Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion
Topics: Data Mining, Entity based search, Knowledge Graph, Semantic Search
The paper introduces Knowledge Vault (KV), a web-scale probabilistic knowledge base combining web content extractions with existing knowledge repositories using supervised machine learning for knowledge fusion. KV aims to enhance knowledge base completeness and correctness by leveraging probabilistic inference systems.
Knowledge Vault represents a significant advancement in the automatic construction of knowledge bases by integrating machine learning and probabilistic models to handle web-scale data. It addresses the challenges of noise in data extraction and incomplete knowledge bases, making it a robust tool for improving information retrieval systems and supporting various applications like search and question answering.
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