Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pages 1321–1331, Beijing, China, July 26-31, 2015. c⃝2015 Association for Computational Linguistics Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base Wen-tau Yih Ming-Wei Chang Xiaodong He Jianfeng Gao Microsoft Research Redmond, WA 98052, USA {scottyih,minchang,xiaohe,jfgao}@microsoft.com Abstract We propose a novel semantic parsing framework for question answering using a knowledge base. We define a query graph that resembles subgraphs of the knowl- edge base and can be directly mapped to a logical form. Semantic parsing is re- duced to query graph generation, formu- lated as a staged search problem. Unlike traditional approaches, our method lever- ages the knowledge base in an early stage to prune the search space and thus simpli- fies the semantic matching problem. By applying an advanced entity linking sys- tem and a deep convolutional neural net- work model that matches questions and predicate sequences, our system outper- forms previous methods substantially, and achieves an F1 measure of 52.5% on the WEBQUESTIONS dataset. 1 Introduction Organizing the world’s facts and storing them in a structured database, large-scale knowledge bases (KB) like DBPedia (Auer et al., 2007) and Freebase (Bollacker et al., 2008) have become important resources for supporting open-domain question answering (QA). Most state-of-the-art approaches to KB-QA are based on semantic pars- ing, where a question (utterance) is mapped to its formal meaning representation (e.g., logical form) and then translated to a KB query. The answers to the question can then be retrieved simply by exe- cuting the query. The semantic parse also provides a deeper understanding of the question, which can be used to justify the answer to users, as well as to provide easily interpretable information to devel- opers for error analysis. However, most traditional approaches for se- mantic parsing are largely decoupled from the knowledge base, and thus are faced with sev- eral challenges when adapted to applications like QA. For instance, a generic meaning represen- tation may have the ontology matching problem when the logical form uses predicates that differ from those defined in the KB (Kwiatkowski et al., 2013). Even when the representation language is closely related to the knowledge base schema, finding the correct predicates from the large vo- cabulary in the KB to relations described in the utterance remains a difficult problem (Berant and Liang, 2014). Inspired by (Yao and Van Durme, 2014; Bao et al., 2014), we propose a semantic parsing frame- work that leverages the knowledge base more tightly when forming the parse for an input ques- tion. We first define a query graph that can be straightforwardly mapped to a logical form in λ- calculus and is semantically closely related to λ- DCS (Liang, 2013). Semantic parsing is then re- duced to query graph generation, formulated as a search problem with staged states and actions. Each state is a candidate parse in the query graph representation and each action defines a way …

Content truncated to 500 words