WebPIE: A web-scale parallel inference engine using MapReduce

Jacopo Urbani*, Spyros Kotoulas, Jason Maassen, Frank Van Harmelen, Henri Bal

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


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.

Original languageEnglish
JournalBelgian/Netherlands Artificial Intelligence Conference
Publication statusPublished - 2012


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