Dynamic Document Generation from Semantic Web Data

This talk will present an overview of the ongoing ERCIM project SMARTDOCS (SeMAntically-cReaTed DOCuments) which aims at automatically generating webpages from RDF data. It will particularly focus on the current issues and the investigated solutions in the different modules of the project, which are related to document planning, natural language generation and multimedia perspectives. The second part of the talk will be dedicated to the KODA annotation system, which is a knowledge-base-agnostic annotator designed to provide the RDF annotations required in the document generation process.

COMP6215 Semantic Web

Semantic Web Class 2016 by Nick Gibbins and Steffen Staab

Embedding Semantic Web Data

RDFa JSON-LD Microdata

Programming with Semantic Broad Data

Presentation at WAIS Away Day, April 2016

Contribution from neuropsychology to the study of semantic memory

Semantic memory has been studied from various fields. The first models emerged from cognitive psychology from the hand of the division proposed by Tulving between semantic and episodic memory. Over the past thirty years there have been parallel developments in the fields of psycholinguistics, cognitive psychology and cognitive neuropsychology. The present work is to review the contributions that have emerged within the neuropsychology to the study of semantic memory and to present an updated overview of the points of consensus. First, it is defined the term "semantics" conceptually within the field of neuropsychology. Then, there is a dichotomy that passes through both psychological and neuropsychological models on semantic memory: the existence of modals versus amodal representations. Third, there are  developed the main theoretical models in neuropsychology that emerged in an attempt to explain categoryspecific semantic deficits. Finally, more robust contributions and points that still generate some discussion are reviewed.

Realizing interoperability of e-learning repositories.

Esta investigación tiene como objetivo contribuir a mejorar la recuperación de información en la web relacionada con los sistemas de aprendizaje en línea.. Se proporciona una revisión del estado de la cuestión del área de interoperabilidad en sistemas distribuidos enfocados parcialmente al aprendizaje. Se detallan, tanto la motivación para el trabajo en interoperabilidad y su necesidad desde el punto de vista del consumidor y proveedor de información, como los diferentes componentes necesarios para garantizarla.. Este trabajo contribuye a mejorar la interoperabilidad en sistemas de gestión de aprendizaje en línea y facilita medios necesarios para conseguirlo: un lenguaje de búsqueda común, un vocabulario global, integración semántica y ranking. También se ofrecen soluciones para la mejora de la interoperabilidad de estas aplicaciones, facilitando su efectividad desde el punto de vista del consumidor y proveedor de información..

Is semantic impairment a predictive sign for Alzheimer disease?

Resumen basado en el de la publicaci??n

The effects of phonological and semantic priming in speech production

This paper is a review of experiments to investigate the influence of experimental task, level of processing, and time course in speech production via the priming paradigm.

Interoperability for eLearning Service Management and Provision

Learning Objects offer flexibility and adaptability for users to request personalised information for learning. There are standards to guide the development of learning objects. However, individual developers may customise these standards for serving different purposes when defining, describing, managing and providing learning objects, which are normally stored in heterogeneous repositories. Barriers to interoperability hinder sharing of learning services and subsequently affect quality of instructional design as learners expect to be able to receive their personalised learning content. All these impose difficulties to the users in getting the right information from the right sources. This paper investigates the interoperability issues in eLearning services management and provision and presents an approach to resolve interoperability at three levels.

Semantic-level Visual Content Descriptor combining visual and textual cues as collaterals

In this paper, we introduce a novel high-level visual content descriptor which is devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt to bridge the so called “semantic gap”. The proposed image feature vector model is fundamentally underpinned by the image labelling framework, called Collaterally Confirmed Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts of the images with the state-of-the-art low-level image processing and visual feature extraction techniques for automatically assigning linguistic keywords to image regions. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicates that our proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models.