Communications of the ACM
Solving the grid interoperability problem by P-GRADE portal at workflow level
Future Generation Computer Systems
Towards Large Scale Semantic Annotation Built on MapReduce Architecture
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
Media Meets Semantic Web --- How the BBC Uses DBpedia and Linked Data to Make Connections
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
RelFinder: Revealing Relationships in RDF Knowledge Bases
SAMT '09 Proceedings of the 4th International Conference on Semantic and Digital Media Technologies: Semantic Multimedia
GMBS: A new middleware service for making grids interoperable
Future Generation Computer Systems
Empowering automatic semantic annotation in grid
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Linkator: enriching web pages by automatically adding dereferenceable semantic annotations
ICWE'10 Proceedings of the 10th international conference on Web engineering
DBpedia spotlight: shedding light on the web of documents
Proceedings of the 7th International Conference on Semantic Systems
Journal of Grid Computing
GJMF - a composable service-oriented grid job management framework
Future Generation Computer Systems
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From a computational point of view, the semantic annotation of large-scale data collections is an extremely expensive task. One possible way of dealing with this drawback is to distribute the execution of the annotation algorithm in several computing environments. In this paper, we show how the problem of semantically annotating a large-scale collection of learning objects has been conducted. The terms related to each learning object have been processed. The output was an RDF graph computed from the DBpedia database. According to an initial study, the use of a sequential implementation of the annotation algorithm would require more than 1600 CPU-years to deal with the whole set of learning objects (about 15 millions). For this reason, a framework able to integrate a set of heterogeneous computing infrastructures has been used to execute a new parallel version of the algorithm. As a result, the problem was solved in 178 days.