This paper describes an Open Linked Data backlinking service, a generic architecture component to support the discovery of useful links between items across highly connected data sets. Using Public Sector Information (PSI) currently available as Linked Data, we demonstrate that contemporary publishing practices do not adequately support the ability to navigate or automatically traverse between resources published by different vendors, or the capacity to discover information relevant to a particular URL Although some useful services in this area have been developed, such as large triple indexes of published data, and the collection of sameAs relationships between individuals, we believe that an important component is missing: a mechanism to discover the backlinks to relevant resources that cannot be found by direct URI resolution. We present the implementation of such a component, integrating data from various PSI sources.

We present a Semantic Web application that we callCS AKTive Space. The application exploits a wide range of semantically heterogeneousand distributed content relating to Computer Science research in theUK. This content is gathered on a continuous basis using a variety of methods including harvesting and scraping as well as adopting a range models for content acquisition. The content currently comprises aroundten million RDF triples and we have developed storage, retrieval andmaintenance methods to support its management. The content is mediated through an ontology constructed for the application domainand incorporates components from other published ontologies. CS AKTive Spacesupports the exploration of patterns and implications inherent in the content and exploits a variety of visualisations and multi dimensional representations. Knowledge services supported in the applicationinclude investigating communities of practice: who is working, researching or publishing with whom. This work illustrates a number ofsubstantial challenges for the Semantic Web. These include problems of referential integrity, tractable inference and interaction support. Wereview our approaches to these issues and discuss relevant related work.

A functional programming language can be taught successfully as a first language, but if there is no follow up the students do not appreciate the functional approach. Following discussions concerning this issue at the 1995 FPLE conference (Hartel & Plasmeijer, 1995), we decided to develop such a follow up by writing a book that teaches C to students who can write simple functional programs. This paper summarises the essence of our approach, which is based on program transformation, and presents our experience teaching functional C at the Universities of Southampton and Bristol.

Students often have difficulty with the minutiae of program construction. We introduce the idea of ‘Programming by Numbers’, which breaks some of the programming process down into smaller steps, giving such students a way into the process of Programming in the Small. Programming by Numbers doesnot add intellectual difficulty to learning programming, as it does not require the student to learn additional tools or theory. In fact it can be done with pencil and paper or the normal editor, and only requires the student to remember (and understand) seven simple steps. Programming by Numbers works best with languages that offer pattern matching, such as ML, or data-directed dispatching, such as Java.

Abstract The paper explores the application of a lazy functional language, Haskell, to a series of grid-based scientific problems—solution of the Poisson equation, and Monte Carlo simulation of two theoretical models from statistical and particle physics. The implementations introduce certain abstractions of grid topology, making extensive use of the polymorphic features of Haskell. Updating is expressed naturally through use of infinite lists, exploiting the laziness of the language. Evolution of systems is represented by arrays of interacting streams.

Abstract The resource constrained shortest path problem is an NP-hard problem for which many ingenious algorithms have been developed. These algorithms are usually implemented in Fortran or another imperative programming language. We have implemented some of the simpler algorithms in a lazy functional language. Benefits accrue in the software engineering of the implementations. Our implementations have been applied to a standard benchmark of data files, which is available from the Operational Research Library of Imperial College, London. The performance of the lazy functional implementations, even with the comparatively simple algorithms that we have used, is competitive with a reference Fortran implementation.

Abstract A system based on the notion of a flow graph is used to specify formally and to implement a compiler for a lazy functional language. The compiler takes a simple functional language as input and generates C. The generated C program can then be compiled, and loaded with an extensive run‐time system to provide the facility to experiment with different analysis techniques. The compiler provides a single, unified, efficient, formal framework for all the analysis and synthesis phases, including the generation of C. Many of the standard techniques, such as strictness and boxing analyses, have been included.