AKSW has announced the latest release of their project, LIMES, “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 web interface. It can also be downloaded as standalone tool for carrying out link discovery locally.” According to the AKSW blog, “We could not resist the pleasure of making the demo of the new release candidate of LIMES (0.5RC1) available for all. LIMES 0.5 comes fitted with a new grammar for complex metric specification and fully novel algorithms.”

LIMES “implements novel time-efficient approaches for link discovery in metric spaces. Our approaches utilize the mathematical characteristics of metric spaces 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 description continues, “The general workflow implemented by the LIMES framework comprises four steps: Given a source, a target and a threshold, LIMES first computes a set exemplars for the target data source (step 1). This process is concluded by matching each target instance to the exemplar closest to it. In step 2 and 3, the matching is carried out. In the filterig step, the distance between all
source instances and target instances is approximated via the exemplars computed previously (step 3). Obvious non-matches are then filtered out. Subsequently, the real distance between the remaining source and target instances are computed (step 3). Finally, the matching instances are are serialized, i.e., written in a user-defined output stream according to a user-specified format, e.g. NTriples (step 4).”

Learn more and download a demo here.

Image: Courtesy AKSW

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