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.

In this paper, we introduce USR Bank 1.0, a multi-layered, text-level semantic representation framework designed to capture not only the predicate-argument structure of an utterance but also the speaker’s communicative intent as expressed linguistically. Built on the Universal Semantic Grammar (USG), which is grounded in Pāṇinian grammar and the Indian Grammatical Tradition (IGT), USR systematically encodes semantic, morpho-syntactic, discourse, and pragmatic information across distinct layers. In the USR generation process, initial USRs are automatically generated using a dedicated USR-builder tool and subsequently validated via a web-based interface (SAVI), ensuring high inter-annotator agreement and semantic fidelity. Our evaluation on Hindi texts demonstrates robust dependency and discourse annotation consistency and strong semantic similarity in USR-to-text generation. By distributing semantic-pragmatic information across layers and capturing the speaker’s perspective, USR provides a cognitively motivated, language-agnostic framework with promising applications in multilingual natural language processing.