Dynamic Planning for Link Discovery

  • Kleanthi GeorgalaEmail author
  • Daniel Obraczka
  • Axel-Cyrille Ngonga Ngomo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10843)


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.


Link Discovery (LD) Dynamic Planning Approach Link Specification (LS) Canonical Plane Execution Engine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been supported by H2020 projects SLIPO (GA no. 731581) and HOBBIT (GA no. 688227) as well as the DFG project LinkingLOD (project no. NG 105/3-2) and the BMWI Projects SAKE (project no. 01MD15006E) and GEISER (project no. 01MD16014).


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.AKSW Research GroupUniversity of LeipzigLeipzigGermany
  2. 2.Data Science GroupPaderborn UniversityPaderbornGermany

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