Reasoning Web 2012 Papers

Description Logics (DLs) play a central role as formalisms for representing ontologies and reasoning about them. This lecture introduces the basics of DLs. We discuss the knowledge modeling capabilities of some of the most prominent DLs, including expressive ones, and present some DL reasoning services. Particular attention is devoted to the query answering problem, and to the increasingly popular framework in which data repositories are queried through DL ontologies. We give an overview of the main challenges that arise in this setting, survey some query answering techniques for both lightweight and expressive DLs, and give an overview of the computational complexity landscape.

Since the early 70s, data management played a central role in organizations and represented a challenging area of research. A number of languages have been proposed to model, query, and manipulate data, as well as for expressing very general classes of integrity constraints, inference procedures, and ontological knowledge. Such languages are nowa- days crucial for many applications such as semantic data publishing and integration, decision support, and knowledge management. In this tutorial we first introduce Datalog, a powerful rule-based language originally intended for expressing complex queries over relational data, and that today is at the basis of languages for the specification of optimization and constraint satisfaction problems as well as of ontological constraints in data and knowledge bases. We then discuss the limitations of Datalog for the semantic web, in particular for ontological modeling and reasoning, and we present several extensions that allow to capture some of the ontology languages of the OWL family, the standard language for semantic data modeling on the semantic web.

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 paper, we survey two key features of SPARQL 1.1: Federation and navigation capabilities. We first introduce the SPARQL standard presenting its syntax and formal semantics. We then focus on the formalization of federation and navigation in SPARQL 1.1. We analyze some classical theoretical problems such as expressiveness and complexity, and discuss algorithmic properties. More- over, we present some important recently discovered issues regarding the norma- tive semantics of federation and navigation in SPARQL 1.1, specifically, on the impossibility of answering some unbounded federated queries and the high com- putational complexity of the evaluation problem for queries including navigation functionalities. Finally, we discuss on possible alternatives to overcome these is- sues and their implications on the adoption of the standard.

This chapter gives an extended introduction to the lightweight pro- files OWL EL, OWL QL, and OWL RL of the Web Ontology Language OWL. The three ontology language standards are sublanguages of OWL DL that are restricted in ways that significantly simplify ontological reasoning. Compared to OWL DL as a whole, reasoning algorithms for the OWL profiles show higher performance, are easier to implement, and can scale to larger amounts of data. Since ontological reasoning is of great importance for designing and deploying OWL ontologies, the profiles are highly attractive for many applications. These advantages come at a price: various modelling features of OWL are not available in all or some of the OWL profiles. Moreover, the profiles are mutually incomparable in the sense that each of them offers a combination of features that is available in none of the others. This chapter provides an overview of these differences and explains why some of them are essential to retain the desired properties. To this end, we recall the relationship between OWL and description logics (DLs), and show how each of the profiles is typically treated in reasoning algorithms.

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 organized along the major division of these approaches into site-specific and supervised versus domain-specific and unsupervised. We first 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 present an overview of the existing approaches and the different ways in which they are using ontologies.

Reasoning with uncertainty and inconsistency in description logics are two important issues in the development of description logic-based ontology engineering. When constructing ontologies, one may obtain ontologies that are inconsistent and are pervaded with uncertain information, such as confidence values. In this paper, we propose some approaches to reasoning with inconsistent and uncertain ontologies in description logics. This paper consists of two parts. In the first part, we propose some inconsistency-tolerant semantics for ontologies with uncertain information. In the second part, we propose an approach to resolving inconsistencies between two heterogenous ontologies caused by erroneous mappings.

Linked Stream Data has emerged as an effort to represent dynamic, time-dependent data streams following the principles of Linked Data. Given the increasing number of available stream data sources like sensors and social network services, Linked Stream Data allows an easy and seamless integration, not only among heterogenous stream data, but also between streams and Linked Data collections, enabling a new range of real-time applications.

This tutorial gives an overview about Linked Stream Data process- ing. It describes the basic requirements for the processing, highlighting the challenges that are faced, such as managing the temporal aspects and memory overflow. It presents the different architectures for Linked Stream Data processing engines, their advantages and disadvantages. The tutorial also reviews the state of the art Linked Stream Data processing systems, and provide a comparison among them regarding the design choices and overall performance. A short discussion of the current challenges in open problems is given at the end.

The recent availability of geospatial information as linked open data has generated new interest in geospatial query processing and reasoning, a topic with a long tradition of research in the areas of databases and artificial intelligence. In this paper we survey recent advances in this important research topic, concentrating on issues of data modeling and querying.

In the decade (2001–2011) that has passed since Semantic Wikis were first proposed, systems have been conceived, developed and used for various purposes. This article aims at giving a comprehensive state-of-the-art overview of the research on Semantic Wikis, stressing what makes them easy to use by a wide and possibly inexperienced au- dience. This article further describes applications and use cases that have driven the research on Semantic Wikis, software techniques, and architectures that have been proposed for Semantic Wikis. Finally, this article suggests possible ways ahead for further research.

This tutorial provides an overview of computational argumentation, focusing on abstract argumentation and assumption-based argumentation, how they relate, as well as possible uses of the latter in Web contexts, and in particular the Semantic Web and Social Networks. The tutorial outlines achievements to date as well as (some) open issues.

In the next article, we will discuss Reasoning Web 2013.