The attribute classification model in intelligent question answering system based on domain knowledge graph

Yiming Sun; Dan Zhang; Chunsheng Xu

doi:10.1117/12.3021488

19 February 2024 The attribute classification model in intelligent question answering system based on domain knowledge graph

Yiming Sun, Dan Zhang, Chunsheng Xu

Author Affiliations +

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130632R (2024) https://doi.org/10.1117/12.3021488
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

The knowledge graph-based question answering system aims to find the connection between the internal knowledge behind the problem and the known knowledge base triplet. This paper proposes a framework for knowledge graph question answering (ZDNN-KGQA), which divides intelligent question answering based on knowledge graphs into four steps: named entity recognition, entity disambiguation, attribute classification, and answer selection. In the named entity recognition step, this paper proposes a BERT-BiLSTM-CRF model that combines an attention mechanism to identify entities in question sentences. In the entity disambiguation step, this paper utilizes the BERT-Softmax model to extract the accurate entities from the candidate entity set. In the attribute classification step, it is converted into a problem of text similarity. This paper proposes the use of BERT-Softmax and Word2vec models to extract attributes intext problems, effectively improving entity recognition performance and question semantic parsing performance. The average F1 value of this system on the test dataset provided by the NLPCC-ICCPOL 2016 KBQA task is 0.8974, which is close to the optimal performance level.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yiming Sun, Dan Zhang, and Chunsheng Xu "The attribute classification model in intelligent question answering system based on domain knowledge graph", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130632R (19 February 2024); https://doi.org/10.1117/12.3021488

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available