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 assignments 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 is 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 will develop an interface between an existing IRL implementation and an existing knowledge-graph reasoning engine to test this hypothesis. Evaluation will be done on a corpus of sentences from social-historical scientific narratives against corresponding knowledge graphs with social-historical data.


Software: an interface between nat.lang. parsing software (IRL) and reasoning software (knowledge graphs)


Project Partners:

  • Stichting VU,
  • Universitat Pompeu Fabra (UPF),

Primary Contact: Frank van Harmelen, Stichting Vrije Universiteit Amsterdam

Main results of micro project:

This micro-project aims to build a bridge between a language processing system (incremental recruitment language (IRL)) and semantic memory (knowledge graphs), for building and parsing narratives.
In IRL, a sentence is represented as a network of logical constraints. Resolving the interpretation of a sentence comes down to finding a consistent assignment of entities from the real world that satisfy these constraints. In this microproject, we have used knowledge graphs and other open data repositories as an external source of world knowledge that can be used to bind and disambiguate entities in context.

We have implemented a new library called Web-Services that interacts, through the use of APIs, with several open data knowledge repositories, and integrates their semantic facts into language models such as IRL. Using the Web-Services library, users can write IRL programs that send requests to different open data APIs, or convert SPARQL queries into RESTful APIs using GRLC.

Contribution to the objectives of HumaneAI-net WPs

Natural language processing and understanding in machines often relies on statistical pattern recognition.
What is missing here is the ability of a machine to describe in a human understandable way how it came to a certain interpretation.
This would allow humans to take part in a machine’s reasoning process, thereby facilitating human-computer interaction and collaboration.
By using IRL, the interpretation of an utterance is transparently expanded and ambiguous entities are resolved until a single interpretation is found. At the same time, large datasets with semantic knowledge about the world exist in open repositories on the web. These repositories could be used in a similar way as we humans use our semantic memory, to disambiguate entities that cannot be resolved using the context of a dialogue alone.

Tangible outputs