16 March 2018

EDGE

Em over bDds for description loGics paramEter learning

EDGE is an algorithm for learning the parameters of probabilistic ontologies under the DISPONTE semantics. It applies EM and computes the expectations by traversing twice the BDDs that are built for inference.

EDGE also contains EDGEMR, which parallelizes the execution of the EM algorithm. Moreover, EDGE is used in the structure and parameter learner LEAP.

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Source Code

The latest version of EDGE can be downloaded from bitbucket here.

EDGE and EDGEMR are also available in the Maven Central Repository. Details here.

Source Code: OWLAPI v 3.01 version
EDGE is completely written in Java. For downloading it, click here, select “EDGE.zip” and then click on File > Save.

Source Code: OWLAPI v 3.10 version
For downloading it, click here, select “EDGE-OWLAPI-3-10.zip” and then click on File > Save.

Datasets

For downloading the datasets for testing EDGE, click here, select “datasets.zip” and then click on File > Save.

Issues
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Manual
Click here to see online manual or download it.

Bibliography
Riccardo Zese. Probabilistic Semantic Web, volume 28 of Studies on the Semantic Web. IOS Press, 2017. [ bib | DOI | http ]

Giuseppe Cota, Riccardo Zese, Elena Bellodi, Evelina Lamma, and Fabrizio Riguzzi. Learning probabilistic ontologies with distributed parameter learning. In Proceedings of the Doctoral Consortium (DC) co-located with the 14th Conference of the Italian Association for Artificial Intelligence (AI*IA 2015), volume 1485 of CEUR Workshop Proceedings, pages 7-12, Aachen, Germany, 2015. © by the authors, Sun SITE Central Europe. [ bib | .pdf ]

Giuseppe Cota, Riccardo Zese, Elena Bellodi, Fabrizio Riguzzi, and Evelina Lamma. Distributed parameter learning for probabilistic ontologies. In 25th International Conference on Inductive Logic Programming (ILP 2015), 2015. [ bib | .pdf ]

Giuseppe Cota, Riccardo Zese, Elena Bellodi, Evelina Lamma, and Fabrizio Riguzzi. Structure learning with distributed parameter learning for probabilistic ontologies. In Doctoral Consortium of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pages 75-84, © by the authors, 2015. [ bib | http | http ]

Riguzzi, F., Bellodi, E., Lamma, E., Zese, R.: Learning the parameters of probabilistic description logics. In: Late Breaking Papers of the 23rd International Conference on Inductive Logic Programming, volume 1187 of CEUR Workshop Proceedings, pages 46-51. CEUR-WS.org, 2013.

Riguzzi, F., Bellodi, E., Lamma, E., Zese, R., Cota, G.: Learning Probabilistic Description Logics. In Uncertainty Reasoning for the Semantic Web III – ISWC International Workshops, URSW 2011-2013, Revised Selected Papers, volume 8816 of Lecture Notes in Computer Science, pages 63-78. Springer, 2013.

Fabrizio Riguzzi, Elena Bellodi, Evelina Lamma, and Riccardo Zese. Parameter learning for probabilistic ontologies. In Proceedings of the 7th International Conference on Web Reasoning and Rule Systems, Mannheim, Germany, 27-29 July 2013, vol. 7994 in LNCS, pages 265–270. Springer (2013).