Linking Language and Semantic Memory for Building Narratives

Abstract
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 consistent assignments of variables that satisfy 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.
In this micro project, we have developed an interface between an existing Incremental Recruitment Language (IRL) implementation and knowledge graphs.

Status
Finished

Progress
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. We evaluated the library on a case study about the Florentine Catasto of 1427.

Outputs
● New Lisp Library released under Apache License
● Case Study on the data story about the Florentine Catasto of 1427

Publications
Ongoing

Team
Steels L. (VIU)
Van Harmelen F., Stork L., Tiddi I.(VUA)
Van Trijp R., Galletti M., Kozakosczak J. (Sony CSL)

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Meaning and Understanding
in Human-centric
Artificial Intelligence

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 951846