Constraint-Based Question Answering with Knowledge Graph

WebQuestions and SimpleQuestions are two benchmark data-sets commonly used in recent knowledge-based question answering (KBQA) work. Most questions in them are ‘simple’ questions which can be answered based on a single relation in the knowledge base. Such data-sets lack the capability of evaluating KBQA systems on complicated questions. Motivated by this issue, we release a new data-set, namely ComplexQuestions, aiming to measure the quality of KBQA systems on ‘multi-constraint’ questions which require multiple knowledge base relations to get the answer. Beside, we propose a novel systematic KBQA approach to solve multi-constraint questions. Compared to state-of-the-art methods, our approach not only obtains comparable results on the two existing benchmark data-sets, but also achieves significant improvements on the ComplexQuestions.

[1]  Oren Etzioni,et al.  Open question answering over curated and extracted knowledge bases , 2014, KDD.

[2]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[3]  Andrew Chou,et al.  Semantic Parsing on Freebase from Question-Answer Pairs , 2013, EMNLP.

[4]  Ming Zhou,et al.  Answering Questions with Complex Semantic Constraints on Open Knowledge Bases , 2015, CIKM.

[5]  Ming Zhou,et al.  Question Answering over Freebase with Multi-Column Convolutional Neural Networks , 2015, ACL.

[6]  Mark Steedman,et al.  Large-scale Semantic Parsing without Question-Answer Pairs , 2014, TACL.

[7]  Jason Weston,et al.  Question Answering with Subgraph Embeddings , 2014, EMNLP.

[8]  Tiejun Zhao,et al.  Knowledge-Based Question Answering as Machine Translation , 2014, ACL.

[9]  Jianfeng Gao,et al.  Modeling Interestingness with Deep Neural Networks , 2014, EMNLP.

[10]  Dongyan Zhao,et al.  Question Answering on Freebase via Relation Extraction and Textual Evidence , 2016, ACL.

[11]  Xuchen Yao,et al.  Information Extraction over Structured Data: Question Answering with Freebase , 2014, ACL.

[12]  Mark Steedman,et al.  Transforming Dependency Structures to Logical Forms for Semantic Parsing , 2016, TACL.

[13]  Jason Weston,et al.  Large-scale Simple Question Answering with Memory Networks , 2015, ArXiv.

[14]  Jonathan Berant,et al.  Semantic Parsing via Paraphrasing , 2014, ACL.

[15]  Xuedong Huang,et al.  An Overview of Microsoft Deep QA System on Stanford WebQuestions Benchmark , 2014 .

[16]  Yi Yang,et al.  S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking , 2015, ACL.

[17]  Christopher J. C. Burges,et al.  From RankNet to LambdaRank to LambdaMART: An Overview , 2010 .

[18]  Jason Weston,et al.  Open Question Answering with Weakly Supervised Embedding Models , 2014, ECML/PKDD.

[19]  Christopher Meek,et al.  Semantic Parsing for Single-Relation Question Answering , 2014, ACL.

[20]  Hae-Chang Rim,et al.  Joint Relational Embeddings for Knowledge-based Question Answering , 2014, EMNLP.

[21]  Yelong Shen,et al.  A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval , 2014, CIKM.

[22]  Joakim Nivre,et al.  Transition-based Dependency Parsing with Rich Non-local Features , 2011, ACL.

[23]  Christopher D. Manning,et al.  Generating Typed Dependency Parses from Phrase Structure Parses , 2006, LREC.

[24]  Jonathan Berant,et al.  Imitation Learning of Agenda-based Semantic Parsers , 2015, TACL.

[25]  Christopher D. Manning,et al.  Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.

[26]  Xuchen Yao,et al.  Lean Question Answering over Freebase from Scratch , 2015, NAACL.

[27]  Yelong Shen,et al.  Learning semantic representations using convolutional neural networks for web search , 2014, WWW.

[28]  Ming-Wei Chang,et al.  Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base , 2015, ACL.

[29]  Praveen Paritosh,et al.  Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.