Getting More out of Biomedical Documents with GATE's Full Lifecycle Open Source Text Analytics.

This software article describes the GATE family of open source text analysis tools and processes. GATE is one of the most widely used systems of its type with yearly download rates of tens of thousands and many active users in both academic and industrial contexts. In this paper we report three examples of GATE-based systems operating in the life sciences and in medicine. First, in genome-wide association studies which have contributed to discovery of a head and neck cancer mutation association. Second, medical records analysis which has significantly increased the statistical power of treatment/ outcome models in the UK’s largest psychiatric patient cohort. Third, richer constructs in drug-related searching. We also explore the ways in which the GATE family supports the various stages of the lifecycle present in our examples. We conclude that the deployment of text mining for document abstraction or rich search and navigation is best thought of as a process, and that with the right computational tools and data collection strategies this process can be made defined and repeatable. The GATE research programme is now 20 years old and has grown from its roots as a specialist development tool for text processing to become a rather comprehensive ecosystem, bringing together software developers, language engineers and research staff from diverse fields. GATE now has a strong claim to cover a uniquely wide range of the lifecycle of text analysis systems. It forms a focal point for the integration and reuse of advances that have been made by many people (the majority outside of the authors’ own group) who work in text processing for biomedicine and other areas. GATE is available online ,1. under GNU open source licences and runs on all major operating systems. Support is available from an active user and developer community and also on a commercial basis

Dynamic Web Services Composition

Emerging web services technology has introduced the concept of autonomic interoperability and portability between services. The number of online services has increased dramatically with many duplicating similar functionality and results. Composing online services to solve user needs is a growing area of research. This entails designing systems which can discover participating services and integrate these according to the end user requirements. This thesis proposes a Dynamic Web Services Composition (DWSC) process that is based upon consideration of previously successful attempts in this area, in particular utilizing AI-planning based solutions. It proposes a unique approach for service selection and dynamic web service composition by exploring the possibility of semantic web usability and its limitations. It also proposes a design architecture called Optimal Synthesis Plan Generation framework (OSPG), which supports the composition process through the evaluation of all available solutions (including all participating single and composite services). OSPG is designed to take into account user preferences, which supports optimality and robustness of the output plan. The implementation of OSPG will be con�gured and tested via division of search criteria in di�erent modes thereby locating the best plan for the user. The services composition and discovery-based model is evaluated via considering a range of criteria, such as scope, correctness, scalability and versatility metrics

An Investigation into Dynamic Web Service Composition Using a Simulation Framework

[Motivation] Web Services technology has emerged as a promising solution for creat- ing distributed systems with the potential to overcome the limitation of former distrib- uted system technologies. Web services provide a platform-independent framework that enables companies to run their business services over the internet. Therefore, many techniques and tools are being developed to create business to business/business to customer applications. In particular, researchers are exploring ways to build new services from existing services by dynamically composing services from a range of resources. [Aim] This thesis aims to identify the technologies and strategies cur- rently being explored for organising the dynamic composition of Web services, and to determine how extensively each of these has been demonstrated and assessed. In addition, the thesis will study the matchmaking and selection processes which are essential processes for Web service composition. [Research Method] We under- took a mapping study of empirical papers that had been published over the period 2000 to 2009. The aim of the mapping study was to identify the technologies and strategies currently being explored for organising the composition of Web services, and to determine how extensively each of these has been demonstrated and assessed. We then built a simulation framework to carry out some experiments on composition strategies. The rst experiment compared the results of a close replication of an ex- isting study with the original results in order to evaluate our close replication study. The simulation framework was then used to investigate the use of a QoS model for supporting the selection process, comparing this with the ranking technique in terms of their performance. [Results] The mapping study found 1172 papers that matched our search terms, from which 94 were classied as providing practical demonstration of ideas related to dynamic composition. We have analysed 68 of these in more detail. Only 29 provided a `formal' empirical evaluation. From these, we selected a `baseline' study to test our simulation model. Running the experiments using simulated data- sets have shown that in the rst experiment the results of the close replication study and the original study were similar in terms of their prole. In the second experiment, the results demonstrated that the QoS model was better than the ranking mechanism in terms of selecting a composite plan that has highest quality score. [Conclusions] No one approach to service composition seemed to meet all needs, but a number has been investigated more. The similarity between the results of the close replication and the original study showed the validity of our simulation framework and a proof that the results of the original study can be replicated. Using the simulation it was demonstrated that the performance of the QoS model was better than the ranking mechanism in terms of the overall quality for a selected plan. The overall objectives of this research are to develop a generic life-cycle model for Web service composition from a mapping study of the literature. This was then used to run simulations to replicate studies on matchmaking and compare selection methods

