Semantic Retrieval and Automatic Annotation: Linear Transformations, Correlation and Semantic Spaces

This paper proposes a new technique for auto-annotation and semantic retrieval based upon the idea of linearly mapping an image feature space to a keyword space. The new technique is compared to several related techniques, and a number of salient points about each of the techniques are discussed and contrasted. The paper also discusses how these techniques might actually scale to a real-world retrieval problem, and demonstrates this though a case study of a semantic retrieval technique being used on a real-world data-set (with a mix of annotated and unannotated images) from a picture library

Ontology-Based Semantic Retrieval for Education Management Systems

The traditional information retrieval technologies are based on keywords, and therefore provide limited capabilities to capture the conceptualizations associated with user needs and contents. As a new technology of information retrieval, semantic retrieval can retrieve information resource fully and precisely based on the knowledge understanding and knowledge reasoning. Ontology, which can well represent and reason about the domain knowledge, is proved to be very useful in the semantic retrieval. On this basis, in this paper, we propose a complete ontology-based semantic retrieval approach and framework for education management system. Firstly, we present some rules for constructing domain ontology from the education management system; Then, a semantic annotation method of the constructed ontology is given; Further, the ontologybased semantic retrieval algorithmis proposed; Finally, a complete framework is developed and some experiments are done. Conducted experiments show that our semantic retrieval model obtained comparable and better performance results than the traditional information retrieval technology for education management system

Semantic spaces revisited: investigating the performance of auto-annotation and semantic retrieval using semantic spaces

Semantic spaces encode similarity relationships between objects as a function of position in a mathematical space. This paper discusses three different formulations for building semantic spaces which allow the automatic-annotation and semantic retrieval of images. The models discussed in this paper require that the image content be described in the form of a series of visual-terms, rather than as a continuous feature-vector. The paper also discusses how these term-based models compare to the latest state-of-the-art continuous feature models for auto-annotation and retrieval

Realization of Semantic Atom Blog

Web blog is used as a collaborative platform to publish and share information. The information accumulated in the blog intrinsically contains the knowledge. The knowledge shared by the community of people has intangible value proposition. The blog is viewed as a multimedia information resource available on the Internet. In a blog, information in the form of text, image, audio and video builds up exponentially. The multimedia information contained in an Atom blog does not have the capability, which is required by the software processes so that Atom blog content can be accessed, processed and reused over the Internet. This shortcoming is addressed by exploring OWL knowledge modeling, semantic annotation and semantic categorization techniques in an Atom blog sphere. By adopting these techniques, futuristic Atom blogs can be created and deployed over the Internet

ERP evidence suggests executive dysfunction in ecstasy polydrug users

Background: Deficits in executive functions such as access to semantic/long-term memory have been shown in ecstasy users in previous research. Equally, there have been many reports of equivocal findings in this area. The current study sought to further investigate behavioural and electro-physiological measures of this executive function in ecstasy users. Method: Twenty ecstasy–polydrug users, 20 non-ecstasy–polydrug users and 20 drug-naïve controls were recruited. Participants completed background questionnaires about their drug use, sleep quality, fluid intelligence and mood state. Each individual also completed a semantic retrieval task whilst 64 channel Electroencephalography (EEG) measures were recorded. Results: Analysis of Variance (ANOVA) revealed no between-group differences in behavioural performance on the task. Mixed ANOVA on event-related potential (ERP) components P2, N2 and P3 revealed significant between-group differences in the N2 component. Subsequent exploratory univariate ANOVAs on the N2 component revealed marginally significant between-group differences, generally showing greater negativity at occipito-parietal electrodes in ecstasy users compared to drug-naïve controls. Despite absence of behavioural differences, differences in N2 magnitude are evidence of abnormal executive functioning in ecstasy–polydrug users

Retrieval from memory: Vulnerable or inviolable?

We show that retrieval from semantic memory is vulnerable even to the mere presence of speech. Irrelevant speech impairs semantic fluency—namely, lexical retrieval cued by a semantic category name—but only if it is meaningful (forward speech compared to reversed speech or words compared to nonwords). Moreover, speech related semantically to the retrieval category is more disruptive than unrelated speech. That phonemic fluency—in which participants are cued with the first letter of words they are to report—was not disrupted by the mere presence of meaningful speech, only by speech in a related phonemic category, suggests that distraction is not mediated by executive processing load. The pattern of sensitivity to different properties of sound as a function of the type of retrieval cue is in line with an interference-by-process approach to auditory distraction