Research map / Graph learning / Knowledge and temporal graphs
A Joint Model for Entity Set Expansion and Attribute Extraction from Web Search Queries
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add attribute nodes and entity-attribute reinforcement to entity-only graph smoothing (ablation) | graph reinforcement model · Entity set expansion, average | P@10: 0.74 -> 0.90 | Helped |
| add attribute nodes and entity-attribute reinforcement to entity-only graph (ablation) | graph reinforcement model · Entity set expansion, average | P@20: 0.64 -> 0.75 | Helped |
| add attribute nodes and entity-attribute reinforcement to entity-only graph (ablation) | graph reinforcement model · Entity set expansion, average | P@30: 0.56 -> 0.68 | Helped |
| joint model vs bootstrapping | Bootstrap · Entity set expansion, average | P@30: 0.59 -> 0.68 | Helped |
| joint model vs Nearest Neighbors | Nearest Neighbors · Entity set expansion, average | P@10: 0.84 -> 0.90 | Helped |
| joint model vs Nearest Neighbors | Nearest Neighbors · Entity set expansion, average | P@20 and P@30: 0.75 -> 0.75; 0.67 -> 0.68 | No clear effect |
| joint model vs Nearest Neighbors | Nearest Neighbors · Entity set expansion, USA city | P@20: 0.95 -> 0.65 | Hurt |
| joint model vs pipeline attribute extraction | Pipeline method · Attribute extraction, average | P@10: 0.60 -> 0.74 | Helped |
| joint model vs pipeline attribute extraction | Pipeline method · Attribute extraction, average | P@30: 0.52 -> 0.62 | Helped |
Closest papers by meaning
- Select and Augment: Enhanced Dense Retrieval Knowledge Graph Augmentation (Abstract Reprint) (AAAI 2024 · similarity 0.83)
- An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction (AAAI 2024 · similarity 0.82)
- Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction (AAAI 2024 · similarity 0.82)
- MESED: A Multi-Modal Entity Set Expansion Dataset with Fine-Grained Semantic Classes and Hard Negative Entities (AAAI 2024 · similarity 0.82)
- Knowledge Graph Completion with Relation-Aware Anchor Enhancement (AAAI 2025 · similarity 0.82)
- DocKS-RAG: Optimizing Document-Level Relation Extraction through LLM-Enhanced Hybrid Prompt Tuning (ICML 2025 · similarity 0.81)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.