Matches in ScholarlyData for { ?s ?p ?o. }
- 6 abstract "The Sindice Semantic Web index provides search capabilities over today more than 30 million documents. A scalable reasoning mechanism for real-world web data is important in order to increase the precision and recall of the Sindice index by inferring useful information (e.g. RDF Schema features, equality, property characteristic such as inverse functional properties or annotation properties from OWL). In this paper, we introduce our notion of context dependent reasoning for RDF documents published on the Web according to the linked data principle. We then illustrate an efficient methodology to perform context dependent RDFS and partial OWL inference based on a persistent TBox composed of a network of web ontologies. Finally we report preliminary evaluation results of our implementation underlying the Sindice web data index.".
- 6 hasAuthorList authorList.
- 6 isPartOf proceedings.
- 6 keyword "Linked Data".
- 6 keyword "Reasoning".
- 6 keyword "Scalabiliy".
- 6 title "Context Dependent Reasoning for Semantic Documents in Sindice".
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- item-1 hasContent renaud-delbru.
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- item-2 hasContent axel-polleres.
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- item-3 hasContent giovanni-tummarello.
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- item-4 type ListItem.
- item-4 hasContent stefan-decker.
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- 7 creator andrew-newman.
- 7 creator jane-hunter.
- 7 creator yuan-fang-li.
- 7 type InProceedings.
- 7 label "A Scale-Out RDF Molecule Store for Improved Co-Identification, Querying and Inferencing".
- 7 sameAs 7.
- 7 abstract "Semantic inferencing and querying across large scale RDF triple stores is notoriously slow. Our objective is to expedite this process by employing Google's MapReduce framework to implement scale-out distributed querying and reasoning. This approach requires RDF graphs to be decomposed into smaller units that are distributed across computational nodes. RDF Molecules appear to offer an ideal approach - providing an intermediate level of granularity between RDF graphs and triples. However, the original RDF molecule definition has inherent limitations that will adversely affect performance. In this paper, we propose a number of extensions to RDF molecules (hierarchy and ordering) to overcome these limitations. We then present implementation details for our MapReduce-based RDF molecule store describing: (a) graph decomposition into molecules; (b) SPARQL querying across molecules; and (c) molecule merging to retrieve the search results. Finally we evaluate the benefits of our approach in the context of the BioMANTA project � an application that requires integration and querying across large-scale protein-protein interaction datasets. The results of performance evaluations based on this case study are presented and discussed.".
- 7 hasAuthorList authorList.
- 7 isPartOf proceedings.
- 7 keyword "MapReduce".
- 7 keyword "RDF molecules".
- 7 keyword "distributed querying".
- 7 keyword "scalability".
- 7 title "A Scale-Out RDF Molecule Store for Improved Co-Identification, Querying and Inferencing".
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- item-1 type ListItem.
- item-1 hasContent andrew-newman.
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- item-2 type ListItem.
- item-2 hasContent yuan-fang-li.
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- item-3 type ListItem.
- item-3 hasContent jane-hunter.
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- 8 creator anupam-joshi.
- 8 creator onkar-walavalkar.
- 8 creator tim-finin.
- 8 creator yelena-yesha.
- 8 type InProceedings.
- 8 label "Streaming Knowledge Bases".
- 8 sameAs 8.
- 8 abstract "With the advdent of pervasive computing, we encounter many scenarios where data is constantly flowing between sensors and applications. The volume of data produced is large, so is the rate of the dataflow. In such scenarios, knowledge extraction boils down to finding useful information i.e. detecting events of interest. Typical use cases where event detection is of paramount importance are surveillance, tracking, telecommunications data management, disease outburst detection and environmental monitoring. There are many streaming database applications built to deal with these dynamic environments. However, they can only deal with raw data � not with streaming facts. We argue that much like a new database approach had to be developed to deal with streaming data, a new approach will be required to deal with streaming facts expressed in the languages of the semantic web.Existing reasoners use techniques that load the whole RDF graph in main memory and carry out queries on it. This approach is of little use in real-time reasoning for streaming scenarios and takes considerable amount of time. In this paper, we combine a continuous query processors with Semantic Web techniques to build a reasoner that can deal with streaming facts. We describe our technique, and present empirical validation of its efficacy.".
- 8 hasAuthorList authorList.
- 8 isPartOf proceedings.
- 8 keyword "RDF".
- 8 keyword "Streaming".
- 8 title "Streaming Knowledge Bases".
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- item-1 hasContent onkar-walavalkar.
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- item-2 hasContent anupam-joshi.
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- item-3 hasContent tim-finin.
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- item-4 hasContent yelena-yesha.
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- 10 creator manuel-salvadores.
- 10 creator mark-a-musen.
- 10 creator natalya-noy.
- 10 creator paul-r-alexander.
- 10 type InProceedings.
- 10 label "The Quad Economy of a Semantic Web Ontology Repository".
- 10 sameAs 10.
- 10 abstract "Publishers of Linked Data require scalable storage and retrieval in- frastructure due to the size of datasets and potentially high rate of lookups on popular sites. In this paper we investigate the feasibility of using a distributed key-value store as an underlying storage component for a Linked Data server which provides functionality for serving Linked Data via HTTP lookups and in addition offers single triple pattern lookups. We devise two storage schemes for our CumulusRDF system implemented on Apache Cassandra, an open-source key-value store. We compare the schemes with a state-of-the-art distributed RDF store on a subset of DBpedia and both synthetic workloads and workloads ob- tained from DBpedia’s access logs. Results on a cluster of up to 8 machines show that CumulusRDF is competetive to state-of-the-art distributed RDF stores.".