Visualizing the semantic content of large text databases using text maps

A methodology for generating text map representations of the semantic content of text databases is presented. Text maps provide a graphical metaphor for conceptualizing and visualizing the contents and data interrelationships of large text databases. Described are a set of experiments conducted against the TIPSTER corpora of Wall Street Journal articles. These experiments provide an introduction to current work in the representation and visualization of documents by way of their semantic content

Integrating sensor streams in pHealth networks

Personal Health (pHealth) sensor networks are generally used to monitor the wellbeing of both athletes and the general public to inform health specialists of future and often serious ailments. The problem facing these domain experts is the scale and quality of data they must search in order to extract meaningful results. By using peer-to-peer sensor architectures and a mechanism for reducing the search space, we can, to some extent, address the scalability issue. However, synchronisation and normalisation of distributed sensor streams remains a problem in many networks. In the case of pHealth sensor networks, it is crucial for experts to align multiple sensor readings before query or data mining activities can take place. This paper presents a system for clustering and synchronising sensor streams in preparation for user queries

SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

In this paper, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a flat connectionist analysis. This screening approach aims at supporting speech and language processing by using (1) data-driven learning and (2) robustness of connectionist networks. In order to test this approach, we have developed the SCREEN system which is based on this new robust, learned and flat analysis. In this paper, we focus on a detailed description of SCREEN's architecture, the flat syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the influence of noisy or incomplete input. The main result of this paper is that flat representations allow more robust processing of spontaneous spoken language than deeply structured representations. In particular, we show how the fault-tolerance and learning capability of connectionist networks can support a flat analysis for providing more robust spoken-language processing within an overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial Intelligence Research 6(1), 199

Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

Extending a geo-catalogue with matching capabilities

To achieve semantic interoperability, geo-spatial applications need to be equipped with tools able to understand user terminology that is typically different from the one enforced by standards. In this paper we summarize our experience in providing a semantic extension to the geo-catalogue of the Autonomous Province of Trento (PAT) in Italy. The semantic extension is based on the adoption of the S-Match semantic matching tool and on the use of a specifically designed faceted ontology codifying domain specific knowledge. We also briefly report our experience in the integration of the ontology with the geo-spatial ontology GeoWordNet