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.

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.

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.

Automatically constructing the semantic web

The storage and processing capacity realised by computing has lead to an explosion of data retention. We now reach the point of information overload and must begin to use computers to process more complex information. In particular, the proposition of the Semantic Web has given structure to this problem, but has yet realised practically. The largest of its problems is that of ontology construction; without a suitable automatic method most will have to be encoded by hand. In this paper we discus the current methods for semi and fully automatic construction and their current shortcomings. In particular we pay attention the application of ontologies to products and the particle application of the ontologies.

Networks of contextualised ontologies for the Semantic Web

Currently many ontologies are available for addressing different domains. However, it is not always possible to deploy such ontologies to support collaborative working, so that their full potential can be exploited to implement intelligent cooperative applications capable of reasoning over a network of context-specific ontologies. The main problem arises from the fact that presently ontologies are created in an isolated way to address specific needs. However we foresee the need for a network of ontologies which will support the next generation of intelligent applications/devices, and, the vision of Ambient Intelligence. The main objective of this paper is to motivate the design of a networked ontology (Meta) model which formalises ways of connecting available ontologies so that they are easy to search, to characterise and to maintain. The aim is to make explicit the virtual and implicit network of ontologies serving the Semantic Web.