WebPIE: A web-scale parallel inference engine using MapReduce

WebPIE: A web-scale parallel inference engine using MapReduce. Urbani, J., Kotoulas, S., Maassen, J., Van Harmelen, F., & Bal, H. Belgian/Netherlands Artificial Intelligence Conference, 2012.
abstract bibtex

The large amount of Semantic Web data and its fast growth pose a significant computational challenge in performing efficient and scalable reasoning. On a large scale, the resources of single machines are no longer sufficient and we are required to distribute the process to improve performance. The article that we attach to our submission [1] tackles this problem proposing a methodology to perform inference materializing every possible consequence using the MapReduce programming model. We introduce a number of optimizations to address the issues that a naive implementation would raise and to improve the overall performance. We have implemented the presented techniques in a prototype called WebPIE and the evaluation shows that our approach is able to perform complex inference based on the OWL language over a very large input of about 100 billion triples. To the best of our knowledge, it is the only approach that demonstrates complex inference over an input of a hundred billion of triples.

@article{34d46a307ff4440a8fb5efa26a3fb321,
  title    = "WebPIE: A web-scale parallel inference engine using MapReduce",
  abstract = "The large amount of Semantic Web data and its fast growth pose a significant computational challenge in performing efficient and scalable reasoning. On a large scale, the resources of single machines are no longer sufficient and we are required to distribute the process to improve performance. The article that we attach to our submission [1] tackles this problem proposing a methodology to perform inference materializing every possible consequence using the MapReduce programming model. We introduce a number of optimizations to address the issues that a naive implementation would raise and to improve the overall performance. We have implemented the presented techniques in a prototype called WebPIE and the evaluation shows that our approach is able to perform complex inference based on the OWL language over a very large input of about 100 billion triples. To the best of our knowledge, it is the only approach that demonstrates complex inference over an input of a hundred billion of triples.",
  author   = "Jacopo Urbani and Spyros Kotoulas and Jason Maassen and {Van Harmelen}, Frank and Henri Bal",
  year     = "2012",
  journal  = "Belgian/Netherlands Artificial Intelligence Conference",
  issn     = "1568-7805",
}

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