Dear all,

the LIMES team [1] is happy to announce the first open-source release of LIMES 1.0.0, code name "Arctic Albatros". LIMES is an extensible, time-efficient and accurate link discovery framework for the Web of Data and implements time-efficient algorithms for link discovery such as the original LIMES approach [2] for edit distances, EdJoin and PPJoin+, HR3 [3], HYPPO [4] and ORCHID [5]. LIMES supports the first planning technique for link discovery HELIOS [7] for improving the runtime of complex specifications. The new version also supports supervised and unsupervised machine-learning algorithms for finding accurate link specifications. Try it out and let us know what you think.




User manual:

Developer manual:
Feedback and tickets:

What is new in LIMES 1.0.0:


* 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 creating this release. We also acknowledge the support of the SAKE [8], HOBBIT [9] and GEISER projects [10].

View this announcement on Twitter and the AKSW blog:



Kind regards and link on,

The LIMES team











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