Knowledge Base Q&A is the task of answering questions from a knowledge base.
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The most approaches to Knowledge Base Question Answering are based on semantic parsing.
When answering natural language questions over knowledge bases (KBs), different question components and KB aspects play different roles.
We investigate entity linking in the context of a question answering task and present a jointly optimized neural architecture for entity mention detection and entity disambiguation that models the surrounding context on different levels of granularity.
SOTA for Entity Linking on WebQSP-WD
However, one critical problem is that current approaches only get high accuracy for questions whose relations have been seen in the training data.
Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection.