BibSonomy publications for /concept/user/ivan_herman/semanticwebTue Nov 03 13:54:19 CET 2009The Semantic Web - ISWC 2009731--746Lecture Notes in Computer ScienceLinkedGeoData: Adding a Spatial Dimension to the Web of Data58232009linkeddata linkedgeodata semanticweb In order to employ the Web as a medium for data and information integration, comprehensive datasets and vocabularies are required as they enable the disambiguation and alignment of other data and information. Many real-life information integration and aggregation tasks are impossible without comprehensive background knowledge related to spatial features of the ways, structures and landscapes surrounding us. In this paper we contribute to the generation of a spatial dimension for the Data Web by elaborating on how the collaboratively collected OpenStreetMap data can be transformed and represented adhering to the RDF data model. We describe how this data can be interlinked with other spatial data sets, how it can be made accessible for machines according to the linked data paradigm and for humans by means of a faceted geo-data browser.Tue Nov 03 13:52:23 CET 2009The Semantic Web - ISWC 2009763--778Lecture Notes in Computer ScienceProduce and Consume Linked Data with Drupal!58232009drupal rdfa semanticweb swusage Currently a large number ofWeb sites are driven by Content Management Systems (CMS) which manage textual and multimedia content but also - inherently - carry valuable information about a site’s structure and content model. Exposing this structured information to the Web of Data has so far required considerable expertise in RDF and OWL modelling and additional programming effort. In this paper we tackle one of the most popular CMS: Drupal.We enable site administrators to export their site content model and data to theWeb of Data without requiring extensive knowledge on Semantic Web technologies. Our modules create RDFa annotations and – optionally – a SPARQL endpoint for any Drupal site out of the box. Likewise, we add the means to map the site data to existing ontologies on the Web with a search interface to find commonly used ontology terms. We also allow a Drupal site administrator to include existing RDF data from remote SPARQL endpoints on the Web in the site. When brought together, these features allow networked RDF Drupal sites that reuse and enrich Linked Data. We finally discuss the adoption of our modules and report on a use case in the biomedical field and the current status of its deployment.Tue Nov 03 13:52:06 CET 2009The Semantic Web - ISWC 2009909--924Lecture Notes in Computer ScienceA Case Study in Integrating Multiple E-commerce Standards via Semantic Web Technology58232009ecommerce economics semanticweb swusage Internet business-to-business transactions present great challenges in merging information from different sources. In this paper we describe a project to integrate four representative commercial classification systems with the Federal Cataloging System (FCS). The FCS is used by the US Defense Logistics Agency to name, describe and classify all items under inventory control by the DoD. Our approach uses the ECCMA Open Technical Dictionary (eOTD) as a common vocabulary to accommodate all different classifications. We create a semantic bridging ontology between each classification and the eOTD to describe their logical relationships in OWL DL. The essential idea is that since each classification has formal definitions in a common vocabulary, we can use subsumption to automatically integrate them, thus mitigating the need for pairwise mappings. Furthermore our system provides an interactive interface to let users choose and browse the results and more importantly it can translate catalogs that commit to these classifications using compiled mapping results.Tue Nov 03 13:45:41 CET 2009The Semantic Web - ISWC 2009537--552Lecture Notes in Computer ScienceFunctions over RDF Language Elements58232009rdf semanticweb spreadsheet RDF data are usually accessed using one of two methods: either, graphs are rendered in forms perceivable by human users (e.g., in tabular or in graphical form), which are difficult to handle for large data sets. Alternatively, query languages like SPARQL provide means to express information needs in structured form; hence they are targeted towards developers and experts. Inspired by the concept of spreadsheet tools, where users can perform relatively complex calculations by splitting formulas and values across multiple cells, we have investigated mechanisms that allow us to access RDF graphs in a more intuitive and manageable, yet formally grounded manner. In this paper, we make three contributions towards this direction. First, we present RDFunctions, an algebra that consists of mappings between sets of RDF language elements (URIs, blank nodes, and literals) under consideration of the triples contained in a background graph. Second, we define a syntax for expressing RDFunctions, which can be edited, parsed and evaluated. Third, we discuss Tripcel, an implementation of RDFunctions using a spreadsheet metaphor. Using this tool, users can easily edit and execute function expressions and perform analysis tasks on the data stored in an RDF graph.Tue Nov 03 13:41:58 CET 2009The Semantic Web - ISWC 2009310--327Lecture Notes in Computer ScienceDynamic Querying of Mass-Storage RDF Data with Rule-Based