Generic adaptation framework for unifying adaptive web-based systems

The Generic Adaptation Framework (GAF) research project first and foremost creates a common formal framework for describing current and future adaptive hypermedia (AHS) and adaptive webbased systems in general. It provides a commonly agreed upon taxonomy and a reference model that encompasses the most general architectures of the present and future, including conventional AHS, and different types of personalization-enabling systems and applications such as recommender systems (RS) personalized web search, semantic web enabled applications used in personalized information delivery, adaptive e-Learning applications and many more. At the same time GAF is trying to bring together two (seemingly not intersecting) views on the adaptation: a classical pre-authored type, with conventional domain and overlay user models and data-driven adaptation which includes a set of data mining, machine learning and information retrieval tools. To bring these research fields together we conducted a number GAF compliance studies including RS, AHS, and other applications combining adaptation, recommendation and search. We also performed a number of real systems’ case-studies to prove the point and perform a detailed analysis and evaluation of the framework. Secondly it introduces a number of new ideas in the field of AH, such as the Generic Adaptation Process (GAP) which aligns with a layered (data-oriented) architecture and serves as a reference adaptation process. This also helps to understand the compliance features mentioned earlier. Besides that GAF deals with important and novel aspects of adaptation enabling and leveraging technologies such as provenance and versioning. The existence of such a reference basis should stimulate AHS research and enable researchers to demonstrate ideas for new adaptation methods much more quickly than if they had to start from scratch. GAF will thus help bootstrap any adaptive web-based system research, design, analysis and evaluation

Semantic Rule-based Approach for Supporting Personalised Adaptive E-Learning

Instructional designers are under increasing pressure to enhance the pedagogical quality and technical richness of their learning content offerings, while the task of authoring for such complex educational frameworks is expensive and time consuming. Personalisation and reusability of learning contents are two main factors which can be used to enhance the pedagogical impact of e-learning experiences while also optimising resources, such as the overall cost and time of designing materials for different e-learning systems. However, personalisation services require continuous fine tuning for the different features that should be used, and e-learning systems need sufficient flexibility to offer these continuously required changes. The semantic modelling of adaptable learning components can highly influence the personalisation of the learning experience and enables the reusability, adaptability and maintainability of these components. Through the discrete modelling of these components, the flexibility and extensibility of e-learning systems will be improved as learning contents can be separated from the adaptation logic which results in the learning content being no longer specific to any given adaptation rule, or instructional plan. This thesis proposes an innovative semantic rule-based approach to dynamically generate personalised learning content utilising reusable pieces of learning content. It describes an ontology-based engine that composes, at runtime, adapted learning experiences according to learner’s interaction with the system and learner’s characteristics. Additionally, enriching ontologies with semantic rules increases the reasoning power and helps to represent adaptation decisions. This novel approach aims to improve flexibility, extensibility and reusability of systems, while offering a pedagogically effective and satisfactory learning experience for learners. This thesis offers the theoretical models, design and implementation of an adaptive e-learning system in accordance with this approach. It also describes the evaluation of developed personalised adaptive e-learning system (Rule-PAdel) from pedagogical and technical perspectives