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 4 downloads 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",
}
Downloads: 4
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