Reasoning Web 2012

SPARQL is now widely used as the standard query language for RDF. Since the release of its first version in 2008, the W3C group in charge of the standard has been working on extensions of the language to be included in the new version, SPARQL 1.1. These extensions include several interesting and very useful features for querying RDF.

In this tutorial, we survey two key features of SPARQL 1.1: Federation and navigation capabilities. We focus on the formalization of these two features, in particular, of their syntax and semantics. We analyze some classical theoretical problems such as expressiveness and complexity, and discuss some algorithmic properties. Moreover, we present some important recently discovered issues regarding the normative semantics of federation and navigation in SPARQL 1.1, specifically, on the impossibility of answering some unbounded federated queries and the high computational complexity of the evaluation problem for queries including navigation functionalities. We argue that these issues may have a significant impact on the adoption of the new standard, and we discuss on possible alternatives and their implications.

The web has become a pig sty—everyone dumps information at random places and in random shapes. Try to find the cheapest apartment in Oxford considering rent, travel, tax and heating costs; or a cheap, reasonable reviewed 11" laptop with an SSD drive.

Data extraction flushes structured information out of this sty: It turns mostly unstructured web pages into highly structured knowledge. In this chapter, we give a gentle introduction to data extraction including pointers to existing systems. We start with an overview and classification of data extraction systems along two primary dimensions, the level of supervision and the considered scale. The rest of the chapter is the organized along the first of these dimensions: In the first part, we discuss supervised data extraction, where a human user identifies for each site examples of the relevant data and the system generalizes these examples into extraction programs. We focus particularly on declarative and rule-based paradigms. In the second part, we turn to fully automated (or unsupervised) approaches where the system by itself identifies the relevant data and fully automatically extracts data from many websites. Ontologies or schemata have proven invaluable to guide unsupervised data extraction and we will present an overview of the existing approaches and the different way in which they are using Ontologies.