ANN: Silk - Link Discovery Framework Version 2.5 released

Hi all,

we are happy to announce version 2.5 of the Silk Link Discovery
Framework for the Web of Data.

The Silk framework is a tool for discovering relationships between
data items within different Linked Data sources. Data publishers can
use Silk to set RDF links from their data sources to other data
sources on the Web. Using the declarative Silk – Link Specification
Language (Silk-LSL), developers can specify the linkage rules data
items must fulfill in order to be interlinked. These linkage rules may
combine various similarity metrics and can take the graph around a
data item into account, which is addressed using an RDF path language.

Linkage rules can either be written manually or developed using the
Silk Workbench. The Silk Workbench, is a web application which guides
the user through the process of interlinking different data sources.

Version 2.5 includes the following additions to the last major release 2.4:

(1) Silk Workbench now includes a function to learn linkage rules from
the reference links. The learning function is based on genetic
programming and capable of learning complex linkage rules. Similar to
a genetic algorithm, genetic programming starts with a randomly
created population of linkage rules. From that starting point, the
algorithm iteratively transforms the population into a population with
better linkage rules by applying a number of genetic operators. As
soon as either a linkage rule with a full f-Measure has been found or
a specified maximum number of iterations is reached, the algorithm
stops and the user can select a linkage rule.

(2) A new sampling tab allows for fast creation of the reference link
set. It can be used to bootstrap the learning algorithm by generating
a number of links which are then rated by the user either as correct
or incorrect. In this way positive and negative reference links are
defined which in turn can be used to learn a linkage rule. If a
previous learning run has already been executed, the sampling tries to
generate links which contain features which are not yet covered by the
current reference link set.

(2) The new help sidebar provides the user with a general description
of the current tab as well as with suggestions for the next steps in
the linking process. As new users are usually not familiar with the
steps involved in interlinking two data sources, the help sidebar
currently provides basic guidance to the user and will be extended in
future versions.

(3) Introducing per-comparison thresholds:
- On popular request, thresholds can now be specified on each comparison.
- Backwards-compatible: Link specifications using a global threshold
can still be executed.

(4) New distance measures:
- Jaccard Similarity
- Dice’s coefficient
- DateTime Similarity
- Tokenwise Similarity, contributed by Florian Kleedorfer, Research
Studios Austria

(5) New data transformations:
- RemoveEmptyValues
- Tokenizer
- Merge Values of multiple inputs

(6) New DataSources and Outputs
- In addition to reading from SPARQL endpoints, Silk now also supports
reading from RDF dumps in all common formats. Currently the data set
is held in memory and it is not available in the Workbench yet, but
future versions will improve this.
- New SPARQL/Update Output: In addition to writing the links to a
file, Silk now also supports writing directly to a triple store using
SPARQL/Update.

(7) Various improvements and bugfixes

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More information about the Silk Link Discovery Framework is available at:

http://www4.wiwiss.fu-berlin.de/bizer/silk/

The Silk framework is provided under the terms of the Apache License,
Version 2.0 and can be downloaded from:

http://www4.wiwiss.fu-berlin.de/bizer/silk/releases/

The development of Silk was supported by Vulcan Inc. as part of its
Project Halo (www.projecthalo.com) and by the EU FP7 project
LOD2-Creating Knowledge out of Interlinked Data (http://lod2.eu/, Ref.
No. 257943).

Thanks to Christian Becker, Michal Murawicki and Andrea Matteini for
contributing to the Silk Workbench.

Happy linking,

Robert Isele, Anja Jentzsch and Chris Bizer

Received on Monday, 3 October 2011 13:19:39 UTC