Paper 55 (Research track)

Dynamic Planning for Link Discovery

Author(s): Kleanthi Georgala, Daniel Obraczka, Axel-Cyrille Ngonga Ngomo

Abstract: With the growth of the number and the size of RDF datasets comes an
increasing need for scalable solutions to support the linking of resources. Most
Link Discovery frameworks rely on complex link specifications for this purpose.
We address the scalability of the execution of link specifications by presenting the
first dynamic planning approach for Link Discovery dubbed Condor. In contrast
to the state of the art, Condor can re-evaluate and reshape execution plans for
link specifications during their execution. Thus, it achieves significantly better
runtimes than existing planning solutions while retaining an F-measure of 100%.
We quantify our improvement by evaluating our approach on 7 datasets and 700
link specifications. Our results suggest that Condor is up to 2 orders of magnitude
faster than the state of the art and requires less than 0.1% of the total runtime of
a given specification to generate the corresponding plan.

Keywords: Link Discovery; Semantic Web; Linked Data; Planning; Scalability

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