cuesthat

Cuesthat is a conceptual framework in information science for linking cues in text to the statements they support. It describes a way to annotate documents so that each claim is accompanied by cues that indicate the nature of the evidence, context, or relation. The term is a neologism used in academic discussions and is not a formal standard.

Originating in discussions about evidence tagging and explainable AI, cuesthat emphasizes traceability from a claim back to its supporting indicators. In this view, annotations are not limited to one sentence but form a small network that records how and why a conclusion was drawn.

Structure and mechanism: A cuesthat annotation typically includes a base statement, one or more cue tags (such as evidence type, context, or source category), and a linked claim or context. These relationships can be stored as metadata within a document store or as edges in a knowledge graph, enabling navigation from a conclusion to its supporting cues and sources.

Applications: Cuesthat supports fact-checking workflows, digital humanities research, and explainable AI by making evidence pathways explicit. It helps researchers audit how conclusions were reached and assists language models in surfacing relevant material when generating or evaluating claims.

Example: A sentence like “The intervention reduced symptoms by 20 percent” might be tagged with cues such as “randomized trial,” “statistical significance,” and linked to the claim “Clinical relevance demonstrated,” along with the study citation.

Limitations and challenges: Implementing a consistent cue taxonomy, mitigating subjectivity in cue assignment, and scaling annotations to large corpora are common concerns. Adoption benefits from interoperable formats and clear standards for cue types.

See also: evidence tagging, explainable AI, fact checking, knowledge graphs, annotation standards.