Knowledge bases are becoming increasingly important in structuring and representing information from the web. Meanwhile, webscale information poses significant scalability and quality challenges to knowledge base systems. To address these challenges, we develop a probabilistic knowledge base system, ARCHIMEDESONE, by scaling up the knowledge expansion and statistical inference algorithms. We design a web interface for users to query and update large knowledge bases. In this paper, we demonstrate the ARCHIMEDESONE system to showcase its efficient query and inference engines. The demonstration serves two purposes: 1) to provide an interface for users to interact with ARCHIMEDESONE through load, search, and update queries; and 2) to validate our approaches of knowledge expansion by applying inference rules in batches using relational operations and query-driven inference by focusing computation on the query facts. We compare ARCHIMEDESONE with state-of-the-art approaches using two knowledge bases: NELL-sports with 4.5 million facts and Reverb-Sherlock with 15 million facts.
Authors:
Xiaofeng Zhou, Yang Chen, Daisy Zhe Wang
Bibtex:
@article{zhou2016archimedesone,
title={ArchimedesOne: query processing over probabilistic knowledge bases},
author={Zhou, Xiaofeng and Chen, Yang and Wang, Daisy Zhe},
journal={Proceedings of the VLDB Endowment},
volume={9},
number={13},
pages={1461--1464},
year={2016},
publisher={VLDB Endowment}
}
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