Neural Question Answering over Knowledge Graphs

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Questions in real-world scenarios are mostly factoid, such as ``any universities in Seattle?''. In order to answer factoid questions, a system needs to extract world knowledge and reason over facts. Knowledge graphs (KGs), e.g., Freebase, NELL, YAGO etc, provide large-scale structured knowledge for factoid question answering. What we do is usually parsing the raw questions into path queries of KGs. This talk introduces three pieces of work in different abstraction levels to handle this challenge: i) In case a path query, containing the topical entity and relation chain referred by a question, is available precisely in a KG, how to perform effective path query answering over KGs directly -- KGs usually suffer from severe sparsity. The first part of this talk presents three sequence-to-sequence models for path query answering and vector space learning of KG elements (entities & relations); ii) As questions in reality are raw text and mostly contain single-relation, the second part of this talk presents an effective entity linker and an attentive max-pooling based convolutional neural network to conduct (question, single KG fact) match, which enables the system to pick the best KG fact -- a one-hop path query -- to retrieve the answer; iii) Subsequently, the final part shows how to make improvements over single-relation KGQA to handle the multi-relation KGQA problem -- projecting the multi-relation question into a multi-hop path query for answer retrieval.



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