OKB Canonicalization

Open Knowledge Base (OKB) canonicalization aims to reduce redundancy and ambiguity in large, automatically-generated knowledge bases by grouping synonymous noun and relation phrases. Current research focuses on improving canonicalization accuracy through multi-task learning frameworks that integrate clustering algorithms (like K-means and its variants) with knowledge graph embeddings, often leveraging multi-view approaches incorporating contextual information. These advancements are crucial for enhancing the quality and usability of OKBs, which underpin numerous knowledge-driven applications, including web search and mobile computing. The development of robust canonicalization techniques is therefore vital for improving the reliability and efficiency of these applications.