Human Schema Curation via Causal Association Rule Mining
This addresses the need for machine-readable event schemas in AI/NLP applications, though it appears incremental as it builds on existing schema construction approaches.
The paper tackles the problem of constructing event schemas (structured knowledge of typical scenarios) by developing a human-in-the-loop framework with a novel script induction system and interface, resulting in a released library of 232 detailed event schemas.
Event schemas are structured knowledge sources defining typical real-world scenarios (e.g., going to an airport). We present a framework for efficient human-in-the-loop construction of a schema library, based on a novel script induction system and a well-crafted interface that allows non-experts to "program" complex event structures. Associated with this work we release a schema library: a machine readable resource of 232 detailed event schemas, each of which describe a distinct typical scenario in terms of its relevant sub-event structure (what happens in the scenario), participants (who plays a role in the scenario), fine-grained typing of each participant, and the implied relational constraints between them. We make our schema library and the SchemaBlocks interface available online.