Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

A survey of context modelling and reasoning techniques

SPARQL is the standard language for querying RDF data. In this article, we address systematically the formal study of the database aspects of SPARQL, concentrating in its graph pattern matching facility. We provide a compositional semantics for the core part of SPARQL, and study the complexity of the evaluation of several fragments of the language. Among other complexity results, we show that the evaluation of general SPARQL patterns is PSPACE-complete. We identify a large class of SPARQL patterns, defined by imposing a simple and natural syntactic restriction, where the query evaluation problem can be solved more efficiently. This restriction gives rise to the class of well-designed patterns. We show that the evaluation problem is coNP-complete for well-designed patterns. Moreover, we provide several rewriting rules for well-designed patterns whose application may have a considerable impact in the cost of evaluating SPARQL queries.

/pdf/semantics-and-complexity-of-sparql-1jy733raed.pdf

Semantics and complexity of SPARQL

https://www.cs.ox.ac.uk/boris.motik/pubs/ghmppss08next-steps.pdf

OWL 2: The next step for OWL

The OWL 2 Web Ontology Language, informally OWL 2, is an ontology language for the Semantic Web with formally defined meaning. OWL 2 ontologies provide classes, properties, individuals, and data values and are stored as Semantic Web documents. OWL 2 ontologies can be used along with information written in RDF, and OWL 2 ontologies themselves are primarily exchanged as RDF documents. The OWL 2 Document Overview describes the overall state of OWL 2, and should be read before other OWL 2 documents. The meaningful constructs provided by OWL 2 are defined in terms of their structure. As well, a functional-style syntax is defined for these constructs, with examples and informal descriptions. One can reason with OWL 2 ontologies under either the RDF-Based Semantics [OWL 2 RDF-Based Semantics] or the Direct Semantics [OWL 2 Direct Semantics]. If certain restrictions on OWL 2 ontologies are satisfied and the ontology is in OWL 2 DL, reasoning under the Direct Semantics can be implemented using techniques well known in the literature.

/pdf/owl-2-web-ontology-language-structural-specification-and-16ue9glrem.pdf

OWL 2 Web Ontology Language: structural specification and functional-style syntax

Motivated by the ongoing success of Linked Data and the growing amount of semantic data sources available on theWeb, new challenges to query processing are emerging. Especially in distributed settings that require joining data provided by multiple sources, sophisticated optimization techniques are necessary for efficient query processing. We propose novel join processing and grouping techniques to minimize the number of remote requests, and develop an effective solution for source selection in the absence of preprocessed metadata. We present FedX, a practical framework that enables efficient SPARQL query processing on heterogeneous, virtually integrated Linked Data sources. In experiments, we demonstrate the practicability and efficiency of our framework on a set of real-world queries and data sources from the Linked Open Data cloud. With FedX we achieve a significant improvement in query performance over state-of-the-art federated query engines.

/pdf/fedx-optimization-techniques-for-federated-query-processing-1c8q8e4uuy.pdf

FedX: optimization techniques for federated query processing on linked data

Support for ontology evolution is extremely important in ontology engineering and application of ontologies in dynamic environments. A core aspect in the evolution process is the to guarantee consistency of the ontology when changes occur. In this paper we discuss the consistent evolution of OWL ontologies. We present a model for the semantics of change for OWL ontologies, considering structural, logical, and user-defined consistency. We introduce resolution strategies to ensure that consistency is maintained as the ontology evolves.

/pdf/consistent-evolution-of-owl-ontologies-353kp1fqdm.pdf

Consistent evolution of OWL ontologies

The purpose of this paper is to provide a rigorous comparison of six query languages for RDF. We outline and categorize features that any RDF query language should provide and compare the individual languages along these features. We describe several practical usage examples for RDF queries and conclude with a comparison of the expressiveness of the particular query languages. The use cases, sample data and queries for the respective languages are available on the web [6].

A comparison of RDF query languages

One of the major problems of large scale, distributed and evolving ontologies is the potential introduction of inconsistencies. In this paper we survey four different approaches to handling inconsistency in DL-based ontologies: consistent ontology evolution, repairing inconsistencies, reasoning in the presence of inconsistencies and multi-version reasoning. We present a common formal basis for all of them, and use this common basis to compare these approaches. We discuss the different requirements for each of these methods, the conditions under which each of them is applicable, the knowledge requirements of the various methods, and the different usage scenarios to which they would apply.

/pdf/a-framework-for-handling-inconsistency-in-changing-rzp5krd3f0.pdf

Peter Haase

Papers

OWL 2 Web Ontology Language: structural specification and functional-style syntax

FedX: optimization techniques for federated query processing on linked data

Consistent evolution of OWL ontologies

A comparison of RDF query languages

A framework for handling inconsistency in changing ontologies