Entailment Regimes58232009rdf rdfs rules semanticweb sparql RDF Schema (RDFS) as a lightweight ontology language is gaining popularity and, consequently, tools for scalable RDFS inference and querying are needed. SPARQL has become recently a W3C standard for querying RDF data, but it mostly provides means for querying simple RDF graphs only, whereas querying with respect to RDFS or other entailment regimes is left outside the current specification. In this paper, we show that SPARQL faces certain unwanted ramifications when querying ontologies in conjunction with RDF datasets that comprise multiple named graphs, and we provide an extension for SPARQL that remedies these effects. Moreover, since RDFS inference has a close relationship with logic rules, we generalize our approach to select a custom ruleset for specifying inferences to be taken into account in a SPARQL query. We show that our extensions are technically feasible by providing benchmark results for RDFS querying in our prototype system GiaBATA, which uses Datalog coupled with a persistent Relational Database as a back-end for implementing SPARQL with dynamic rule-based inference. By employing different optimization techniques like magic set rewriting our system remains competitive with state-of-the-art RDFS querying systems.Tue Nov 03 13:38:34 CET 2009The Semantic Web - ISWC 2009293--309Lecture Notes in Computer ScienceExecuting SPARQL Queries over the Web of Linked Data58232009linkeddata rdf se semanticweb sparql The Web of Linked Data forms a single, globally distributed dataspace. Due to the openness of this dataspace, it is not possible to know in advance all data sources that might be relevant for query answering. This openness poses a new challenge that is not addressed by traditional research on federated query processing. In this paper we present an approach to execute SPARQL queries over the Web of Linked Data. The main idea of our approach is to discover data that might be relevant for answering a query during the query execution itself. This discovery is driven by following RDF links between data sources based on URIs in the query and in partial results. The URIs are resolved over the HTTP protocol into RDF data which is continuously added to the queried dataset. This paper describes concepts and algorithms to implement our approach using an iterator-based pipeline. We introduce a formalization of the pipelining approach and show that classical iterators may cause blocking due to the latency of HTTP requests. To avoid blocking, we propose an extension of the iterator paradigm. The evaluation of our approach shows its strengths as well as the still existing challenges.Tue Nov 03 13:36:18 CET 2009The Semantic Web - ISWC 2009229--242Lecture Notes in Computer ScienceWhat Four Million Mappings Can Tell You about Two Hundred Ontologies58232009HCLS ontologies repository semanticweb The field of biomedicine has embraced the Semantic Web probably more than any other field. As a result, there is a large number of biomedical ontologies covering overlapping areas of the field. We have developed BioPortal— an open community-based repository of biomedical ontologies. We analyzed ontologies and terminologies in BioPortal and the Unified Medical Language System (UMLS), creating more than 4 million mappings between concepts in these ontologies and terminologies based on the lexical similarity of concept names and synonyms. We then analyzed the mappings and what they tell us about the ontologies themselves, the structure of the ontology repository, and the ways in which the mappings can help in the process of ontology design and evaluation. For example, we can use the mappings to guide users who are new to a field to the most pertinent ontologies in that field, to identify areas of the domain that are not covered sufficiently by the ontologies in the repository, and to identify which ontologies will serve well as background knowledge in domain-specific tools. While we used a specific (but large) ontology repository for the study, we believe that the lessons we learned about the value of a large-scale set of mappings to ontology users and developers are general and apply in many other domains.Tue Nov 03 13:33:14 CET 2009The Semantic Web - ISWC 2009196--212Lecture Notes in Computer ScienceColoring RDF Triples to Capture Provenance58232009provenance rdf semanticweb Recently, the W3C Linking Open Data effort has boosted the publication and inter-linkage of large amounts of RDF datasets on the Semantic Web. Various ontologies and knowledge bases with millions of RDF triples from Wikipedia and other sources, mostly in e-science, have been created and are publicly available. Recording provenance information of RDF triples aggregated from different heterogeneous sources is crucial in order to effectively support trust mechanisms, digital rights and privacy policies. Managing provenance becomes even more important when we consider not only explicitly stated but also implicit triples (through RDFS inference rules) in conjunction with declarative languages for querying and updating RDF graphs. In this paper we rely on colored RDF triples represented as quadruples to capture and manipulate explicit provenance information.Tue Nov 03 13:29:21 CET 2009The Semantic Web - ISWC 2009634--649Lecture Notes in Computer ScienceScalable Distributed Reasoning Using MapReduce58232009owl