Parental information needs in chronic renal failure and diabetes mellitus

The information needs of parents of children with end stage renal failure (ESRF) or with insulin dependent diabetes mellitus (IDDM) were assessed by questionnaires over a 2-year period. Questionnaires were posted on seven occasions at 4-monthly intervals and were sent to both mothers and fathers. Most information needs were reported to be for detailed test results, for new information about the condition and about the child's future social development. Questions responsible for the three highest scores were concerned with the future: the child's fertility; their social, career and marriage prospects; and the hope for a new improved treatment. For the IDDM mothers, scores were significantly different depending on age of the child (P = 0.02). Change in treatment mode had no significant effect on the information needs of parents of children with ESRF (P = 0.81). Occupation was significantly associated with the mean general information needs scores for parents, with occupations of a lower socioeconomic status associated with higher information needs scores. There were no significant differences between the reported mean general information needs scores of parents of children with ESRF and of parents of children with IDDM (P = 0.69) or between mothers and fathers mean general information needs scores (P = 0.58). CONCLUSION: Multidisciplinary team members need to tailor information to the needs of the individual families and be sensitive to socioeconomic factors and communication issues.

Exploring the rural passenger experience, information needs and decision making during public transport disruption

Acknowledgements The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Research Hub; award reference: EP/G066051/1. Further, we would like to acknowledge the RCUK research grant EP/J000604/2.

Work performed for the Course "The Reader and their information needs"

During "The reader and their information needs" students tried to implement the theory discussed in class. To this end, organized into sub-groups and worked in different types of libraries. The following paper covers the practice that the students: Jenssy Arguedas Salazar Hernández Sandoval Lidiette Oses and Olga Corrales held in the Library "Sister Onorina Leporati" Help Ma College of Alajuela.

A Knowledge-based System for Intelligent Support in Pharmacogenomics Evidence Assessment: Ontology-driven Evidence Representation, Retrieval, Classification and Interpretation

Thesis (Ph.D.)--University of Washington, 2016-06

Ontology-Based Model of Representation of Knowledge about Language Mappings

The paper presents a short review of some systems for program transformations performed on the basis of the internal intermediate representations of these programs. Many systems try to support several languages of representation of the source texts of programs and solve the task of their translation into the internal representation. This task is still a challenge as it is effort-consuming. To reduce the effort, different systems of translator construction, ready compilers with ready grammars of outside designers are used. Though this approach saves the effort, it has its drawbacks and constraints. The paper presents the general idea of using the mapping approach to solve the task within the framework of program transformations and overcome the disadvantages of the existing systems. The paper demonstrates a fragment of the ontology model of high-level languages mappings onto the single representation and gives the example of how the description of (a fragment) a particular mapping is represented in accordance with the ontology model.

Ontology-based image annotation

With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by training Support Vectors Machines with features extracted from salient objects. After that, contextual knowledge contained in ontologies is used to obtain the �nal labels by removing the ambiguity concepts. 4) Scene semantic annotation. The scene semantic extraction phase is to get the scene type by using both mid-level concepts and domain contextual knowledge in ontologies. Domain contextual knowledge is used to create scene con�guration that describes which objects co-exist with which scene type more frequently. The scene con�guration is represented in a probabilistic graph model, and probabilistic inference is employed to calculate the scene type given an annotated image. To evaluate the proposed methods, a series of experiments have been conducted in a large set of fully annotated outdoor scene images. These include a subset of the Corel database, a subset of the LabelMe dataset, the evaluation dataset of localized semantics in images, the spatial context evaluation dataset, and the segmented and annotated IAPR TC-12 benchmark.

Using a knowledge-based approach : the way healthy communities make decisions

The planning for Knowledge Cities faces significant challenges due to the lack of effective information tools. These challenges are magnified while planning healthy communities. The Australian Health Information Council (AHIC) concluded in its last report that health information needs to be shared more effectively (AHIC, 2008). Some research justifies the use of Decision Support Systems (DSS) as an E-planning tool, particularly in the context of healthy communities. However, very limited research has been conducted in this area to date, especially in terms of evaluating the impact of these tools on decision-makers within the health planning practice. The paper presents the methodological instruments which were developed to measure the impact of the E-planning tool (i.e., Health Decision Support System [HDSS])) on a group of health planners, namely, the Logan Beaudesert Health Coalition (LBHC). The paper is focused on the culture in which decisions were made before and after the intervention of the HDSS. Subsequently, the paper presents the observed impact of the HDSS tool, to facilitate a knowledge-based decision-making approach. This study is an attempt to make some contribution to the Knowledge Cities literature in the context of planning healthy communities by adopting E-planning tools.

