From Rules to Neural Graphs: Scalable Structured Prediction for Patent Prior Art Search
cs.IR, cs.CL
Submitted: 2026-10-01
Updated: 2026-10-01
Comments: Accepted for publication at the ECML PKDD 2026 conference (Applied Data Science track)
Code: https://github.com/iprally/invention_graph_eval
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Patent Search Using Triplet Networks Based Fine-Tuned SciBERT
- Longformer: The Long-Document Transformer
- A Generalization of Transformer Networks to Graphs
- BERT based patent novelty search by training claims to their own description
- PaECTER: Patent-level Representation Learning using Citation-informed Transformers
- Distilling the Knowledge in a Neural Network
- PatenTEB: A Comprehensive Benchmark and Model Family for Patent Text Embedding
- Do Transformers Really Perform Bad for Graph Representation?
- Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
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