For certain disable persons unable to communicate, we present a palliative aid which consists of a virtual pictographic keyboard associated to a text processing from a pictographic scripture. Words and the grammar are given as pictograms. The pictographic lexicon must be organized following the mental lexicon of the user to propose the pictograms of grammar in order to facilitate his (her) task of writing. We discuss the utility of ontologies in the organization of lexicons and in the building of texts.

We present a semantically-driven approach to uncertainties within and across ontologies. Ontologies are widely used not only by the Semantic Web but also by artificial systems in general. They represent and structure a domain with respect to its semantics. Uncertainties, however, have been rarely taken into account in ontological representation, even though they are inevitable when applying ontologies in `real world' applications. In this paper, we analyze why uncertainties are necessary for ontologies, how and where uncertainties have to be represented in ontologies, and what their semantics are. In particular, we investigate which ontology constructions need to address uncertainty issues and which ontology constructions should not be affected by uncertainties on the basis of their semantics. As a result, the use of uncertainties is restricted to appropriate cases, which reduces complexity and guides ontology development. We give examples and motivation from the field of spatially-aware systems in indoor environments.

We present a work in the field of formal ontologies, notion taken from the knowledge representation community. What we study is the concept of time and aspect described and conceptualized from linguistics. Our aim is thus to propose a formal ontology of time and aspect considering temporal concepts introduced in a formal way.

This paper describes the Spyglass tool, which is designed to help analysts explore very large collections of unstructured text documents. Spyglass uses a domain ontology to index documents, and provides retrieval and visualization services based on the ontology and the resulting index. The ontology based approach allows analysts to share information and helps to ensure consistency of results. The approach is also scalable and lends itself very well to parallel computation. The Spyglass system is described in detail and indexing and query results using a large set of sample documents are presented.

The emerging Web of Data utilizes the web infrastructure to represent and interrelate data. The foundational standards of the Web of Data include the Uniform Resource Identifier (URI) and the Resource Description Framework (RDF). URIs are used to identify resources and RDF is used to relate resources. While RDF has been posited as a logic language designed specifically for knowledge representation and reasoning, it is more generally useful if it can conveniently support other models of computing. In order to realize the Web of Data as a general-purpose medium for storing and processing the world's data, it is necessary to separate RDF from its logic language legacy and frame it simply as a data model. Moreover, there is significant advantage in seeing the Semantic Web as a particular interpretation of the Web of Data that is focused specifically on knowledge representation and reasoning. By doing so, other interpretations of the Web of Data are exposed that realize RDF in different capacities and in support of different computing models.

Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying Wikipedia to natural language processing; using it to facilitate information retrieval and information extraction; and as a resource for ontology building. The article addresses how Wikipedia is being used as is, how it is being improved and adapted, and how it is being combined with other structures to create entirely new resources. We identify the research groups and individuals involved, and how their work has developed in the last few years. We provide a comprehensive list of the open-source software they have produced.

Social Network Analysis (SNA) tries to understand and exploit the key features of social networks in order to manage their life cycle and predict their evolution. Increasingly popular web 2.0 sites are forming huge social network. Classical methods from social network analysis (SNA) have been applied to such online networks. In this paper, we propose leveraging semantic web technologies to merge and exploit the best features of each domain. We present how to facilitate and enhance the analysis of online social networks, exploiting the power of semantic social network analysis.

As ontologies proliferate and automatic reasoners become more powerful, the problem of protecting sensitive information becomes more serious. In particular, as facts can be inferred from other facts, it becomes increasingly likely that information included in an ontology, while not itself deemed sensitive, may be able to be used to infer other sensitive information. We first consider the problem of testing an ontology for safeness defined as its not being able to be used to derive any sensitive facts using a given collection of inference rules. We then consider the problem of optimizing an ontology based on the criterion of making as much useful information as possible available without revealing any sensitive facts.

Successful management of emotional stimuli is a pivotal issue concerning Affective Computing (AC) and the related research. As a subfield of Artificial Intelligence, AC is concerned not only with the design of computer systems and the accompanying hardware that can recognize, interpret, and process human emotions, but also with the development of systems that can trigger human emotional response in an ordered and controlled manner. This requires the maximum attainable precision and efficiency in the extraction of data from emotionally annotated databases While these databases do use keywords or tags for description of the semantic content, they do not provide either the necessary flexibility or leverage needed to efficiently extract the pertinent emotional content. Therefore, to this extent we propose an introduction of ontologies as a new paradigm for description of emotionally annotated data. The ability to select and sequence data based on their semantic attributes is vital for any study involving metadata, semantics and ontological sorting like the Semantic Web or the Social Semantic Desktop, and the approach described in the paper facilitates reuse in these areas as well.

The promise of e-Science will only be realized when data is discoverable, accessible, and comprehensible within distributed teams, across disciplines, and over the long-term--without reliance on out-of-band (non-digital) means. We have developed the open-source Tupelo semantic content management framework and are employing it to manage a wide range of e-Science entities (including data, documents, workflows, people, and projects) and a broad range of metadata (including provenance, social networks, geospatial relationships, temporal relations, and domain descriptions). Tupelo couples the use of global identifiers and resource description framework (RDF) statements with an aggregatable content repository model to provide a unified space for securely managing distributed heterogeneous content and relationships.