parallel rdfs semanticweb We address the problem of scalable distributed reasoning, proposing a technique for materialising the closure of an RDF graph based on MapReduce. We have implemented our approach on top of Hadoop and deployed it on a compute cluster of up to 64 commodity machines. We show that a naive implementation on top of MapReduce is straightforward but performs badly and we present several non-trivial optimisations. Our algorithm is scalable and allows us to compute the RDFS closure of 865M triples from the Web (producing 30B triples) in less than two hours, faster than any other published approach.Tue Nov 03 13:26:16 CET 2009The Semantic Web - ISWC 2009682--697Lecture Notes in Computer ScienceParallel Materialization of the Finite RDFS Closure for Hundreds of Millions of Triples58232009owl parallel rdfs semanticweb In this paper, we consider the problem of materializing the complete finite RDFS closure in a scalable manner; this includes those parts of the RDFS closure that are often ignored such as literal generalization and container membership properties. We point out characteristics of RDFS that allow us to derive an embarrassingly parallel algorithm for producing said closure, and we evaluate our C/MPI implementation of the algorithm on a cluster with 128 cores using different-size subsets of the LUBM 10,000-university data set. We show that the time to produce inferences scales linearly with the number of processes, evaluating this behavior on up to hundreds of millions of triples. We also show the number of inferences produced for different subsets of LUBM10k. To the best of our knowledge, our work is the first to provide RDFS inferencing on such large data sets in such low times. Finally, we discuss future work in terms of promising applications of this approach including OWL2RL rules, MapReduce implementations, and massive scaling on supercomputers.Tue Nov 03 13:20:40 CET 2009Lecture Notes in Computer ScienceThe Semantic Web, 8th International Semantic Web Conference, ISWC 2009Lecture Notes in Computer Science2009semanticweb sweo Tue Nov 03 13:16:34 CET 2009The Semantic Web: Research and Applications, 6th European Semantic Web Conference, ESWC 20092009semanticweb sweo Tue Nov 03 13:01:35 CET 2009Reasoning Web Semantic Technologies for Information Systems2009semanticweb sweo Tue Nov 03 12:59:41 CET 2009Ontology Economics2010economics owl semanticweb sweo Tue Nov 03 12:58:10 CET 2009Handbook on Ontologies2009ontologies ontology semanticweb sweo Tue Nov 03 12:57:00 CET 2009Semantic Web Information Management2010semanticweb sweo swusage Tue Nov 03 12:53:10 CET 2009Semantic Techniques for the Web2009books semanticweb sweo Sat Sep 12 19:29:16 CEST 2009{IEEE} Internet Computing{Januar/February}48--55Semantic Email Addressing132009email semantic_web user_interface userinterface Email addresses, like telephone numbers, are opaque identifiers. They’re often hard to remember, and, worse still, they change from time to time. Semantic email addressing {(SEA)} lets users send email to a semantically specified group of recipients. {SEA} provides all of the functionality of static email mailing lists, but because users can maintain their own profiles, they don’t need to subscribe, unsubscribe, or change email addresses in static lists. Because of its targeted nature, {SEA} could help combat unintentional spam and preserve the privacy of email addresses and even individual identities.Sat Sep 12 18:27:06 CEST 2009Tokyo, Japan{WWW2005}Named Graphs, Provenance and Trust2005RDF named_graphs semantic_web The Semantic Web consists of many {RDF} graphs nameable by {URIs.} This paper extends the syntax and semantics of {RDF} to cover such Named Graphs. This enables {RDF} statements that describe graphs, which is beneficial in many Semantic Web application areas. As a case study, we explore the application area of Semantic Web publishing: Named Graphs allow publishers to communicate assertional intent, and to sign their graphs; information consumers can evaluate specific graphs using task-specific trust policies, and act on information from those Named Graphs that they accept. Graphs are trusted depending on: their content; information about the graph; and the task the user is performing. The extension of {RDF} to Named Graphs provides a formally defined framework to be a foundation for the Semantic Web trust layer.Sat Sep 12 18:24:01 CEST 2009Yokohama, JapanThe Semantic Web — {ISWC2004}Named Graphs, Provenance and Trust2005Graphs Provenance RDF Trust named_graphs security semantic_web The Semantic Web consists of many {RDF} graphs nameable by {URIs.} This paper extends the syntax and semantics of {RDF} to cover such Named Graphs. This enables {RDF} statements that describe graphs, which is beneficial in many Semantic Web application areas. As a case study, we explore the application area of Semantic Web publishing: Named Graphs allow publishers to communicate assertional intent, and to sign their graphs; information consumers can evaluate specific graphs using task-specific trust policies, and act on information from those Named Graphs that they accept. Graphs are trusted depending on: their content; information about the graph; and the task the user is performing. The extension of {RDF} to Named Graphs provides a formally defined framework to be a foundation for the Semantic Web trust layer.