TY - GEN
T1 - Template-based Question Answering analysis on the LC-QuAD2.0 Dataset
AU - Dileep, Akshay Kumar
AU - Mishra, Anurag
AU - Mehta, Ria
AU - Uppal, Siddharth
AU - Chakraborty, Jaydeep
AU - Bansal, Srividya K.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1
Y1 - 2021/1
N2 - In recent years, template-based question answer has picked up steam as a solution for evaluating RDF triples. Once we delve into the domain of template-based question answering, two important questions arise which are, the size of the dataset used as the knowledge base and the process of training used on that knowledge base. Previous studies attempted this problem with the LC-QuAD dataset and recursive neural network for training. This paper studies the same problem with a larger and newer benchmark dataset called LC-QuAD 2.0 and training using different machine learning models. The objective of this paper is to provide a comparative study using the newer LC-QuAD 2.0 dataset that has an updated schema and 30,000 question-answer pairs. Our study will focus on using and comparing two Machine Learning models and 3 different pre-processing techniques to generate results and identify the best model for this problem.
AB - In recent years, template-based question answer has picked up steam as a solution for evaluating RDF triples. Once we delve into the domain of template-based question answering, two important questions arise which are, the size of the dataset used as the knowledge base and the process of training used on that knowledge base. Previous studies attempted this problem with the LC-QuAD dataset and recursive neural network for training. This paper studies the same problem with a larger and newer benchmark dataset called LC-QuAD 2.0 and training using different machine learning models. The objective of this paper is to provide a comparative study using the newer LC-QuAD 2.0 dataset that has an updated schema and 30,000 question-answer pairs. Our study will focus on using and comparing two Machine Learning models and 3 different pre-processing techniques to generate results and identify the best model for this problem.
KW - Linked Data
KW - Machine Learning
KW - Question Answering
KW - Template-based question answering
UR - http://www.scopus.com/inward/record.url?scp=85102655046&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102655046&partnerID=8YFLogxK
U2 - 10.1109/ICSC50631.2021.00079
DO - 10.1109/ICSC50631.2021.00079
M3 - Conference contribution
AN - SCOPUS:85102655046
T3 - Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021
SP - 443
EP - 448
BT - Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Semantic Computing, ICSC 2021
Y2 - 27 January 2021 through 29 January 2021
ER -