An approach to ontology for institutional facts in the semantic web

Refinement in software engineering allows a specification to be developed in stages, with design decisions taken at earlier stages constraining the design at later stages. Refinement in complex data models is difficult due to lack of a way of defining constraints, which can be progressively maintained over increasingly detailed refinements. Category theory provides a way of stating wide scale constraints. These constraints lead to a set of design guidelines, which maintain the wide scale constraints under increasing detail. Previous methods of refinement are essentially local, and the proposed method does not interfere very much with these local methods. The result is particularly applicable to semantic web applications, where ontologies provide systems of more or less abstract constraints on systems, which must be implemented and therefore refined by participating systems. With the approach of this paper, the concept of committing to an ontology carries much more force. (c) 2005 Elsevier B.V. All rights reserved.

Optimizing the data intensive mediator-based Web services composition

Effectively using heterogeneous, distributed information has attracted much research in recent years. Current web services technologies have been used successfully in some non data intensive distributed prototype systems. However, most of them can not work well in data intensive environment. This paper provides an infrastructure layer in data intensive environment for the effectively providing spatial information services by using the web services over the Internet. We extensively investigate and analyze the overhead of web services in data intensive environment, and propose some new optimization techniques which can greatly increase the system’s efficiency. Our experiments show that these techniques are suitable to data intensive environment. Finally, we present the requirement of these techniques for the information of web services over the Internet.

The influence of dopamine on semantic activation in Parkinson's disease: Evidence from a multipriming task

Research has suggested that semantic processing deficits in Parkinson's disease (PD) are related to striatal dopamine deficiency. As an investigation of the influence of dopamine on semantic activation in PD, 7 participants with PD performed a lexical-decision task when on and off levodopa medication. Seven healthy controls matched to the participants with PD in terms of sex, age, and education also participated in the study. By use of a multipriming paradigm, whereby 2 prime words were presented prior to the target word, semantic priming effects were measured across stimulus onset asynchronies (SOAs) of 250 Ins and 1,200 Ins. The results revealed a similar pattern of priming across SOAs for the control group and the PD participants on medication. In contrast, within-group comparisons revealed that automatic semantic activation was compromised in PD participants when off medication. The implications of these results for the neuromodulatory influence of dopamine on semantic processing in PD are discussed.

Proof Explanation in the DR-DEVICE System

Trust is a vital feature for Semantic Web: If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs, and this issue is the topic of the proof layer in the design of the Semantic Web. This paper presents the design and implementation of a system for proof explanation on the Semantic Web, based on defeasible reasoning. The basis of this work is the DR-DEVICE system that is extended to handle proofs. A critical aspect is the representation of proofs in an XML language, which is achieved by a RuleML language extension.

Model-based clustering in gene expression microarrays: An application to breast cancer data

In microarray studies, the application of clustering techniques is often used to derive meaningful insights into the data. In the past, hierarchical methods have been the primary clustering tool employed to perform this task. The hierarchical algorithms have been mainly applied heuristically to these cluster analysis problems. Further, a major limitation of these methods is their inability to determine the number of clusters. Thus there is a need for a model-based approach to these. clustering problems. To this end, McLachlan et al. [7] developed a mixture model-based algorithm (EMMIX-GENE) for the clustering of tissue samples. To further investigate the EMMIX-GENE procedure as a model-based -approach, we present a case study involving the application of EMMIX-GENE to the breast cancer data as studied recently in van 't Veer et al. [10]. Our analysis considers the problem of clustering the tissue samples on the basis of the genes which is a non-standard problem because the number of genes greatly exceed the number of tissue samples. We demonstrate how EMMIX-GENE can be useful in reducing the initial set of genes down to a more computationally manageable size. The results from this analysis also emphasise the difficulty associated with the task of separating two tissue groups on the basis of a particular subset of genes. These results also shed light on why supervised methods have such a high misallocation error rate for the breast cancer data.

