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Towards Large Scale Semantic Annotation Built on MapReduce Architecture

Towards Large Scale Semantic Annotation Built on MapReduce Architecture. Michal Laclavík , Martin Šeleng , Ladislav Hluchý Institute of Informatics Slovak Academy of Sciences in Bratislava. Motivation. Semantic Annotation or Tagging

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Towards Large Scale Semantic Annotation Built on MapReduce Architecture

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  1. Towards Large Scale Semantic Annotation Built on MapReduce Architecture Michal Laclavík, Martin Šeleng, LadislavHluchý Institute of InformaticsSlovak Academy of Sciences in Bratislava June 23-25, 2008

  2. Motivation • Semantic Annotation or Tagging • Deliver formal understanding of text documents one of main focuses of semantic web • Documents on Web or in enterprise to be understood by computer • To understand content and context June 23-25, 2008

  3. Semantic Annotation • Similar to Information Extraction • Finding meta data about entities, its properties and their relations • Ontologies • Manual tools • (Semi) Automatic tools • Usually tested on a few hundreds documents • Needs: • To deliver application on the web or in enterprises we need to annotate large scale • Semantic Web can be exploited only if metadata understood by a computer reach critical mass • Examples: • Geographical locations, People, Organizations June 23-25, 2008

  4. MapReduce • Google approach for large scale information processing • Commodity PC’s • Application developer needs to implement only Map and Reduce methods • Inputs and outputs are ordered key-value pairs • Fault tolerant, easy to use, scalable to hundred thousands computers • Hadoop • open sourceimplementation by Apache • Yahoo! is using it on10 000 cores in production environment. June 23-25, 2008

  5. Ontea: Pattern Based Annotation • Information extraction and semantic annotation using patterns • Find objects and properties in text • Possibility to transform it to RDF/OWL • Similar to C-PANKOW, KIM or GATE • Very simple solution good for languages where advanced NLP is not present • Applicable in enterprise applications June 23-25, 2008

  6. Ontea in Hadoop • Map function - Pattern.annotation() • Input lines of text • Output key-value pairs e.g. • file_name => organization:Apple • Organization:Apple=>address:Mountain View • Map function – transformers • E.g. lemmatization transformer • input: Settlement:Bratislave,Settlement:Bratislava • Output: Settlement:Bratislava • Reduce function • input key-value pairs (objects and properties) • Output as needed – objects and its relations to files with properties (e.g. in RDF/OWL) June 23-25, 2008

  7. Results & Conclusion • It works, it is portable, it is faster • 12 times faster on 16 cores • http://ontea.sourceforge.net/ June 23-25, 2008

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