[TMP-071] Linking language and semantic memory for building narratives
An interface linking IRL parsing technique with knowledge graphs to resolve sentence meanings using contextual information
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 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 developed 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.
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.
Tangible Outcomes
- Web-Services library – Steels, Luc & Van Harmelen, Frank & Van Trijp, Remi & Galletti, Martina & Kozakosczak, Jakub & Stork, Lise & Tiddi, Ilaria https://github.com/SonyCSLParis/web-services
- Video presentation summarizing the project
Partners
- Stichting VU, Frank.van.Harmelen@vu.nl
- Universitat Pompeu Fabra (UPF), luc.steels@upf.edu