arXiv (All)
open access
Pattern Over-Generalization of Knowledge Graph Embedding
Knowledge graph embedding (KGE) demonstrates its effectiveness for predicting missing links in knowledge graphs (KGs) by projecting entities and relations into a low-dimensional vector space. It is crucial for KGE models to effectively capture inference patterns (patterns) inherent in KGs, such as s
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs#1049699
- A hierarchy of faithfulness criteria for knowledge base completion#1064407
- When Successful Knowledge Graph Edits Displace Correct Answers: Rank-Level Locality beyond Parameter Support#1000085
- Efficient Dense Vector Search within Knowledge Graph Content Embeddings#1050419
- Efficient Dense Vector Search within Knowledge Graph Content Embeddings#1028790
Record · ID 926403
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive