NERVIS: An Interactive System for Graph-Based Exploration and Editing of Named Entities
October 06, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
UroΕ‘ Ε majdek, Ciril Bohak
arXiv ID
2510.04971
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted