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On Evaluating Embedding Models for Knowledge Base Completion
[article]
2019
arXiv
pre-print
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
arXiv:1810.07180v4
fatcat:iuxewsnjwbf5fobr7yqfmu2ree