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Authors: Reham Mohamed ; Nagwa M. El-Makky and Khaled Nagi

Affiliation: Alexandria University, Egypt

ISBN: 978-989-758-272-1

Keyword(s): Question Answering, Relation Extraction, Linked Data.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Natural Language Processing ; Pattern Recognition ; Symbolic Systems

Abstract: Question Answering over knowledge-based data is one of the most important Natural Language Processing tasks. Despite numerous efforts that have been made in this field, it is not yet in the mainstream. Question Answering can be formulated as a Relation Extraction task between the question focus entity and the expected answer. Therefore, it requires high accuracy to solve a dual problem where the relation and answer are unknown. In this work, we propose a HybQA, a Hybrid Relation Extraction system to provide high accuracy for the Relation Extraction and the Question Answering tasks over Freebase. We propose a hybrid model that combines different types of state-of-the-art deep networks that capture the relation type between the question and the expected answer from different perspectives and combine their outputs to provide accurate relations. We then use a joint model to infer the possible relation and answer pairs simultaneously. However, since Relation Extraction might still be prone to errors due to the large size of the knowledge-base corpus (Freebase), we finally use evidence from Wikipedia as an unstructured knowledge base to select the best relation-answer pair. We evaluate the system on WebQuestions data and show that the system achieves a statistical significant improvement over the existing state-of-the-art models and provides the best accuracy which is 57%. (More)

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Paper citation in several formats:
Mohamed, R.; M. El-Makky, N. and Nagi, K. (2017). HybQA: Hybrid Deep Relation Extraction for Question Answering on Freebase.In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, ISBN 978-989-758-272-1, pages 128-136. DOI: 10.5220/0006589301280136

@conference{keod17,
author={Reham Mohamed. and Nagwa M. El{-}Makky. and Khaled Nagi.},
title={HybQA: Hybrid Deep Relation Extraction for Question Answering on Freebase},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD,},
year={2017},
pages={128-136},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006589301280136},
isbn={978-989-758-272-1},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD,
TI - HybQA: Hybrid Deep Relation Extraction for Question Answering on Freebase
SN - 978-989-758-272-1
AU - Mohamed, R.
AU - M. El-Makky, N.
AU - Nagi, K.
PY - 2017
SP - 128
EP - 136
DO - 10.5220/0006589301280136

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