Semantic caching for multiresolution spatial query processing in mobile environments

Spatial data are particularly useful in mobile environments. However, due to the low bandwidth of most wireless networks, developing large spatial database applications becomes a challenging process. In this paper, we provide the first attempt to combine two important techniques, multiresolution spatial data structure and semantic caching, towards efficient spatial query processing in mobile environments. Based on the study of the characteristics of multiresolution spatial data (MSD) and multiresolution spatial query, we propose a new semantic caching model called Multiresolution Semantic Caching (MSC) for caching MSD in mobile environments. MSC enriches the traditional three-category query processing in semantic cache to five categories, thus improving the performance in three ways: 1) a reduction in the amount and complexity of the remainder queries; 2) the redundant transmission of spatial data already residing in a cache is avoided; 3) a provision for satisfactory answers before 100% query results have been transmitted to the client side. Our extensive experiments on a very large and complex real spatial database show that MSC outperforms the traditional semantic caching models significantly

Dopamine and semantic activation: An investigation of masked direct and indirect priming

To investigate the effects of dopamine on the dynamics of semantic activation, 39 healthy volunteers were randomly assigned to ingest either a placebo (n = 24) or a levodopa (it = 16) capsule. Participants then performed a lexical decision task that implemented a masked priming paradigm. Direct and indirect semantic priming was measured across stimulus onset asynchronies (SOAs) of 250, 500 and 1200 ms. The results revealed significant direct and indirect semantic priming effects for the placebo group at SOAs of 250 ms and 500 ms, but no significant direct or indirect priming effects at the 1200 ms SOA. In contrast, the levodopa group showed significant direct and indirect semantic priming effects at the 250 ms SOA, while no significant direct or indirect priming effects were evident at the SOAs of 500 ins or 1200 ms. These results suggest that dopamine has a role in modulating both automatic and attentional aspects of semantic activation according to a specific time course. The implications of these results for current theories of dopaminergic modulation of semantic activation are discussed.

Maintaining and updating semantic context in schizophrenia: an investigation of the effects of multiple remote primes

Conflicting findings regarding the ability of people with schizophrenia to maintain and update semantic contexts have been due, arguably, to vagaries within the experimental design employed (e.g. whether strongly or remotely associated prime-target pairs have been used, what delay between the prime and the target was employed, and what proportion of related prime-target pairs appeared) or to characteristics of the participant cohort (e.g. medication status, chronicity of illness). The aim of the present study was to examine how people with schizophrenia maintain and update contextual information over an extended temporal window by using multiple primes that were either remotely associated or unrelated to the target. Fourteen participants with schizophrenia and 12 healthy matched controls were compared across two stimulus onset asynchronies (SOAs) (short and long) and two relatedness proportions (RP) (high and low) in a crossed design. Analysis of variance statistics revealed significant two- and three-way interactions between Group and SOA, Group and Condition, SOA and RP, and Group, SOA and RP. The participants with schizophrenia showed evidence of enhanced remote priming at the short SOA and low RP, combined with a reduction in the time course over which context could be maintained. There was some sensitivity to biasing contextual information at the short SOA, although the mechanism over which context served to update information appeared to be different from that in the controls. The participants with schizophrenia showed marked performance decrements at the long SOA (both low and high RP). Indices of remote priming at the short (but not the long) SOA correlated with both clinical ratings of thought disorder and with increasing length of illness. The results support and extend the hypothesis that schizophrenia is associated with concurrent increases in tonic dopamine activity and decreases in phasic dopamine activity. (C) 2004 Elsevier Ireland Ltd. All rights reserved.

Summation of semantic priming and complex sentence comprehension in Parkinson's disease

Research has suggested that the integrity of semantic processing may be compromised in Parkinson's disease (PD), which may account for difficulties in complex sentence comprehension. In order to investigate the time course and integrity of semantic activation in PD, 20 patients with PD and 23 healthy controls performed a lexical decision task based on the multi-priming paradigm. Semantic priming effects were measured across stimulus onset asynchronies of 250 ms, 600 ms, and 1200 ms. Further, PD participants performed an auditory comprehension task. The results revealed significantly different patterns of semantic priming for the PD group at the 250-ms and 1200-ms SOAs. In addition, a delayed time course of semantic activation was evident for PD patients with poor comprehension of complex sentences. These results provide further support to suggest that both automatic and controlled aspects of semantic activation may be compromised in PD. Furthermore, the results also suggest that some sentence comprehension deficits in PD may be related to a reduction in information processing speed.

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.

ICICLE: A semantic-based retrieval system for WWW images

In this paper, we present ICICLE (Image ChainNet and Incremental Clustering Engine), a prototype system that we have developed to efficiently and effectively retrieve WWW images based on image semantics. ICICLE has two distinguishing features. First, it employs a novel image representation model called Weight ChainNet to capture the semantics of the image content. A new formula, called list space model, for computing semantic similarities is also introduced. Second, to speed up retrieval, ICICLE employs an incremental clustering mechanism, ICC (Incremental Clustering on ChainNet), to cluster images with similar semantics into the same partition. Each cluster has a summary representative and all clusters' representatives are further summarized into a balanced and full binary tree structure. We conducted an extensive performance study to evaluate ICICLE. Compared with some recently proposed methods, our results show that ICICLE provides better recall and precision. Our clustering technique ICC facilitates speedy retrieval of images without sacrificing recall and precision significantly.

Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping

The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in an unsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks.