A Semantic Approach to the Academic Publishing Network: Document Vector Representations and Hybrid Structural-Semantic Fusion over OpenAlex Data

arXiv:2609.26218 · cs.IR, cs.CL, cs.DL · Submitted 2026-08-11 · Read on arXiv

cs.IR, cs.CL, cs.DL

Submitted: 2026-08-11

Updated: 2026-08-11

Code: https://github.com/lancedb/lancedb

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Embeddings — 34× in this paper · explained in 2 episodes
• Contrastive Learning — 4× in this paper · explained in 5 episodes
• Quantization — 4× in this paper · explained in 12 episodes
• Semantic Similarity — 4× in this paper · explained in 2 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Steering — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Vector Representations — 3× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Late Fusion — 2× in this paper · explained in 2 episodes
• RAG — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Anisotropy — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Knowledge graphs — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• The Faiss library
• From Local to Global: A Graph RAG Approach to Query-Focused Summarization
• NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models
• Towards General Text Embeddings with Multi-stage Contrastive Learning
• Text Embeddings by Weakly-Supervised Contrastive Pre-training

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