Abstract
We present the ARCHIMEDES system for efficient query processing over probabilistic knowledge bases. We design ARCHIMEDES for knowledge bases containing incomplete and uncertain information due to limitations of information sources and human knowledge. Answering queries over these knowledge bases requires efficient probabilistic inference. In this paper, we describe ARCHIMEDES's efficient knowledge expansion and querydriven inference over UDA-GIST, an in-database unified data- and graph-parallel computation framework. With an efficient inference engine, ARCHIMEDES produces reasonable results for queries over large uncertain knowledge bases. We use the Reverb-Sherlock andWikilinks knowledge bases to show ARCHIMEDES achieves satisfactory quality with real-time performance.
Publisher
Association for Computing Machinery (ACM)
Subject
Information Systems,Software
Cited by
2 articles.
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1. Effectively Representing Short Text via the Improved Semantic Feature Space Mapping;Lecture Notes in Computer Science;2019
2. A Demonstration of Sya;Proceedings of the 2018 International Conference on Management of Data;2018-05-27