On Evaluating Embedding Models for Knowledge Base Completion

Understand

Knowledge bases contribute to many web search and mining tasks, yet they are often incomplete.

• To add missing facts to a given knowledge base, various embedding models have been proposed in the recent literature.
• Perhaps surprisingly, relatively simple models with limited expressiveness often performed remarkably well under today's most commonly used evaluation protocols.
• In this paper, we explore whether recent models work well for knowledge base completion and argue that the current evaluation protocols are more suited for question answering rather than knowledge base completion.