How quantum theory is developing the field of information retrieval

This position paper provides an overview of work conducted and an outlook of future directions within the field of Information Retrieval (IR) that aims to develop novel models, methods and frameworks inspired by Quantum Theory (QT).

An information accountability framework for shared eHealth policies

Privacy issues have hindered the evolution of e-health since its emergence. Patients demand better solutions for the protection of private information. Health professionals demand open access to patient health records. Existing e-health systems find it difficult to fulfill these competing requirements. In this paper, we present an information accountability framework (IAF) for e-health systems. The IAF is intended to address privacy issues and their competing concerns related to e-health. Capabilities of the IAF adhere to information accountability principles and e-health requirements. Policy representation and policy reasoning are key capabilities introduced in the IAF. We investigate how these capabilities are feasible using Semantic Web technologies. We discuss with the use of a case scenario, how we can represent the different types of policies in the IAF using the Open Digital Rights Language (ODRL).

Semantics-based web service discovery using information retrieval techniques

This paper demonstrates an experimental study that examines the accuracy of various information retrieval techniques for Web service discovery. The main goal of this research is to evaluate algorithms for semantic web service discovery. The evaluation is comprehensively benchmarked using more than 1,700 real-world WSDL documents from INEX 2010 Web Service Discovery Track dataset. For automatic search, we successfully use Latent Semantic Analysis and BM25 to perform Web service discovery. Moreover, we provide linking analysis which automatically links possible atomic Web services to meet the complex requirements of users. Our fusion engine recommends a final result to users. Our experiments show that linking analysis can improve the overall performance of Web service discovery. We also find that keyword-based search can quickly return results but it has limitation of understanding users’ goals.

A requirements framework for semantic business process modelling

The emergence of semantic technologies to deal with the underlying meaning of things, instead of a purely syntactical representation, has led to new developments in various fields, including business process modeling. Inspired by artificial intelligence research, technologies for semantic Web services have been proposed and extended to process modeling. However, the applicablility of semantic Web services for semantic business processes is limited because business processes encompass wider requirements of business than Web services. In particular, processes are concerned with the composition of tasks, that is, in which order activities are carried out, regardless of their implementation details; resources assigned to carry out tasks, such as machinery, people, and goods; data exchange; and security and compliance concerns.

Effects of enhanced somatosensory information on postural stability in older people and people with Parkinson’s disease