Personalized ontology learning for enhancing text mining effectiveness

Over the last decade, the majority of existing search techniques is either keyword- based or category-based, resulting in unsatisfactory effectiveness. Meanwhile, studies have illustrated that more than 80% of users preferred personalized search results. As a result, many studies paid a great deal of efforts (referred to as col- laborative filtering) investigating on personalized notions for enhancing retrieval performance. One of the fundamental yet most challenging steps is to capture precise user information needs. Most Web users are inexperienced or lack the capability to express their needs properly, whereas the existent retrieval systems are highly sensitive to vocabulary. Researchers have increasingly proposed the utilization of ontology-based tech- niques to improve current mining approaches. The related techniques are not only able to refine search intentions among specific generic domains, but also to access new knowledge by tracking semantic relations. In recent years, some researchers have attempted to build ontological user profiles according to discovered user background knowledge. The knowledge is considered to be both global and lo- cal analyses, which aim to produce tailored ontologies by a group of concepts. However, a key problem here that has not been addressed is: how to accurately match diverse local information to universal global knowledge. This research conducts a theoretical study on the use of personalized ontolo- gies to enhance text mining performance. The objective is to understand user information needs by a \bag-of-concepts" rather than \words". The concepts are gathered from a general world knowledge base named the Library of Congress Subject Headings. To return desirable search results, a novel ontology-based mining approach is introduced to discover accurate search intentions and learn personalized ontologies as user profiles. The approach can not only pinpoint users' individual intentions in a rough hierarchical structure, but can also in- terpret their needs by a set of acknowledged concepts. Along with global and local analyses, another solid concept matching approach is carried out to address about the mismatch between local information and world knowledge. Relevance features produced by the Relevance Feature Discovery model, are determined as representatives of local information. These features have been proven as the best alternative for user queries to avoid ambiguity and consistently outperform the features extracted by other filtering models. The two attempt-to-proposed ap- proaches are both evaluated by a scientific evaluation with the standard Reuters Corpus Volume 1 testing set. A comprehensive comparison is made with a num- ber of the state-of-the art baseline models, including TF-IDF, Rocchio, Okapi BM25, the deploying Pattern Taxonomy Model, and an ontology-based model. The gathered results indicate that the top precision can be improved remarkably with the proposed ontology mining approach, where the matching approach is successful and achieves significant improvements in most information filtering measurements. This research contributes to the fields of ontological filtering, user profiling, and knowledge representation. The related outputs are critical when systems are expected to return proper mining results and provide personalized services. The scientific findings have the potential to facilitate the design of advanced preference mining models, where impact on people's daily lives.

The IKBALS project: multi-modal reasoning in legal knowledge based systems

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work integrates rule based and case based reasoning with intelligent information retrieval. When using the case based reasoning methodology, or in our case the specialisation of case based retrieval, we need to be aware of how to retrieve relevant experience. Our research, in the legal domain, specifies an approach to the retrieval problem which relies heavily on an extended object oriented/rule based system architecture that is supplemented with causal background information. We use a distributed agent architecture to help support the reasoning process of lawyers. Our approach to integrating rule based reasoning, case based reasoning and case based retrieval is contrasted to the CABARET and PROLEXS architectures which rely on a centralised blackboard architecture. We discuss in detail how our various cooperating agents interact, and provide examples of the system at work. The IKBALS system uses a specialised induction algorithm to induce rules from cases. These rules are then used as indices during the case based retrieval process. Because we aim to build legal support tools which can be modified to suit various domains rather than single purpose legal expert systems, we focus on principles behind developing legal knowledge based systems. The original domain chosen was theAccident Compensation Act 1989 (Victoria, Australia), which relates to the provision of benefits for employees injured at work. For various reasons, which are indicated in the paper, we changed our domain to that ofCredit Act 1984 (Victoria, Australia). This Act regulates the provision of loans by financial institutions. The rule based part of our system which provides advice on the Credit Act has been commercially developed in conjunction with a legal firm. We indicate how this work has lead to the development of a methodology for constructing rule based legal knowledge based systems. We explain the process of integrating this existing commercial rule based system with the case base reasoning and retrieval architecture.

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Developing Co-operating Legal Knowledge Based Systems

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work supplements rule-based reasoning with case based reasoning and intelligent information retrieval. This research, specifies an approach to the case based retrieval problem which relies heavily on an extended object-oriented / rule-based system architecture that is supplemented with causal background information. Machine learning techniques and a distributed agent architecture are used to help simulate the reasoning process of lawyers. In this paper, we outline our implementation of the hybrid IKBALS II Rule Based Reasoning / Case Based Reasoning system. It makes extensive use of an automated case representation editor and background information.

Industry based rehabilitation and closure knowledge management system : scoping study

The Central Queensland Mine Rehabilitation Group (CQMRG) has hosted mine site rehabilitation inspections combined with technical workshops for more than 20 years. It was recognised at CQMRG's anniversary meeting in April 2013 that the vast body of knowledge held by rehabilitation and closure planning practitioners was being lost as senior rehabilitation experts retire from the industry. It was noted that even more knowledge could be readily lost unless a knowledge management platform was developed to capture, store and enable retrieval of this information. This loss of knowledge results in a significant cost to industry. This project was therefore undertaken to review tools which have the capability to gather the less formal knowledge as well as to make links to existing resources and bibliographic material. This scoping study evaluated eight alternative knowledge management systems to provide guidance on the best method of providing the industry with an up-to-date, good practice, knowledge management system for rehabilitation and closure practices, with capability for information sharing via a portal and discussion forum. This project provides guidance for a larger project which will implement the knowledge management system to meet the requirements of the CQMRG and be transferrable to other regions if applicable. It will also provide the opportunity to identify missing links between existing tools and their application. That is, users may not be aware of how these existing tools can be used to assist with mine rehabilitation planning and implementation and the development of a new platform will help to create those linkages. The outcomes of this project are directed toward providing access to a live repository of rehabilitation practice information which is Central Queensland coal mine-specific, namely: highlighting best practice activities, results of trials and innovative practices; updated legislative requirements; links to practices elsewhere; and informal anecdotal information relevant to particular sites which may be of assistance in the development of rehabilitation of new areas. Solutions to the rehabilitation of challenging spoils/soils will also be provided. The project will also develop a process which can be applied more broadly within the mining sector to other regions and other commodities. Providing a platform for uploading information and holding discussion forums which can be managed by a regional practitioner network enables the new system to be kept alive, driven by users and information needs as they evolve over time. Similar internet-based platforms exist and are managed successfully. The preferred knowledge management system will capture the less formal and more difficult to access knowledge from rehabilitation and mine closure practitioners and stakeholders through the CQMRG and other contributors. It will also provide direct links, and greater accessibility, to more formal sources of knowledge with anticipated cost savings to the industry and improved rehabilitation practices with successful transitioning to closure and post-mining land use.