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Could someone explain what differentiates the Google Knowledge Graph from a Markov Logic Network implementation? Is that what it is? For reference: https://sit

• nl 11y ago
  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). [1] http://www.cs.ubc.ca/~murphyk/Papers/kv-kdd14.pdf http://www.cs.ubc.ca/~murphyk/Papers/kv-kdd14.pdf [2] http://deepdive.stanford.edu/doc/general/kbc.html http://deepdive.stanford.edu/doc/general/kbc.html