Cleaning Inconsistencies in Information Extraction via Prioritized Repairs

The population of a predefined relational schema from textual con-tent, commonly known as Information Extraction (IE), is a per-vasive task in contemporary computational challenges associated with Big Data. Since the textual content varies widely in nature and structure (from machine logs to informal natural language), it is notoriously difficult to write IE programs that extract the sought information without any inconsistencies (e.g., a substring should not be annotated as both an address and a person name). Dealing with inconsistencies is hence of crucial importance in IE systems. Industrial-strength IE systems like GATE and IBM SystemT there-fore provide a built-in collection of cleaning operations to remove inconsistencies from extracted relations. These operations, how-ever, are collected in an ad-hoc fashion through use cases. Ideally, we would like to allow IE developers to declare their own poli