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Inference capabilities

Once you have data organized as a graph, there are several ways to perform inference, which is a core capability of AI systems.

A general definition for inference is:

a conclusion reached on the basis of evidence and reasoning

For the W3C perspective, see https://www.w3.org/standards/semanticweb/inference

  • improve the quality of data integration
  • discover new relationships
  • indentify potential inconsistencies in the (integrated) data

The integrations within kglab to support inference capabilities may be combined to leverage each other's relative strengths, along with potential use of human-in-the-loop (or "machine teaching") approaches such as active learning and weak supervision.

These integrations include:

  • Efforts by owlrl toward OWL 2 RL reasoning

    • adding axiomatic triples based on OWL properties
    • forward chaining using RDF Schema
  • Expanding the semantic relationships in SKOS for inference based on hierarchical transitivity and associativity

  • Machine learing models in general, and neural networks in particular can be viewed as means for function approximation, i.e., generalizing from data patterns to predict values or labels. In that sense, graph embedding approaches such as node2vec provides inference capabilities.

  • The probabilisic soft logic in pslpython evaluates systems of rules to infer predicates.

  • Using pgmpy for statistical inference in Bayesian networks.


Last update: 2020-12-25