SAVI: Web-based Multilayered Semantic Annotation Validation Interface

Abstract

This paper presents SAVI, a web-based interface for multilayer semantic annotation validation of Universal Semantic Representation (USR). USR encodes meaning across interdependent lexical, constructional, relational, discourse, and co-reference layers, making validation challenging using conventional annotation tools. SAVI addresses this limitation through structured tab-based layer separation, constraint-aware editing mechanisms, and role-based review workflows. The system integrates a multilingual concept dictionary to ensure sense-level consistency, along with a Hindi text-generation module and dependency-based visualization to support interpretation and correction. SAVI is implemented using a Flask backend, Flutter frontend, and PostgreSQL for structured data management. Evaluation results demonstrate effective governance of concept proposals and improved efficiency in multilayer USR correction, positioning SAVI as a structured validation framework for scalable semantic corpus development.

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