IRL, developed by Luc Steels and collaborators, is a parsing technique that captures the semantics of a natural language expression as a network of logical constraints. Determining the meaning of a sentence then amounts to finding a consistent assignment of variables that satisfies these constraints.

Typically, such meaning can only be determined (i.e. such constraints can only be resolved) by using the context ("narrative") in which the sentence is to be interpreted. The central hypothesis of this project was that modern large-scale knowledge graphs are a promising source of such contextual information to help resolve the correct interpretation of a given sentence.

We developed an interface between an existing IRL implementation and an existing knowledge-graph reasoning engine to test this hypothesis. Evaluation has been done on a corpus of sentences from social-historical scientific narratives against corresponding knowledge graphs with social-historical data.