Column-Oriented Datalog Materialization for Large Knowledge Graphs
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
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
Original language | English |
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Title of host publication | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 |
Pages | 258-264 |
Number of pages | 7 |
ISBN (electronic) | 9781577357605 |
Publication status | Published - 2016 |
Peer-reviewed | Yes |
Publication series
Series | Proceedings of the AAAI Conference on Artificial Intelligence |
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Number | 1 |
Volume | 30 |
ISSN | 2159-5399 |
External IDs
Scopus | 85007190432 |
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