Dear all,
the LIMES Dev team is happy to announce LIMES 1.0.0.
LIMES, the Link Discovery Framework for Metric Spaces, is a link discovery framework for the Web of Data. It implements time-efficient approaches for large-scale link discovery based on the characteristics of metric spaces. Our approaches facilitate different approximation techniques to compute estimates of the similarity between instances. These estimates are then used to filter out a large amount of those instance pairs that do not suffice the mapping conditions. By these means, LIMES can reduce the number of comparisons needed during the mapping process by several orders of magnitude. The approaches implemented in LIMES include the original LIMES algorithm for edit distances, HR3, HYPPO and ORCHID.
Additionally, LIMES supports the first planning technique for link discovery HELIOS, that minimizes the overall execution of a link specification, without any loss of completeness. Moreover, LIMES implements supervised and unsupervised machine-learning algorithms for finding accurate link specifications. The algorithms implemented here include the supervised, active and unsupervised versions of EAGLE and WOMBAT.
Website: http://aksw.org/Projects/LIMES.html
Download: https://github.com/AKSW/LIMES-dev/releases/tag/1.0.0
GitHub: https://github.com/AKSW/LIMES-dev
User manual: http://aksw.github.io/LIMES-dev/user_manual/
Developer manual: http://aksw.github.io/LIMES-dev/developer_manual/
What is new in LIMES 1.0.0:
- New LIMES GUI
- New Controller that supports manual and graphical configuration
- New machine learning pipeline: supports supervised, unsupervised and active learning algorithms
- New dynamic planning for efficient link discovery
- Updated execution engine to handle dynamic planning
- Added support for qualitative (Precision, Recall, F-measure etc.) and quantitative (runtime duration etc.) evaluation metrics for mapping evaluation, in the presence of a gold standard
- Added support for configuration files in XML and RDF formats
- Added support for pointsets metrics such as Mean, Hausdorff and Surjection
- Added support for MongeElkan, RatcliffObershelp string measures
- Added support for Allen’s algebra temporal relations for event data
- Added support for all topological relations derived from the DE-9IM model
- Migrated the codebase to Java 8 and Jena 3.0.1
We would like to thank everyone who helped to create this release. We also acknowledge the support of the SAKE and HOBBIT projects.
Kind regards,
