LIMES: LInk discovery framework for MEtric Spaces

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LIMES 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. It is easily configurable via a configuration file as well as through a graphical user interface. LIMES can be downloaded as standalone tool for carrying out link discovery or as a Java library.

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General Overview

LIMES implements novel time-efficient approaches for link discovery in 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 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.


The LIMES framework consists of eight main modules of which each can be extended to accommodate new or improved functionality. The central modules of LIMES is the controller module, which coordinates the matching process. The matching process is carried out as follows: First, the controller calls the configuration module, which reads the configuration file and extracts all the information necessary to carry out the comparison of instances, including the URL of the SPARQL-endpoints of the knowledge bases S (source) and T(target), the restrictions on the instances to map (e.g., their type), the expression of the metric to be used and the threshold to be used.

Given that the configuration file is valid w.r.t. the LIMES Specification Language (LSL), the query module is called. This module uses the configuration for the target and source knowledge bases to retrieve instances and properties from the SPARQL-endpoints of the source and target knowledge bases that adhere to the restrictions specified in the configuration file. The query module writes its output into a file by invoking the cache module. Once all instances have been stored in the cache, the controller chooses between performing Link Discovery or Machine Learning. For Link Discovery, LIMES will re-write, plan and execute the Link Specification (LS) included in the configuration file, by calling the rewriter, planner and engine modules resp. The main goal of LD is to identify the set of links (mapping) that satisfy the conditions opposed by the input LS. For Machine Learning, LIMES calls the machine learning algorithm included in the configuration file, to identify an appropriate LS to link S and T. Then it proceeds in executing the LS. For both taks, the mapping will be stored in the output file choosen by the user in the configuration file. The results are finally stored into a RDF or a XML file.

Evaluation Results

The algorithms implemented in LIMES were published in several papers. Below are links to evaluation results.

Running LIMES

  • Download the LIMES package (includes a user manual) and run it locally on your server
  • You can either execute LIMES using the graphical interface or run LIMES via the command line as a Java executable package.


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AKSW Colloquium, 14 September, 3pm, Learning Metrics for Link Discovery ( 2015-09-13T23:23:17+02:00 TommasoSoru)

2015-09-13T23:23:17+02:00 TommasoSoru

In this Colloquium, Tommaso Soru will present the progress of his PhD titled “Learning Metrics for Link Discovery”. The discovery of new links is essential for the construction of the Linked Data cloud. Read more about "AKSW Colloquium, 14 September, 3pm, Learning Metrics for Link Discovery"

AKSW Colloquium, 15-06-2015, Caching for Link Discovery ( 2015-06-12T23:33:25+02:00 TommasoSoru)

2015-06-12T23:33:25+02:00 TommasoSoru

Using Caching for Local Link Discovery on Large Data Sets [PDF] by Mofeed Hassan Engineering the Data Web in the Big Data era demands the development of time- and space-efficient solutions for covering the lifecycle of Linked Data. Read more about "AKSW Colloquium, 15-06-2015, Caching for Link Discovery"

AKSW Colloquium: Edgard Marx and Tommaso Soru on Monday, February 23, 3.00 p.m. ( 2015-02-19T22:53:47+01:00 TommasoSoru)

2015-02-19T22:53:47+01:00 TommasoSoru

On Monday, 23rd of February 2015, Edgard Marx will introduce Smart, a search engine designed over the Semantic Search paradigm; subsequently, Tommaso Soru will present ROCKER, a refinement operator approach for key discovery. Read more about "AKSW Colloquium: Edgard Marx and Tommaso Soru on Monday, February 23, 3.00 p.m."

Two AKSW Papers at #WWW2015 in Florence, Italy ( 2015-01-20T16:09:05+01:00 admin)

2015-01-20T16:09:05+01:00 admin

Hello Community! We are very pleased to announce that two of our papers were accepted for presentation at WWW 2015.  The papers cover novel approaches for Key Discovery while Linking Ontologies and a benchmark framework for entity annotation systems. Read more about "Two AKSW Papers at #WWW2015 in Florence, Italy"

AKSW at #ISWC2014. Come and join, talk and discuss with us! ( 2014-10-16T14:00:30+02:00 admin)

2014-10-16T14:00:30+02:00 admin

Hello AKSW Follower! We are very pleased to announce that nine of our papers were accepted for presentation at ISWC 2014. Read more about "AKSW at #ISWC2014. Come and join, talk and discuss with us!"