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.



Website: http://limes.sf.net

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/
Feedback and tickets: https://github.com/AKSW/LIMES-dev


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 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:

* http://blog.aksw.org/limes-1-0-0-released/

* https://twitter.com/akswgroup/status/786864628579995648


Kind regards and link on,

The LIMES team


[1] http://limes.sf.net

[2] http://ijcai.org/Proceedings/11/Papers/385.pdf

[3] http://link.springer.com/chapter/10.1007%2F978-3-642-35176-1_24

[4] http://link.springer.com/article/10.1007%2Fs13740-012-0012-y

[5] http://link.springer.com/chapter/10.1007%2F978-3-642-41335-3_25

[6] http://svn.aksw.org/papers/2012/ESWC_EAGLE/public.pdf

[7] http://iswc2014.semanticweb.org/raw.githubusercontent.com/lidingpku/iswc2014/master/paper/87960017-helios-execution-optimization-for-link-discovery.pdf?raw=true

[8] https://www.sake-projekt.de/en/start/

[9] https://project-hobbit.eu/

[10] http://www.projekt-geiser.de

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