Brain morphology in large pelagic fishes: A comparison between sharks and teleosts

A quantitative comparison was made of both relative brain size (encephalization) and the relative development of five brain area of pelagic sharks and teleosts. Two integration areas (the telencephalon and the corpus cerebellum) and three sensory brain areas (the olfactory bulbs, optic tectum and octavolateralis area, which receive primary projections from the olfactory epithelium, eye and octavolateralis senses, respectively), in four species of pelagic shark and six species of pelagic teleost were investigated. The relative proportions of the three sensory brain areas were assessed as a proportion of the total 'sensory brain', while the two integration areas were assessed relative to the sensory brain. The allometric analysis of relative brain size revealed that pelagic sharks had larger brains than pelagic teleosts. The volume of the telencephalon was significantly larger in the sharks, while the corpus cerebellum was also larger and more heavily foliated in these animals. There were also significant differences in the relative development of the sensory brain areas between the two groups, with the sharks having larger olfactory bulbs and octavolateralis areas, whilst the teleosts had larger optic tecta. Cluster analysis performed on the sensory brain areas data confirmed the differences in the composition of the sensory brain in sharks and teleosts and indicated that these two groups of pelagic fishes had evolved different sensory strategies to cope with the demands of life in the open ocean.

Reflective web interface agent

Web interface agent is used with web browsers to assist users in searching and interactions with the WWW. It is used for a variety of purposes, such as web-enabled remote control, web interactive visualization, and e-commerce activities. User may be aware or unaware of its existence. The intelligence of interface agent consists in its capability of learning and decision-making in performing interactive functions on behalf of a user. However, since web is an open system environment, the reasoning mechanism in an agent should be able to adapt changes and make decisions on exceptional situations, and therefore use meta knowledge. This paper proposes a framework of Reflective Web Interface Agent (RWIA) that is to provide causal connections between the application interfaces and the knowledge model of the interface agent. A prototype is also implemented for the purpose of demonstration.

Can and may: Monosemy or polysemy?

This paper argues, on the basis of a corpus-based study of the meanings of can and may in contemporary British, American and Australian English, that a polysemy-based analysis is applicable to both modals. With may, epistemic possibility is the dominant meaning, but the dynamic and deontic possibility meanings still account for over 16.5% of tokens. By contrast the meanings of can, apart from a small percentage (1.1%) of epistemic cases, are united through the concept of potentiality. Nevertheless there are signs that the epistemic possibility meaning is becoming established, as it sheds its syntactic/semantic restriction to non-affirmative contexts.

Equational Reasoning as a Tool for Data Analysis

A combination of deductive reasoning, clustering, and inductive learning is given as an example of a hybrid system for exploratory data analysis. Visualization is replaced by a dialogue with the data.

Data analysis in multiple-frequency bioelectrical impedance analysis

The performance of three analytical methods for multiple-frequency bioelectrical impedance analysis (MFBIA) data was assessed. The methods were the established method of Cole and Cole, the newly proposed method of Siconolfi and co-workers and a modification of this procedure. Method performance was assessed from the adequacy of the curve fitting techniques, as judged by the correlation coefficient and standard error of the estimate, and the accuracy of the different methods in determining the theoretical values of impedance parameters describing a set of model electrical circuits. The experimental data were well fitted by all curve-fitting procedures (r = 0.9 with SEE 0.3 to 3.5% or better for most circuit-procedure combinations). Cole-Cole modelling provided the most accurate estimates of circuit impedance values, generally within 1-2% of the theoretical values, followed by the Siconolfi procedure using a sixth-order polynomial regression (1-6% variation). None of the methods, however, accurately estimated circuit parameters when the measured impedances were low (<20 Omega) reflecting the electronic limits of the impedance meter used. These data suggest that Cole-Cole modelling remains the preferred method for the analysis of MFBIA data.

Sensitivity of multiple frequency bioelectrical impedance analysis to changes in ion status

Bioelectrical impedance analysis has found extensive application as a simple noninvasive method for the assessment of body fluid volumes, The measured impedance is, however, not only related to the volume of fluid but also to its inherent resistivity. The primary determinant of the resistivities of body fluids is the concentration of ions. The aim of this study was to investigate the sensitivity of bioelectrical impedance analysis to bodily ion status. Whole body impedance over a range of frequencies (4-1012 kHz) of rats was measured during infusion of various concentrations of saline into rats concomitant with measurement of total body and intracellular water by tracer dilution techniques. Extracellular resistance (R-o), intracellular resistance (R-i) and impedance at the characteristic frequency (Z(c)) were calculated. R-o and Z(c) were used to predict extracellular and total body water respectively using previously published formulae. The results showed that whilst R-o and Z(c) decreased proportionately to the amount of NaCl infused, R-i increased only slightly. Impedances at the end of infusion predicted increases iu TBW and ECW of approximately 4-6% despite a volume increase of less than 0.5% in TBW due to the volume of fluid infused. These data are discussed in relation to the assumption of constant resistivity in the prediction of fluid volumes from impedance data.