Terich

TErich is a framework and metric in information systems used to quantify the richness and usefulness of digital content. TErich is commonly presented as Technology-Enhanced Rich Information, reflecting its aim to capture how effectively a document or dataset communicates its subject matter beyond basic length or frequency counts.

The TErich approach combines several sub-metrics. Linguistic richness assesses vocabulary variety and syntactic diversity. Semantic coverage measures breadth and depth of topics and concepts represented. Alignment with user intent evaluates how well the content supports typical user goals and tasks. A composite TErich score is usually derived by normalizing each component and applying domain-informed weights to produce a single, interpretable value.

Applications of TErich include improving content ranking and retrieval, guiding document summarization, enriching knowledge graphs, and conducting content quality audits. In practice, TErich signals are intended to complement traditional metrics such as term frequency or perplexity, helping systems favor content that provides broader context, practical usefulness, and clearer information pathways for users.

Development and evaluation of TErich concepts have progressed through information science and data analytics research in the 2020s, with adoption seen across search engines, digital libraries, and content platforms. Limitations include computational cost, potential biases introduced by weighting schemes, and a reliance on high-quality annotations or ground-truth data. Ongoing work aims to standardize scoring, enable cross-lingual applicability, and improve integration with multimodal content.

See also: information quality, content richness, data quality metrics, information retrieval, and text analytics.