In this paper, we present our on-going work on ProbKB, a PROBabilistic Knowledge Base constructed from web-scale extracted entities, facts, and rules represented as a Markov logic network (MLN). We aim at web-scale MLN inference by designing a novel relational model to represent MLNs and algorithms that apply rules in batches. Errors are handled in a principled and elegant manner to avoid error propagation and unnecessary resource consumption. MLNs infer from the input a factor graph that encodes a probability distribution over extracted and inferred facts. We run parallel Gibbs sampling algorithms on Graphlab to query this distribution. Initial experiment results show promising scalability of our approach.
Authors:
Yang Chen, Daisy Zhe Wang
Bibtex:
@article{,
author = "Yang Chen, Daisy Zhe Wang",
title = "Web-Scale Knowledge Inference Using Markov Logic Networks",
journal = "ICML workshop on Structured Learning: Inferring Graphs from Structured and Unstructured Inputs",
year = "2013",
month = "June"
}