Matches in ScholarlyData for { ?s ?p Links between knowledge bases build the backbone of the Linked Data Web. Several time-efficient algorithms have hence been developed for computing links between knowledge bases. However, these approaches pay little attention to the fact that very large datasets cannot be held in the main memory of most computing devices. In this paper, we address this research gap by presenting a generic memory management approach that can by combined with any blocking- or filtering-based algorithm for Link Discovery. We show that the problem at hand is a variation of the Traveling Salesman Problem and is thus NP-complete. We thus provide efficient best-effort graph-based algorithms that allow scheduling link discovery tasks efficiently. We combine these algorithms with caching approaches that allow for loading required data from massive storage. Our evaluation on real data shows that our approach allows computing links between large amounts of resources efficiently even when using commodity hardware.. }
Showing items 1 to 1 of
1
with 100 items per page.
- 81 abstract "Links between knowledge bases build the backbone of the Linked Data Web. Several time-efficient algorithms have hence been developed for computing links between knowledge bases. However, these approaches pay little attention to the fact that very large datasets cannot be held in the main memory of most computing devices. In this paper, we address this research gap by presenting a generic memory management approach that can by combined with any blocking- or filtering-based algorithm for Link Discovery. We show that the problem at hand is a variation of the Traveling Salesman Problem and is thus NP-complete. We thus provide efficient best-effort graph-based algorithms that allow scheduling link discovery tasks efficiently. We combine these algorithms with caching approaches that allow for loading required data from massive storage. Our evaluation on real data shows that our approach allows computing links between large amounts of resources efficiently even when using commodity hardware.".