NEXT GENERATION INTERNET 7.1 Extensions of SPARQL towards Heterogeneous Sources and Domain Annotations 112 7.2 Enabling Federation of Government Metadata Repositories 113 7.3 A Semantic Web Representation of a Product Range Specification in the Automotive Industry 114 7.4 Curated Entities for Enterprise 115 7.5 Continuous Query Optimization and Evaluation over Unified Linked Stream Data and Linked Open Data 116 7.6 Mobile Web + Social Web + Semantic Web = Citizen Sensing 117 7.7 Using the Web to Enhance Desktop Data 118 7.8 Engaging Citizens in the Policy‐Making Process 119 7.9 Extending BPMN 2.0 with Sensor Functions 120 7.10 Preference‐based Discovery of Dynamically Generated Service Offers 121 7.11 Querying Live Linked Data 122 7.12 RDF On the Go: An RDF Storage and Query Processor for Mobile Devices 123 7.13 Generic Provenance Management for Linked Dataspaces 124 7.14 Policy Modeling meets Linked Open Data 125 7.15 E‐Brainstorming in Support of Innovation Processes 126 7.16 A Contextualized Perspective for Linked Sensor Data 127 7.17 A Privacy Preference Framework for the Social Semantic Web 128 7.18 Improving Discovery in Life Sciences and Health Care with Semantic Web Technologies and Linked Data 129 7.19 Why Reading Papers when EEYORE will do that for you!? 130 7.20 The Semantic Public Service Portal (S‐PSP) 131 7.21 Semantic, Terminological and Linguistic Features for Sentence Classification 132 7.22 Personalized Content Delivery on Mobile Phones 133 7.23 Empirically Grounded Linked Knowledge 134 7.24 A Framework to Describe Localisation Services for Automatic Selection of Optimum Service(s) Facilitating the Dynamic Execution of Workflow 135 111
Extensions of SPARQL towards Heterogeneous Sources and Domain Annotations Abstract SPARQL is the W3C Recommended query language for RDF. My current work aims at extending SPARQL in two distinct ways: (i) to allow a better integration of RDF and XML; and (ii) to define a query language for RDF extended with domain specific annotations. Transforming data between XML and RDF is a much required, but not so simple, task in the Semantic Web. The aim of (i) is to enable transparent transformations between these two representation formats, relying on a new query language called XSPARQL. On a different aspect, representing and reasoning with meta-information about RDF triples has been addressed by several proposals for representing time, trust and provenance. Building on top of Annotated RDF, we present an extension of RDF and the SPARQL language, capable of representing and querying triples with annotations. 1. XSPARQL XML and RDF are the underlying representation and storage formats for the Semantic Web. For instance, in the Semantic Web Services domain, data represented in RDF needs to be converted to specific formats of XML (lowering) in order to be transmitted and the received data needs to be converted back to RDF (lifting). However, it is not easy to convert data between the two formats. These transformations, mainly the lowering traditionally have been done in a two step approach, first performing a SPARQL query to retrieve the RDF data and then using XSLT or XQuery on the SPARQL XML results format. One focus of my PhD is to improve these procedures by defining a single step approach relying on a combination of SPARQL and XQuery, called XSPARQL [1]. This language allows to easily convert between the XML and RDF formats thus improving the tasks of lifting and lowering. The merge of XQuery and SPARQL allows to interchangeably use XQuery return clauses and SPARQL construct clauses for the generation of XML and RDF data respectively. Most of the existing proposals to merge XML and RDF rely on translating the data from different formats and/or translating the queries from different languages [2], [3]. 2. AnQL <strong>–</strong> Annotated SPARQL Another extension of SPARQL involves querying Annotated RDF, an extension of RDF towards domain specific annotations [4]. Some of my previous work [5], was to define an extension of Annotated RDF [6] towards supporting RDFS inferencing rules, along with the definition of the Temporal and Fuzzy annotation domains. The Nuno Lopes, Axel Polleres, Stefan Decker DERI, National University of Ireland, <strong>Galway</strong> email: {nuno.lopes,axel.polleres,stefan.decker}@deri.org 112 presented RDFS reasoning procedure which can be formulated independently of the specific annotation domain by being parameterised with operations any domain needs to provide. In order to support querying Annotated RDF, we developed an extension of the SPARQL query language towards domain specific annotations, called AnQL [4]. Annotated RDF, first presented by Udrea et al. [6], consists of extending an RDF triple (s, p, o) with an annotation, where the annotation is taken from a specific domain. For instance, in the temporal domain [7], a triple: :nuno :worksF or :DERI : [2008, 2010] has intended meaning “Nuno has worked for DERI from 2008 to 2010”, while in the fuzzy domain [8] the triple: :nuno :locatedIn :room103 : 0.9 has the intended meaning “Nuno is located in Room 103 to a degree of at least 0.9”. The annotation domain must define the elements of the annotations, a partial order between elements and operations for combining elements of the domain. Based on this, it is possible to define an extension of the RDFS rules where the inferences take into account the annotations of the triples by using these domain specific operations. Further details about our prototype implementation are available in our project webpage at http://anql.deri.org/. Acknowledgements: This work been funded by Science Foundation Ireland, Grant No. SFI/08/CE/I1380 (Lion-2). References [1] W. Akhtar, J. Kopeck´y, T. Krennwallner, and A. Polleres, “XS- PARQL: Traveling between the XML and RDF Worlds - and Avoiding the XSLT Pilgrimage,” in ESWC, S. Bechhofer, M. Hauswirth, J. Hoffmann, and M. Koubarakis, Springer, 2008. [2] D. Berrueta, J. E. Labra, and I. Herman, “XSLT+SPARQL : Scripting the Semantic Web with SPARQL embedded into XSLT stylesheets,” in 4th Workshop on Scripting for the Semantic Web, C. Bizer, S. Auer, G. A. Grimmes, and T. Heath, Tenerife, 2008. [3] N. Bikakis, N. Gioldasis, C. Tsinaraki, and S. Christodoulakis, “Querying XML Data with SPARQL,” in DEXA, S. S. Bhowmick, J. Küng, and R. Wagner, Eds., vol. 5690. Springer, 2009. [4] N. Lopes, A. Polleres, U. Straccia, and A. Zimmermann, “AnQL: SPARQLing Up Annotated RDFS,” in ISWC, P. F. Patel-Schneider, Y. Pan, P. Hitzler, P. Mika, L. Zhang, J. Z. Pan, I. Horrocks, and B. Glimm, Eds., vol. 6496. Springer, November 2010. [5] U. Straccia, N. Lopes, G. Lukácsy, and A. Polleres, “A General Framework for Representing, Reasoning and Querying with Annotated Semantic Web Data,” in AAAI 10. Atlanta, 2010. [6] O. Udrea, D. R. Recupero, and V. S. Subrahmanian, “Annotated RDF,” ACM Trans. Comput. Logic, vol. 11, no. 2, 2010. [7] C. Gutierrez, C. A. Hurtado, and A. A. Vaisman, “Introducing Time into RDF,” IEEE TKDE, vol. 19, no. 2, 2007. [8] U. Straccia, “A Minimal Deductive System for General Fuzzy RDF,” in RR, S. Tessaris, E. Franconi, T. Eiter, C. Gutierrez, S. Handschuh, M.-C. Rousset, and R. A. Schmidt, vol. 5689. Springer, 2009.
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