The somatosensory system plays an important role in balance control and age-related changes to this system have been implicated in falls. Parkinson’s disease (PD) is a chronic and progressive disease of the brain, characterized by postural instability and gait disturbance. Previous research has shown that deficiencies in somatosensory feedback may contribute to the poorer postural control demonstrated by PD individuals. However, few studies have comprehensively explored differences in somatosensory function and postural control between PD participants and healthy older individuals. The soles of the feet contain many cutaneous mechanoreceptors that provide important somatosensory information sources for postural control. Different types of insole devices have been developed to enhance this somatosensory information and improve postural stability, but these devices are often too complex and expensive to integrate into daily life. Textured insoles provide a more passive intervention that may be an inexpensive and accessible means to enhance the somatosensory input from the plantar surface of the feet. However, to date, there has been little work conducted to test the efficacy of enhanced somatosensory input induced by textured insoles in both healthy and PD populations during standing and walking. Therefore, the aims of this thesis were to determine: 1) whether textured insole surfaces can improve postural stability by enhancing somatosensory information in younger and older adults, 2) the differences between healthy older participants and PD participants for measures of physiological function and postural stability during standing and walking, 3) how changes in somatosensory information affect postural stability in both groups during standing and walking; and 4), whether textured insoles can improve postural stability in both groups during standing and walking. To address these aims, Study 1 recruited seven older individuals and ten healthy young controls to investigate the effects of two textured insole surfaces on postural stability while performing standing balance tests on a force plate. Participants were tested under three insole surface conditions: 1) barefoot; 2) standing on a hard textured insole surface; and 3), standing on a soft textured insole surface. Measurements derived from the centre of pressure displacement included the range of anterior-posterior and medial-lateral displacement, path length and the 90% confidence elliptical area (C90 area). Results of study 1 revealed a significant Group*Surface*Insole interaction for the four measures. Both textured insole surfaces reduced postural sway for the older group, especially in the eyes closed condition on the foam surface. However, participants reported that the soft textured insole surface was more comfortable and, hence, the soft textured insoles were adopted for Studies 2 and 3. For Study 2, 20 healthy older adults (controls) and 20 participants with Parkinson’s disease were recruited. Participants were evaluated using a series of physiological assessments that included touch sensitivity, vibratory perception, and pain and temperature threshold detection. Furthermore, nerve function and somatosensory evoked potentials tests were utilized to provide detailed information regarding peripheral nerve function for these participants. Standing balance and walking were assessed on different surfaces using a force plate and the 3D Vicon motion analysis system, respectively. Data derived from the force plate included the range of anterior-posterior and medial-lateral sway, while measures of stride length, stride period, cadence, double support time, stance phase, velocity and stride timing variability were reported for the walking assessment. The results of this study demonstrated that the PD group had decrements in somatosensory function compared to the healthy older control group. For electrodiagnosis, PD participants had poorer nerve function than controls, as evidenced by slower nerve conduction velocities and longer latencies in sural nerve and prolonged latency in the P37 somatosensory evoked potential. Furthermore, the PD group displayed more postural sway in both the anterior-posterior and medial-lateral directions relative to controls and these differences were increased when standing on a foam surface. With respect to the gait assessment, the PD group took shorter strides and had a reduced stride period compared with the control group. Furthermore, the PD group spent more time in the stance phase and had increased cadence and stride timing variability than the controls. Compared with walking on the firm surface, the two groups demonstrated different gait adaptations while walking on the uneven surface. Controls increased their stride length and stride period and decreased their cadence, which resulted in a consistent walking velocity on both surfaces. Conversely, while the PD patients also increased their stride period and decreased their cadence and stance period on the uneven surface, they did not increase their stride length and, hence walked slower on the uneven surface. In the PD group, there was a strong positive association between decreased somatosensory function and decreased clinical balance, as assessed by the Tinetti test. Poorer somatosensory function was also strongly positively correlated with the temporospatial gait parameters, especially shorter stride length. Study 3 evaluated the effects of manipulating the somatosensory information from the plantar surface of the feet using textured insoles in the same populations assessed in Study 2. For this study, participants performed the standing and walking balance tests under three footwear conditions: 1) barefoot; 2) with smooth insoles; and 3), with textured insoles. Standing balance and walking were evaluated using a force plate and a Vicon motion analysis system and the data were analysed in the same way outlined for Study 2. The findings showed that the smooth and textured insoles caused different effects on postural control during both the standing and walking trials. Both insoles decreased medial-lateral sway to the same level on the firm surface. The greatest benefits were observed in the PD group while wearing the textured insole. When standing under a more challenging condition on the foam surface with eyes closed, only the textured insole decreased medial-lateral sway in the PD group. With respect to the gait trials, both insoles increased walking velocity, stride length and stride time and decreased cadence, but these changes were more pronounced for the textured insoles. The effects of the textured insoles were evident under challenging conditions in the PD group and increased walking velocity and stride length, while decreasing cadence. Textured insoles were also effective in reducing the time spent in the double support and stance phases of the gait cycle and did not increase stride timing variability, as was the case for the smooth insoles for the PD group. The results of this study suggest that textured insoles, such as those evaluated in this research, may provide a low-cost means of improving postural stability in high-risk groups, such as people with PD, which may act as an important intervention to prevent falls.

A tensor encoding model for semantic processing

This paper develops and evaluates an enhanced corpus based approach for semantic processing. Corpus based models that build representations of words directly from text do not require pre-existing linguistic knowledge, and have demonstrated psychologically relevant performance on a number of cognitive tasks. However, they have been criticised in the past for not incorporating sufficient structural information. Using ideas underpinning recent attempts to overcome this weakness, we develop an enhanced tensor encoding model to build representations of word meaning for semantic processing. Our enhanced model demonstrates superior performance when compared to a robust baseline model on a number of semantic processing tasks.

Real, complex, and binary semantic vectors

This paper presents a combined structure for using real, complex, and binary valued vectors for semantic representation. The theory, implementation, and application of this structure are all significant. For the theory underlying quantum interaction, it is important to develop a core set of mathematical operators that describe systems of information, just as core mathematical operators in quantum mechanics are used to describe the behavior of physical systems. The system described in this paper enables us to compare more traditional quantum mechanical models (which use complex state vectors), alongside more generalized quantum models that use real and binary vectors. The implementation of such a system presents fundamental computational challenges. For large and sometimes sparse datasets, the demands on time and space are different for real, complex, and binary vectors. To accommodate these demands, the Semantic Vectors package has been carefully adapted and can now switch between different number types comparatively seamlessly. This paper describes the key abstract operations in our semantic vector models, and describes the implementations for real, complex, and binary vectors. We also discuss some of the key questions that arise in the field of quantum interaction and informatics, explaining how the wide availability of modelling options for different number fields will help to investigate some of these questions.

