LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia

2019 ◽  
pp. 69-78 ◽  
Author(s):  
Mohnish Dubey ◽  
Debayan Banerjee ◽  
Abdelrahman Abdelkawi ◽  
Jens Lehmann
Keyword(s):  
Question Answering ◽  
Large Dataset ◽  
Complex Question

scholarly journals Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base

2020 ◽  
Author(s):  
Yongrui Chen ◽  
Huiying Li ◽  
Yuncheng Hua ◽  
Guilin Qi
Keyword(s):  
Knowledge Base ◽  
Structure Prediction ◽  
Question Answering ◽  
State Transition ◽  
Knowledge Bases ◽  
Second Stage ◽  
Transition Strategy ◽  
Query Structure ◽  
Two Stages ◽  
Complex Question

Formal query building is an important part of complex question answering over knowledge bases. It aims to build correct executable queries for questions. Recent methods try to rank candidate queries generated by a state-transition strategy. However, this candidate generation strategy ignores the structure of queries, resulting in a considerable number of noisy queries. In this paper, we propose a new formal query building approach that consists of two stages. In the first stage, we predict the query structure of the question and leverage the structure to constrain the generation of the candidate queries. We propose a novel graph generation framework to handle the structure prediction task and design an encoder-decoder model to predict the argument of the predetermined operation in each generative step. In the second stage, we follow the previous methods to rank the candidate queries. The experimental results show that our formal query building approach outperforms existing methods on complex questions while staying competitive on simple questions.

scholarly journals Leveraging social Q&A collections for improving complex question answering

2015 ◽  
Vol 29 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Youzheng Wu ◽  
Chiori Hori ◽  
Hideki Kashioka ◽  
Hisashi Kawai
Keyword(s):  
Question Answering ◽  
Complex Question

LC-QuAD: A Corpus for Complex Question Answering over Knowledge Graphs

2017 ◽  
pp. 210-218 ◽  
Author(s):  
Priyansh Trivedi ◽  
Gaurav Maheshwari ◽  
Mohnish Dubey ◽  
Jens Lehmann
Keyword(s):  
Question Answering ◽  
Knowledge Graphs ◽  
Complex Question

scholarly journals Convolutional recurrent neural network with template based representation for complex question answering

2020 ◽  
Vol 10 (3) ◽  
pp. 2710
Author(s):  
A. Chandra Obula Reddy ◽  
K. Madhavi
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Question Answering ◽  
Internal Representation ◽  
Semantic Matching ◽  
Softmax Classifier ◽  
Questions And Answers ◽  
Fully Connected ◽  
The Given ◽  
Complex Question

Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier.

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