory. Experiments on various large-scale datasets have demonstrated the scalability and robustness of our algorithms. The MATLAB code and experimental data are available at https://www.researchgate.net/publication/330760669.Comment: To appear in IEEE Transactions on Knowledge and Data Engineering, 201