Column-Oriented Datalog Materialization for Large Knowledge Graphs
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
Abstract
The evaluation of Datalog rules over large Knowledge Graphs (KGs) is essential for many applications. In this paper, we present a new method of materializing Datalog inferences, which combines a column-based memory layout with novel optimization methods that avoid redundant inferences at runtime. The pro-active caching of certain subqueries further increases efficiency. Our empirical evaluation shows that this approach can often match or even surpass the performance of state-of-the-art systems, especially under restricted resources.
Details
Originalsprache | Englisch |
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Titel | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 |
Seiten | 258-264 |
Seitenumfang | 7 |
ISBN (elektronisch) | 9781577357605 |
Publikationsstatus | Veröffentlicht - 2016 |
Peer-Review-Status | Ja |
Publikationsreihe
Reihe | Proceedings of the AAAI Conference on Artificial Intelligence |
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Nummer | 1 |
Band | 30 |
ISSN | 2159-5399 |
Externe IDs
Scopus | 85007190432 |
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