LabelingFolgen

LabelingFolgen is a conceptual framework for annotating the consequences that follow events in sequential data or narratives. The term combines labeling, the tagging of events with descriptive tags, and Folgen, the German word for consequences or follow-on effects, reflecting its emphasis on causal outcomes within sequences. The framework is used to capture how specific events lead to subsequent outcomes across different domains.

In formal terms, a sequence of events E = (e1, e2, ..., en) is paired with a labeling function L that assigns to each event ei a label from a predefined set C of consequence types. Typical labels include direct consequence, indirect consequence, enabling, preventive, contradictory, and uncertain. Some implementations also record relationships between events, such as precedence, dependency, or conditional triggering, forming a labeled sequence graph that encodes both event order and consequence type.

LabelingFolgen can be applied in manual and automated settings. Analysts manually annotate texts or process logs to reveal causal chains, while automated approaches use supervised learning to propagate labels from annotated examples to new data. Evaluation often relies on label accuracy, inter-annotator agreement, and task-specific metrics that measure the correctness of both the labeling and the inferred causal links.

Applications span several fields. In narrative analysis, it helps map storytelling causal structures; in process mining, it annotates steps with their downstream effects; in legal and policy contexts, it supports reasoning about outcomes of actions and compliance implications. The concept is related to causal labeling, event extraction, and graph labeling, but focuses specifically on the characterization of consequences rather than mere event attributes. Limitations include subjectivity in determining consequences, domain dependence, and challenges in handling ambiguous or multi-causal scenarios.