https://www.cs.ox.ac.uk/people/bernardo.cuencagrau/publications/PelletDemo.pdf

Pellet: A practical OWL-DL reasoner

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

The new Semantic Web recommendations for RDF, RDFS and OWL have, at their heart, the RDF graph. Jena2, a second-generation RDF toolkit, is similarly centered on the RDF graph. RDFS and OWL reasoning are seen as graph-to-graph transforms, producing graphs of virtual triples. Rich APIs are provided. The Model API includes support for other aspects of the RDF recommendations, such as containers and reification. The Ontology API includes support for RDFS and OWL, including advanced OWL Full support. Jena includes the de facto reference RDF/XML parser, and provides RDF/XML output using the full range of the rich RDF/XML grammar. N3 I/O is supported. RDF graphs can be stored in-memory or in databases. Jena's query language, RDQL, and the Web API are both offered for the next round of standardization.

/pdf/jena-implementing-the-semantic-web-recommendations-1y9j1osjb1.pdf

Jena: implementing the semantic web recommendations

Activities such as Web Services and the Semantic Web are working to create a web of distributed machine understandable data. In this paper we present an application called 'Semantic Search' which is built on these supporting technologies and is designed to improve traditional web searching. We provide an overview of TAP, the application framework upon which the Semantic Search is built. We describe two implemented Semantic Search systems which, based on the denotation of the search query, augment traditional search results with relevant data aggregated from distributed sources. We also discuss some general issues related to searching and the Semantic Web and outline how an understanding of the semantics of the search terms can be used to provide better results.

Semantic search

We present the OWL API, a high level Application Programming Interface (API) for working with OWL ontologies. The OWL API is closely aligned with the OWL 2 structural specification. It supports parsing and rendering in the syntaxes defined in the W3C specification (Functional Syntax, RDF/XML, OWL/XML and the Manchester OWL Syntax); manipulation of ontological structures; and the use of reasoning engines. The reference implementation of the OWL API, written in Java, includes validators for the various OWL 2 profiles - OWL 2 QL, OWL 2 EL and OWL 2 RL. The OWL API has widespread usage in a variety of tools and applications.

/pdf/the-owl-api-a-java-api-for-owl-ontologies-2hqctf0ncg.pdf

The OWL API: A Java API for OWL ontologies

In this paper, we formalize the problem of Basic Graph Pattern (BGP) optimization for SPARQL queries and main memory graph implementations of RDF data. We define and analyze the characteristics of heuristics for selectivity-based static BGP optimization. The heuristics range from simple triple pattern variable counting to more sophisticated selectivity estimation techniques. Customized summary statistics for RDF data enable the selectivity estimation of joined triple patterns and the development of efficient heuristics. Using the Lehigh University Benchmark (LUBM), we evaluate the performance of the heuristics for the queries provided by the LUBM and discuss some of them in more details.

/pdf/sparql-basic-graph-pattern-optimization-using-selectivity-29u2lg18au.pdf

SPARQL basic graph pattern optimization using selectivity estimation

RDF provides a basic way to represent data for the Semantic Web. We have been experimenting with the query paradigm for working with RDF data in semantic web applications. Query of RDF data provides a declarative access mechanism that is suitable for application usage and remote access. We describe work on a conceptual model for querying RDF data that refines ideas first presented in at the W3C workshop on Query Languages [14] and the design of one possible syntax, derived from [7], that is suitable for application programmers. Further, we present experience gained in three implementations of the query language.

/pdf/three-implementations-of-squishql-a-simple-rdf-query-12s3cou0zm.pdf

Three Implementations of SquishQL, a Simple RDF Query Language

https://dash.harvard.edu/bitstream/handle/1/10588036/Gao%20et%20al.%202006.pdf?sequence=1

SWAN: A distributed knowledge infrastructure for Alzheimer disease research

© A Parallel Processing Framework for RDF Design and Issues Paolo Castagna, Andy Seaborne, Chris Dollin HP Laboratories HPL-2009-346 rdf, parallel processing This document describes the hardware and software architecture of a parallel processing framework for RDF data. The aim of the framework is to simplify the implementation of parallel programs and to support execution over cluster of off-the-shelf machines. From a scripting language, logical execution plans are constructed and then compiled into physical execution plans which are scheduled for execution in parallel over a cluster. At runtime, processing elements simply exchange data as streams of tuples. We are not pioneers in this field; however, not many of the existing systems support parallel RDF data processing. External Posting Date: October 21, 2009 [Fulltext] Approved for External Publication Internal Posting Date: October 21, 2009 [Fulltext] Copyright 2009 Hewlett-Packard Development Company, L.P. 1 A Parallel Processing Framework for RDF Design and Issues Paolo Castagna, Andy Seaborne, Chris Dollin Hewlett-Packard Laboratories, Long Down Avenue, Stoke Gifford, Bristol BS34 8QZ, UK

http://www.hpl.hp.com/techreports/2009/HPL-2009-346.pdf

Andy Seaborne

Papers

Jena: implementing the semantic web recommendations

SPARQL basic graph pattern optimization using selectivity estimation

Three Implementations of SquishQL, a Simple RDF Query Language

SWAN: A distributed knowledge infrastructure for Alzheimer disease research

A Parallel Processing Framework for RDF Design and Issues