LYNX: A Scalable 3D Semantic Knowledge Galaxy for Interactive Information Exploration

LYNX (Linked Knowledge Network Explorer) is a semantic mapping and visualization platform designed to transform knowledge exploration through an immersive 3D galaxy metaphor. The system integrates semantic embeddings, graph theory, and 3D rendering to reveal conceptual relationships across diverse knowledge domains. LYNX processes data from sources such as Wikipedia and arXiv, generating 384-dimensional embeddings via Sentence-BERT to compute semantic similarity between concepts. The resulting graph is visualized in an interactive WebGL environment that supports semantic search, dynamic navigation, and progressive Level-of-Detail rendering for scalable performance. The platform demonstrates sub-200 ms search latency and sustained 60 FPS visualization for datasets exceeding 10,000 concepts. This paper details the architecture, implementation, and evaluation of the LYNX system, showcasing its potential applications in research, education, and information discovery.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-08
DOI
https://doi.org/10.5281/zenodo.17229302
Primary Topic
Latin American history and culture
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

LYNX: A Scalable 3D Semantic Knowledge Galaxy for Interactive Information Exploration

Zenodo (CERN European Organization for Nuclear Research)
Latin American history and culture
article

LYNX: A Scalable 3D Semantic Knowledge Galaxy for Interactive Information Exploration

article en

Abstract

LYNX (Linked Knowledge Network Explorer) is a semantic mapping and visualization platform designed to transform knowledge exploration through an immersive 3D galaxy metaphor. The system integrates semantic embeddings, graph theory, and 3D rendering to reveal conceptual relationships across diverse knowledge domains. LYNX processes data from sources such as Wikipedia and arXiv, generating 384-dimensional embeddings via Sentence-BERT to compute semantic similarity between concepts. The resulting graph is visualized in an interactive WebGL environment that supports semantic search, dynamic navigation, and progressive Level-of-Detail rendering for scalable performance. The platform demonstrates sub-200 ms search latency and sustained 60 FPS visualization for datasets exceeding 10,000 concepts. This paper details the architecture, implementation, and evaluation of the LYNX system, showcasing its potential applications in research, education, and information discovery.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 42%
Latin American history and culture
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.