ArchimedesOne

Knowledge bases are becoming increasingly important in structuring and representing information from the web. Meanwhile, web-scale information poses significant scalability and quality challenges to knowledge base systems. To address these challenges, we develop a probabilistic knowledge base system, A rchimedes O ne , 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 A rchimedes O ne system to showcase its efficient query and inference engines. The demonstration serves two purposes: 1) to provide an interface for users to interact with A rchimedes O ne 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 A rchimedes O ne with state-of-the-art approaches using two knowledge bases: NELL-sports with 4.5 million facts and Reverb-Sherlock with 15 million facts.