Abstract
Question answering over knowledge bases is a challenging task for next-generation search engines. The core of this task is to understand the meaning of questions and translate them into structured language-based queries. Previous research has focused on a specific knowledge base with a constrained domain, but with the increase in the size and domain of existing knowledge bases, fulfilling this aim is even more challenging. This article introduces the mainstream methods for question answering over knowledge bases, describing typical semantic meaning representation models and state-of-the-art systems for converting questions to predefined logical forms. It also puts a particular focus on the approaches for question answering over a large-scale knowledge base and multiple heterogeneous knowledge bases.
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