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Lecture Notes in Computer Science
We propose a new end-to-end method for extending a Knowledge Graph (KG) from tables. Existing techniques tend to interpret tables by focusing on information that is already in the KG, and therefore tend to extract many redundant facts. Our method aims to find more novel facts. We introduce a new technique for table interpretation based on a scalable graphical model using entity similarities. Our method further disambiguates cell values using KG embeddings as additional ranking method. Otherdoi:10.1007/978-3-030-30793-6_21 fatcat:ocfjoeu6nzfrnplfy7ibddhuv4