Reasoning Web 2013 Papers

With Linked Data, a very pragmatic approach towards achieving the vision of the Semantic Web has gained some traction in the last years. The term Linked Data refers to a set of best practices for publishing and interlinking structured data on the Web. While many standards, methods and technologies developed within by the Semantic Web community are applicable for Linked Data, there are also a number of specific characteristics of Linked Data, which have to be considered. In this article we introduce the main concepts of Linked Data. We present an overview of the Linked Data lifecycle and discuss individual approaches as well as the state-of-the-art with regard to extraction, authoring, link- ing, enrichment as well as quality of Linked Data. We conclude the chapter with a discussion of issues, limitations and further research and development challenges of Linked Data. This article is an updated version of a similar lecture given at Reasoning Web Summer School 2011.

Linked Data promises that a large portion of Web Data will be usable as one big interlinked RDF database against which structured queries can be answered. In this lecture we will show how reasoning – using RDF Schema (RDFS) and the Web Ontology Language (OWL) – can help to obtain more complete answers for such queries over Linked Data. We first look at the extent to which RDFS and OWL features are being adopted on the Web. We then introduce two high-level architectures for query answering over Linked Data and outline how these can be enriched by (lightweight) RDFS and OWL reasoning, enumerating the main challenges faced and discussing reasoning methods that make practical and theoretical trade-offs to address these challenges. In the end, we also ask whether or not RDFS and OWL are enough and discuss numeric reasoning methods that are beyond the scope of these standards but that are often important when integrating Linked Data from several, heterogeneous sources.

Description Logics (DLs) are the logical formalism underlying the standard web ontology language OWL 2. DLs have formal semantics which are the basis for many powerful reasoning services. This paper provides an overview of basic topics in the field of Description Logics by surveying the introductory literature and course material with a focus on DL reasoning services. The resulting compilation also gives a historical perspective on DLs as a research area.

Answer Set Programming (ASP) evolved from various fields such as Logic Programming, Deductive Databases, Knowledge Representation, and Non- monotonic Reasoning, and serves as a flexible language for declarative problem solving. There are two main tasks in problem solving, representation and reasoning, which are clearly separated in the declarative paradigm. In ASP, representation is done using a rule-based language, while reasoning is performed using implementations of general-purpose algorithms, referred to as ASP solvers. Rules in ASP are interpreted according to common sense principles, including a variant of the closed-world-assumption (CWA) and the unique-name-assumption (UNA). Collections of ASP rules are referred to as ASP programs, which represent the modelled knowledge. To each ASP program a collection of answer sets, or intended models, is associated, which stand for the solutions to the modelled problem; this collection can also be empty, meaning that the modelled problem does not admit a solution. Several reasoning tasks exist: the classical ASP task is enumerating all answer sets or determining whether an answer set exists, but ASP also allows for query answering in brave or cautious modes. This article pro- vides an introduction to the field, starting with historical perspectives, followed by a definition of the core language, a guideline to knowledge representation, an overview of existing ASP solvers, and a panorama of current research topics in the field.

Ontology-based data access (OBDA) is regarded as a key ingredient of the new generation of information systems. In the OBDA paradigm, an ontology defines a high-level global schema of (already existing) data sources and provides a vocabulary for user queries. An OBDA system rewrites such queries and ontologies into the vocabulary of the data sources and then delegates the actual query evaluation to a suitable query answering system such as a relational database management system or a datalog engine. In this chapter, we mainly focus on OBDA with the ontology language OWL 2 QL, one of the three profiles of the W3C standard Web Ontology Language OWL2, and relational databases, although other possible languages will also be discussed. We consider different types of conjunctive query rewriting and their succinct- ness, different architectures of OBDA systems, and give an overview of the OBDA system Ontop.

Geospatial semantics as a research field studies how to publish, retrieve, reuse, and integrate geo-data, how to describe geo-data by conceptual models, and how to develop formal specifications on top of data structures to reduce the risk of incompatibilities. Geo-data is highly heterogeneous and ranges from qualitative interviews and thematic maps to satellite imagery and complex simulations. This makes ontologies, se- mantic annotations, and reasoning support essential ingredients towards a Geospatial Semantic Web. In this paper, we present an overview of major research questions, recent findings, and core literature.

These lecture notes provide a brief overview of some state of the art large scale information extraction projects. Consequently, these projects are related to current research activities in the semantic web community. The majority of the learning algorithms developed for these information extraction projects are based on the lexical and syntactical processing of Wikipedia and large web corpora. Due to the size of the processed data and the resulting intractability of the associated inference problems existing knowledge representation formalism are often inadequate for the task. We will present recent advances in combining tractable logical and probabilistic models that bring statistical language process- ing and rule-based approaches closer together. With these lecture notes we hope to convince the attendees that there are numerous synergies and research agendas that can arise when uncertainty-based data-driven research meets rule-based schema-driven research. We also describe cer- tain theoretical and practical advances in making probabilistic inference scale to very large problems.