CFP last date
20 January 2025
Reseach Article

Cognitive Computing based Question-Answering System for Teaching Electrical Motor Concepts

by Atul Prakash Prajapati, D. K. Chaturvedi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 45
Year of Publication: 2019
Authors: Atul Prakash Prajapati, D. K. Chaturvedi
10.5120/ijca2019919353

Atul Prakash Prajapati, D. K. Chaturvedi . Cognitive Computing based Question-Answering System for Teaching Electrical Motor Concepts. International Journal of Computer Applications. 178, 45 ( Sep 2019), 4-15. DOI=10.5120/ijca2019919353

@article{ 10.5120/ijca2019919353,
author = { Atul Prakash Prajapati, D. K. Chaturvedi },
title = { Cognitive Computing based Question-Answering System for Teaching Electrical Motor Concepts },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2019 },
volume = { 178 },
number = { 45 },
month = { Sep },
year = { 2019 },
issn = { 0975-8887 },
pages = { 4-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number45/30849-2019919353/ },
doi = { 10.5120/ijca2019919353 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:53:10.167277+05:30
%A Atul Prakash Prajapati
%A D. K. Chaturvedi
%T Cognitive Computing based Question-Answering System for Teaching Electrical Motor Concepts
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 45
%P 4-15
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Today is the era of ”Big Data”, and one has to spend ample amount of time to extract the meaningful information from such a huge store of data. It leads towards such a question answering system which can offer exact and precise answers to user queries. For that there is a requirement of understanding user queries effectively. Thus this paper proposes a cognitive computing powered question answering system in the field of education, which posses the power of Natural Language Processing (NLP). Here, cognitive computing provides the methods for synergism of several powers into a single architecture, NLP provides understanding of the user questions effectively, and Ontology endows with the techniques for the construction of robust knowledge base. So for the realistic implementation of the proposed architecture, the education domain has chosen and will be teaching electrical motor concepts to the edification of the students.

References
  1. Prajapati A.P., Chandiok A., Chaturvedi D.K. (2019), Semantic Network Based Cognitive, NLP Powered Question Answering System for Teaching Electrical Motor Concepts. In: Akoglu L., Ferrara E., Deivamani M., Baeza-Yates R., Yogesh P. (eds) Advances in Data Science, ICIIT 2018, Communications in Computer and Information Science, vol 941, 98-112, Springer, Singapore, doi: https://doi.org/10.1007/978-981-13-3582-2 8.
  2. Graesser, A.C., Hu, X., Nye, B.D. et al., ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics, IJ STEM Ed, 5:15, 2018, doi: https://doi.org/10.1186/s40594-018-0110-y.
  3. Hu, Ze and Zhang, Zhan and Yang, Haiqin and Chen, Qing and Zuo, Decheng (2017), A deep learning approach for predicting the quality of online health expert question-answering services, Journal of Biomedical Informatics,71, 2017, doi:10.1016/j.specom.2017.05.001.
  4. Jaya Kumar, Ashwini and Schmidt, Christoph and K¨ohler, Joachim (2017), A knowledge graph based speech interface for question answering systems, Speech Communication, 1–12, 92, 2017, doi:1016/j.specom.2017.05.001.
  5. Figueroa, Alejandro (2017), automatically generating effective search queries directly from community question-answering questions for finding related questions, Expert Systems with Applications, 11–19, 77, 2017, doi:10.1016/j.eswa.2017.01.041.
  6. Yue, Chunyi and Cao, Hanqiang and Xiong, Kun and Cui, Anqi and Qin, Haocheng and Li, Ming (2017), Enhanced question understanding with dynamic memory networks for textual question answering, Expert Systems with Applications, 39–45, 80, 2017, doi: 10.1016/j.eswa.2017.03.006.
  7. Peng, Peng and Zou, Lei and Qin, Zheng (2017), Answering top-K query combined keywords and structural queries on RDF graphs, Information Systems, 19–35, 67, 2017, doi: .1016/j.is.2017.03.002.
  8. Fu, Hongping and Niu, Zhendong and Zhang, Chunxia and Yu, Hanchao and Ma, Jing and Chen, Jie and Chen, Yiqiang and Liu, Junfa (2016), ASELM: Adaptive semi-supervised ELM with application in question subjectivity identification, Neurocomputing, 599–609, 207, 2016, doi:10.1016/j.neucom.2016.05.041.
  9. Stevens, Jon Scott and Benz, Anton and Reuße, Sebastian and Klabunde, Ralf (2016), Pragmatic question answering: A game-theoretic approach, Data & Knowledge Engineering, 52–69, 106,2016, doi: 10.1016/j.datak.2016.06.002.
  10. Olteeanu, Ana-Maria and Falomir, Zoe (2015), comRAT-C: A computational compound Remote Associates Test solver based on language data and its comparison to human performance, Pattern Recognition Letters, 81–90, 67, 2015, doi: 10.1016/j.patrec.2015.05.015.
  11. Momtazi, Saeedeh and Klakow, Dietrich (2017), Bridging the vocabulary gap between questions and answer sentences, Information Processing & Management, 595–615, 51, 2015, doi: 10.1016/j.ipm.2015.04.005.
  12. Neves, Mariana and Leser, Ulf (2015), Question answering for Biology, Methods, 36–46, 74, 2015, doi:10.1016/j.ymeth.2014.10.023.
  13. Shekarpour, Saeedeh and Marx, Edgard and Ngonga Ngomo, Axel-Cyrille and Auer, S¨oren (2015), SINA: Semantic interpretation of user queries for question answering on interlinked data, Web Semantics: Science, Services and Agents on the World Wide Web, 39–51, 30, 2015, doi: 10.1016/j.websem.2014.06.002.
  14. Procaci, Thiago Baesso and Siqueira, SeanWolfgand Matsui and Braz, Maria Helena Lima Baptista and Vasconcelos de Andrade, Leila Cristina (2015), How to find people who can help to answer a question? Analyses of metrics and machine learning in online communities, Computers in Human Behavior, 664–673, 51, 2015, doi:10.1016/j.chb.2014.12.026.
  15. Hattori, Lile and D’Ambros, Marco and Lanza, Michele and Lungu, Mircea (2013), Answering software evolution questions: An empirical evaluation, Information and Software Technology, 755–775,55, 2013, doi: 10.1016/j.infsof.2012.09.001.
  16. Heie, Matthias H. and Whittaker, Edward W.D. and Furui, Sadaoki (2012), A Question answering using statistical language modelling, Computer Speech & Language, 193–209, 26, 2012, doi: 10.1016/j.csl.2011.11.001.
  17. Prajapati A.P., Chaturvedi D.K. (2017) Ontology Based Knowledge Representation for Cognitive Decision Making in Teaching Electrical Motor Concepts, Silhavy R., Senkerik R., Kominkova Oplatkova Z., Prokopova Z., Silhavy P. (eds) Artificial Intelligence Trends in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, 573, 43-53, 2017, Springer Cham, doi: 10.1007/978-3-319-57261-1 5.
Index Terms

Computer Science
Information Sciences

Keywords

Question-Answering (QA) System NLP (Natural Language Processing) Ontology Education Domain (Basic Electrical Motor Concepts).