Empirical analysis of the effect of dimension reduction and word order on semantic vectors

The aim of this paper is to provide a comparison of various algorithms and parameters to build reduced semantic spaces. The effect of dimension reduction, the stability of the representation and the effect of word order are examined in the context of the five algorithms bearing on semantic vectors: Random projection (RP), singular value decom- position (SVD), non-negative matrix factorization (NMF), permutations and holographic reduced representations (HRR). The quality of semantic representation was tested by means of synonym finding task using the TOEFL test on the TASA corpus. Dimension reduction was found to improve the quality of semantic representation but it is hard to find the optimal parameter settings. Even though dimension reduction by RP was found to be more generally applicable than SVD, the semantic vectors produced by RP are somewhat unstable. The effect of encoding word order into the semantic vector representation via HRR did not lead to any increase in scores over vectors constructed from word co-occurrence in context information. In this regard, very small context windows resulted in better semantic vectors for the TOEFL test.

How activation, entanglement, and searching a semantic network contribute to event memory

Free association norms indicate that words are organized into semantic/associative neighborhoods within a larger network of words and links that bind the net together. We present evidence indicating that memory for a recent word event can depend on implicitly and simultaneously activating related words in its neighborhood. Processing a word during encoding primes its network representation as a function of the density of the links in its neighborhood. Such priming increases recall and recognition and can have long lasting effects when the word is processed in working memory. Evidence for this phenomenon is reviewed in extralist cuing, primed free association, intralist cuing, and single-item recognition tasks. The findings also show that when a related word is presented to cue the recall of a studied word, the cue activates it in an array of related words that distract and reduce the probability of its selection. The activation of the semantic network produces priming benefits during encoding and search costs during retrieval. In extralist cuing recall is a negative function of cue-to-distracter strength and a positive function of neighborhood density, cue-to-target strength, and target-to cue strength. We show how four measures derived from the network can be combined and used to predict memory performance. These measures play different roles in different tasks indicating that the contribution of the semantic network varies with the context provided by the task. We evaluate spreading activation and quantum-like entanglement explanations for the priming effect produced by neighborhood density.

Modelling word activation in semantic networks : three scaled entanglement models compared

Modelling how a word is activated in human memory is an important requirement for determining the probability of recall of a word in an extra-list cueing experiment. Previous research assumed a quantum-like model in which the semantic network was modelled as entangled qubits, however the level of activation was clearly being over-estimated. This paper explores three variations of this model, each of which are distinguished by a scaling factor designed to compensate the overestimation.

A tensor encoding model of word meaning : theory and application to information retrieval

This project was a step forward in developing and evaluating a novel, mathematical model that can deduce the meaning of words based on their use in language. This model can be applied to a wide range of natural language applications, including the information seeking process most of us undertake on a daily basis.

Discovering interesting information with advances in web technology

The Web is a steadily evolving resource comprising much more than mere HTML pages. With its ever-growing data sources in a variety of formats, it provides great potential for knowledge discovery. In this article, we shed light on some interesting phenomena of the Web: the deep Web, which surfaces database records as Web pages; the Semantic Web, which de�nes meaningful data exchange formats; XML, which has established itself as a lingua franca for Web data exchange; and domain-speci�c markup languages, which are designed based on XML syntax with the goal of preserving semantics in targeted domains. We detail these four developments in Web technology, and explain how they can be used for data mining. Our goal is to show that all these areas can be as useful for knowledge discovery as the HTML-based part of the Web.

Pattern-based topic models for information filtering

Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, which has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering is rarely known. Patterns are always thought to be more representative than single terms for representing documents. In this paper, a novel information filtering model, Pattern-based Topic Model(PBTM) , is proposed to represent the text documents not only using the topic distributions at general level but also using semantic pattern representations at detailed specific level, both of which contribute to the accurate document representation and document relevance ranking. Extensive experiments are conducted to evaluate the effectiveness of PBTM by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model achieves outstanding performance.