Survey of semantic annotation platforms
Proceedings of the 2005 ACM symposium on Applied computing
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Ontology based document annotation: trends and open research problems
International Journal of Metadata, Semantics and Ontologies
Ontology based Text Annotation --OnTeA
Proceedings of the 2007 conference on Information Modelling and Knowledge Bases XVIII
Semantic annotation for knowledge management: Requirements and a survey of the state of the art
Web Semantics: Science, Services and Agents on the World Wide Web
Empowering automatic semantic annotation in grid
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Emails as graph: relation discovery in email archive
Proceedings of the 21st international conference companion on World Wide Web
MapReduce indexing strategies: Studying scalability and efficiency
Information Processing and Management: an International Journal
A practical experience concerning the parallel semantic annotation of a large-scale data collection
Proceedings of the 9th International Conference on Semantic Systems
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Automated annotation of the web documents is a key challenge of the Semantic Web effort. Web documents are structured but their structure is understandable only for a human that is the major problem of the Semantic Web. Semantic Web can be exploited only if metadata understood by a computer reach critical mass. Semantic metadata can be created manually, using automated annotation or tagging tools. Automated semantic annotation tools with the best results are built on different machine learning algorithms requiring training sets. Another approach is to use pattern based semantic annotation solutions built on NLP, information retrieval or information extraction methods. Most of developed methods are tested and evaluated on hundreds of documents which cannot prove its real usage on large scale data such as web or email communication in enterprise or community environment. In this paper we present how a pattern based annotation tool can benefit from Google's MapReduce architecture to process large amount of text data.