NERVIS: An Interactive System for Graph-Based Exploration and Editing of Named Entities

2025-10-06

AI Summary

To address the need for interactive exploration and editing of named entities and their relationships within document collections, this paper proposes a tripartite graph representation model integrating documents, entity mentions, and entities as distinct node types. We design an interactive visualization system supporting coordinated multi-perspective analysis. Key innovations include a fuzzy-view mechanism and an editable graph structure, enabling users to dynamically refine entity relations, define novel associations, and receive real-time visual feedback. The system incorporates linked multi-view interfaces, hierarchical filtering, thumbnail-based navigation, and semantic-aware graph layout—balancing macro-level overviews with micro-level validation. It supports export of structured JSON data and high-resolution images. Evaluation demonstrates significant improvements in named entity clustering efficiency and relational inference accuracy, thereby enhancing human-AI collaboration for deep semantic understanding of textual relationships.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Natural Language Processing: Information Extraction

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

We present an interactive visualization system for exploring named entities and their relationships across document collections. The system is designed around a graph-based representation that integrates three types of nodes: documents, entity mentions, and entities. Connections capture two key relationship types: (i) identical entities across contexts, and (ii) co-locations of mentions within documents. Multiple coordinated views enable users to examine entity occurrences, discover clusters of related mentions, and explore higher-level entity group relationships. To support flexible and iterative exploration, the interface offers fuzzy views with approximate connections, as well as tools for interactively editing the graph by adding or removing links, entities, and mentions, as well as editing entity terms. Additional interaction features include filtering, mini-map navigation, and export options to JSON or image formats for downstream analysis and reporting. This approach contributes to human-centered exploration of entity-rich text data by combining graph visualization, interactive refinement, and adaptable perspectives on relationships.

Problem

• Explores named entities and relationships across document collections
• Integrates documents, entity mentions and entities in graph representation
• Supports interactive graph editing and exploration through coordinated views

Innovation

• Graph-based representation integrating documents and entities
• Interactive editing tools for flexible graph manipulation
• Multiple coordinated views with fuzzy connections for exploration