LIMES - A Framework for Link Discovery on the Semantic Web

The Linked Data paradigm builds upon the backbone of distributed knowledge bases connected by typed links. The aim of Link Discovery is to identify the set {(s, t) ∈ S × T : R(s, t)} given an input relation R and two sets S (source) and T (target) of RDF resources. The mere size of current knowledge bases as well as their sheer number pose two major challenges when aiming to support the computation of links across and within them. The first is that tools for link discovery have to be time-efficient when they compute links. Secondly, these tools have to produce links of high quality to serve the applications built upon Linked Data well.

LIMES is a link discovery framework for the Web of Data, which implements time-efficient approaches for large-scale link discovery based on the characteristics of metric spaces. LIMES 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.

The current version of the LIMES framework is the product of seven years of research on these two challenges. The framework combines diverse algorithms for link discovery within a generic and extensible architecture.

LIMES implements novel time-efficient approaches for link discovery in metric spaces. Such approaches facilitate different approximation techniques to compute estimates of the similarity between instances. These estimates are then used to filter out a large number 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.

In addition to supporting configurations as input files, LIMES provides a graphical user interface (GUI) to assist the end user during the LD process. The GUI provides wizards which ease the link specification creation process and allow configuration of the machine learning algorithms.

LIMES includes a set of manuals, with a comprehensive guide about the tool, that includes:

  1. User manual, with details on how to use and configure the LIMES Java application;

  2. Developer manual, which describes the internal design and the fundamental building blocks of LIMES. This guide is suitable for those who want to either extend it or embed it into their own software products. It aims to deliver an overview of the architecture underlying our framework, explain the core concepts and give developers entry points into the java docs for further reading.

LIMES is open-source and available under a dual license at For further theoretical details about the approaches implemented in LIMES, please visit the project web site where you can find more than 30 peer-reviewed and published papers in top conferences and journals.