Dynamic Planning for Link Discovery
| Autor: | Daniel Obraczka, Axel-Cyrille Ngonga Ngomo, Kleanthi Georgala |
|---|---|
| Jazyk: | angličtina |
| Předmět: |
Computer science
Distributed computing 02 engineering and technology Plan (drawing) computer.file_format Dynamic planning 020204 information systems Scalability 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing State (computer science) RDF Link (knot theory) computer |
| Zdroj: | Lecture Notes in Computer Science Lecture Notes in Computer Science-The Semantic Web The Semantic Web ISBN: 9783319934167 ESWC The Semantic Web |
| ISSN: | 0302-9743 1611-3349 |
| DOI: | 10.1007/978-3-319-93417-4_16 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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