Metaannotation

Metaannotation is the practice of annotating annotations, i.e., adding metadata that describes or qualifies a prior annotation. It records information such as who performed the annotation, when, by what method, with what confidence, and what evidence supports it.

It serves to improve traceability, reproducibility, and interoperability in data annotation pipelines across domains such as natural language processing, bioinformatics, and digital humanities. By attaching metainformation to an annotation, downstream users can assess reliability, combine annotations from different sources, and audit the annotation process.

Common examples include tagging a textual span with a part-of-speech or named-entity label and providing a metaannotation that indicates the annotator, timestamp, tool version, inter-annotator agreement, and a confidence score. In gene or genomic annotation, metaannotations may encode the supporting evidence code, data source, and methodology used to derive the annotation.

Representations for metaannotations often use metadata frameworks or annotation models that can attach information to a statement. In RDF and semantic web contexts, metaannotations may be modeled via reification, named graphs, or provenance vocabularies to capture provenance, confidence, and version. They enable filtering, quality assessment, and cumulative annotation across datasets.

Challenges include standardizing vocabularies for metainformation, avoiding annotation bloat, and ensuring interoperability between systems. Proper design of metaannotations can reduce ambiguity and support auditing, provenance tracking, and more reliable data integration. See also annotation, metadata, provenance, and evidence in annotation practices.