New method measures semantic similarity between knowledge graphs using embeddings

Researchers have developed a new method to measure semantic similarity between knowledge graphs (KGs), addressing the limitations of existing approaches that primarily focus on entities, relations, and triples. The proposed technique evaluates graph-level semantics by comparing KGs based on their underlying information, rather than just structural patterns. Experiments using a custom semantic matching dataset derived from text documents demonstrated that the new KG embedding-based approach, particularly the EmbPairSim scoring function, outperforms traditional methods like Sentence-BERT in capturing graph-to-graph semantic similarity. AI

IMPACT This research could lead to more effective methods for comparing and understanding large knowledge graphs, potentially improving downstream AI applications that rely on structured knowledge.

RANK_REASON The cluster contains an academic paper detailing a new method for evaluating knowledge graph embeddings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• AvgEmbSim
• CC-News
• EmbPairSim
• graph-to-graph semantic similarity
• KG embedding
• knowledge graph
• Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
• WikiText-2

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →