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.
| Original language | English |
|---|---|
| Journal | Belgian/Netherlands Artificial Intelligence Conference |
| Publication status | Published - 2012 |
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WebPIE : A web-scale parallel inference engine using MapReduce. / Urbani, Jacopo; Kotoulas, Spyros; Maassen, Jason; Van Harmelen, Frank; Bal, Henri.
In: Belgian/Netherlands Artificial Intelligence Conference, 2012.Research output: Contribution to Journal › Article › Academic › peer-review
TY - JOUR
T1 - WebPIE
T2 - A web-scale parallel inference engine using MapReduce
AU - Urbani, Jacopo
AU - Kotoulas, Spyros
AU - Maassen, Jason
AU - Van Harmelen, Frank
AU - Bal, Henri
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84874794293&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874794293&partnerID=8YFLogxK
M3 - Article
JO - Belgian/Netherlands Artificial Intelligence Conference
JF - Belgian/Netherlands Artificial Intelligence Conference
SN - 1568-7805
ER -