ERlike

erlike is a neologism used in theoretical discussions of data modeling and artificial intelligence to describe representations that are “event-relations-like.” The term blends “event” and “relation” with the common -like suffix to signal a data paradigm in which events and the relations among them take priority over static objects and attributes.

In an erlike representation, data are organized around events—defined occurrences with participants, timestamps, and attributes—and the relations that link these events, such as temporal order, causality, participation, or thematic connections. This contrasts with traditional object-centric graphs where nodes primarily represent entities and edges encode their static relationships. Implementations are described in terms of erlike graphs (nodes as events, edges as relations), erlike tables, or erlike schemas that specify temporal predicates and narrative links.

Applications of erlike concepts include event sequence reasoning, temporal query answering, plan recognition, and narrative understanding. They are used to model domains such as medical treatment episodes, user interaction logs, or any scenario where understanding the flow and interdependence of events is essential.

Reception and usage are currently informal and somewhat provisional. The term is not widely standardized and often overlaps with established concepts such as temporal graphs, event-centric modeling, or narrative graphs. Some researchers adopt erlike as a clarifying label for event-focused representations, while others prefer the conventional terminology.

See also: temporal graphs, event-centric modeling, narrative graphs, knowledge graphs.