SMILE - SEmantIc roLe Extraction

The research question this proposal addresses is:

• Is it possible to create a Natural Language Understanding (NLU) system that can extract the components of a SPO’s Impact Model, including stakeholders, programs, services, outcomes and indicators, from unstructured text, in an explainable, tractable, governable, and verifiable way?

Our research objectives are to:

1. Develop an architecture that can extract Impact Model components from unstructured texts where the components are not explicitly stated but must be inferred from the text.
2. Extend existing social services ontologies (Fox et al., 2021a; Fox et al., 2021b; Gajderowicz et al., 2022b; Rosu et al., 2022; Fox et al., 2022), implemented as a knowledge graph, and are capable of storing learned information about Impact Models and responding to queries about learned knowledge.
3. Develop metrics for measuring the architecture’s correctness, tractability, explainability, and governance.