With the rise of linked data, more and more semantically described information is being published online according to the principles and technologies of the Semantic Web (especially, RDF and SPARQL). The use of such standard technologies means that this data should be exploitable, integrable and reusable straight away. However, once a potentially interesting dataset has been discovered, significant efforts are currently required in order to understand its schema, its content, the way to query it and what it can answer. In this paper, we propose a method and a tool to automatically discover questions that can be answered by an RDF dataset. We use formal concept analysis to build a hierarchy of meaningful sets of entities from a dataset. These sets of entities represent answers, which common characteristics represent the clauses of the corresponding questions. This hierarchy can then be used as a querying interface, proposing questions of varying levels of granularity and specificity to the user. A major issue is however that thousands of questions can be included in this hierarchy. Based on an empirical analysis and using metrics inspired both from formal concept analysis and from ontology summarization, we devise an approach for identifying relevant questions to act as a starting point to the navigation in the question hierarchy.

The Bibliographic Ontology Specification provides main concepts and properties for describing citations and bibliographic references (i.e. quotes, books, articles, etc) on the Semantic Web.

Ordnance Survey, the national mapping agency of Great Britain, is investigating how semantic web technologies assist its role as a geographical information provider. A major part of this work involves the development of prototype products and datasets in RDF. This article discusses the production of an example dataset for the administrative geography of Great Britain, demonstrating the advantages of explicitly encoding topological relations between geographic entities over traditional spatial queries. We also outline how these data can be linked to other datasets on the web of linked data and some of the challenges that this raises.

The recent evolution of the Web, now designated by the term Web 2.0, has seen the appearance of a huge number of resources created and annotated by users. However the annotations consist only in simple tags that are gathered in unstructured sets called folksonomies. The use of more complex languages to annotate resources and to define semantics according to the vision of the Semantic Web, would improve the understanding by machines and programs, like search engines, of what is on the Web. Indeed tags expressivity is very low compared to the representation standards of the Semantic Web, like RDF and OWL. But users appear to be still reluctant to annotate resources with RDF, and it should be recognized that Semantic Web, contrary to Web 2.0, is still not a reality of today’s Web. One way to take advantage of Semantic Web capabilities right now, without waiting for a change of the annotation usages, would be to be able to generate RDF annotations from tags. As a first step toward this direction, this paper presents a tentative to automatically convert a set of tags into a RDF description in the context of photos on Flickr. Such a method exploits some specificity of tags used on Flickr, some basic natural language processing tools and some semantic resources, in order to relate semantically tags describing a given photo and build a pertinent RDF annotation for this photo.

The OWL Web Ontology Language is a new formal language for representing ontologies in the Semantic Web. OWL has features from several families of representation languages, including primarily Description Logics and frames. OWL also shares many characteristics with RDF, the W3C base of the Semantic Web. In this paper, we discuss how the philosophy and features of OWL can be traced back to these older formalisms, with modifications driven by several other constraints on OWL. Several interesting problems have arisen where these influences on OWL have clashed.