MLNs are one possible way to implement the inference component that any knowledge graph needs.
Google's Knowledge Vault uses a fusion of a number of different extraction methods. Their exact methodology is laid out in their "Knowledge Vault" paper[1].
If you want to go deeper (ha!), then Deep Dive[2] is open source, and pretty much state-of-the art. It does inference using Gibbs sampling on a Factor Graph (Markov models/MLNs can be represented as Factor Graphs).
Google's Knowledge Vault uses a fusion of a number of different extraction methods. Their exact methodology is laid out in their "Knowledge Vault" paper[1].
If you want to go deeper (ha!), then Deep Dive[2] is open source, and pretty much state-of-the art. It does inference using Gibbs sampling on a Factor Graph (Markov models/MLNs can be represented as Factor Graphs).
[1] http://www.cs.ubc.ca/~murphyk/Papers/kv-kdd14.pdf
[2] http://deepdive.stanford.edu/doc/general/kbc.html