What can be done with the Semantic Web? An overview of Watson-based applications

Thanks to the huge efforts deployed in the community for creating, building and generating semantic information for the Semantic Web, large amounts of machine processable knowledge are now openly available. Watson is an infrastructure component for the Semantic Web, a gateway that provides the necessary functions to support applications in using the Semantic Web. In this paper, we describe a number of applications relying on Watson, with the purpose of demonstrating what can be achieved with the Semantic Web nowadays and what sort of new, smart and useful features can be derived from the exploitation of this large, distributed and heterogeneous base of semantic information

Supporting the semi-automatic acquisition of semantic RESTful service descriptions

This paper presents SWEET: Semantic Web sErvices Editing Tool, the first tool developed for the semi-automatic acquisition of semantic RESTful service descriptions, aiming to support a higher level of automation of common RESTful service tasks, such as discovery and composition

Semantically Annotating RESTful Services with SWEET

This paper presents SWEET: Semantic Web sErvices Editing Tool, the first tool developed for the semi-automatic acquisition of semantic RESTful service descriptions, aiming to support a higher level of automation of common RESTful service tasks, such as discovery and composition

Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources

The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach