  Making sense of familiar yet new situations typically involves making
generalizations about causal schemas, stories that help humans reason about
event sequences. Reasoning about events includes identifying cause and effect
relations shared across event instances, a process we refer to as causal schema
induction. Statistical schema induction systems may leverage structural
knowledge encoded in discourse or the causal graphs associated with event
meaning, however resources to study such causal structure are few in number and
limited in size. In this work, we investigate how to apply schema induction
models to the task of knowledge discovery for enhanced search of
English-language news texts. To tackle the problem of data scarcity, we present
Torquestra, a manually curated dataset of text-graph-schema units integrating
temporal, event, and causal structures. We benchmark our dataset on three
knowledge discovery tasks, building and evaluating models for each. Results
show that systems that harness causal structure are effective at identifying
texts sharing similar causal meaning components rather than relying on lexical
cues alone. We make our dataset and models available for research purposes.
