The Semantic Web initiative pursues a Web that is understandable to machines as well as it is to humans, while preserving their identifying characteristics: anybody can create and link new information at anytime from anywhere. The Semantic Web stack includes several standard technologies that covers different aspects of this next-generation Web; e.g., models for linking and exchanging data (such as RDF), distributed query languages (SPARQL), and ontology languages (OWL). Accordingly, we use Semantic Technologies to store, integrate and analyze distributed data in problems that require flexible but expressive knowledge models.
Data often comes in many different representations. Knowledge representation techniques take knowledge extracted from data and creates an intermediate representation in order to be further processed using different techniques. A useful visualization of intermediate knowledge representation structures are Tag Clouds. Tag Clouds provide easy to use access to information through a visual interface which provides interaction with the data processed.
Data in real-world applications usually come from different information sources and have different format, meaning, and granularity. Therefore, to achieve effective data exploitation, it is necessary to aggregate them at the syntactic and the semantic levels. We address these issues by applying Information Fusion techniques and tools, which provide solutions for the automatic combination of data from multiple sources, as well as additional contextual knowledge, in order to achieve a better picture of the situation under observation and to make better decisions.