Semantic Search Systems Based on Triple Stores: a New Model for Aggregating, Combining and Retrieving Information

Semantic search systems are going to play a crucial role in the evolution from the current Web to the Semantic Web by providing new, complex functionalities for retrieving, aggregating and combining information. To successfully accomplish this migration three main directions shall be considered: different media formats, different information sources and different search strategies. The more systems will be able to seamlessly operate on all dimensions, the more will be the chances to provide an unprecedented level of versatility to knowledge management systems. This dissertation aims at building the representation basis for systems able to operate on all these three axis. A new resource model is proposed, which is able to support different media formats, to host resource-specific data such as metadata descriptions, text explanations, etc., and to provide a container for semantic interpretations of such resource-specific data (i.e., for semantic knowledge extracted from it). A real-world case study is discussed, where an adapted version of a Semantic Indexing and Retrieval platform is used to index and search several heterogeneous resources.