Skip to main content
SpringerLink
Log in

KG4ASTRA: question answering over Indian Missiles Knowledge Graph

  • Foundations
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Natural language, being unstructured, makes the tedious task for building a model to parse it into a query language successfully. An incorrect query for a particular question would lead to an absurd answer. As seen in many semantic parsing approaches, the inaccurate answering of complex questions increases significantly. This leads to many novel effective strategies that can apply to semantic parsing approaches to accurately parse complex natural language questions and generate an appropriate answer over a large knowledge graph. The defense system in a particular country is one of the most crucial components, and the lack of a proper defensive domain interface makes it a significant motivation. Hence, a knowledge graph has been constructed to collect and enquire about all information in one place. In this paper, KG4ASTRA has been designed with a Missile Knowledge Graph consisting of 177 entities linked using 400 relationships. A query-answering model then utilizes this manually created Missile Knowledge Graph, which generates tabular or graph representation for the natural language question. Neo4j platform has been used to prepare the knowledge graph, and Cypher queries are used to execute queries. The modeled knowledge has been evaluated by executing natural language queries on KG4ASTRA query-answering model and compared the search results with other existing knowledge graphs. As of our best knowledge, neither the knowledge graph nor the question-answering model has been designed for Indian Missiles. As a future scope, the proposed knowledge graph will be connected with the existing knowledge graph and extended to automatically extract domain-specific entities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. https://wiki.dbpedia.org/.

  2. http://rtw.ml.cmu.edu/rtw/.

  3. https://www.google.com/intl/bn/insidesearch/features/search/knowledge.html.

  4. https://searchengineland.com/library/bing/bing-satori.

  5. https://neo4j.com/use-cases/knowledge-graph/.

  6. https://neo4j.com/developer/cypher/.

  7. https://docs.google.com/spreadsheets/d/1vc61pgSpPXd2wD0-SByMVHKC_2znOboOVROxyaex4/edit#gid=0

  8. https://www.mod.gov.in/sites/default/files/MoDAR2018.pdf.

  9. https://github.com/shivu117/KG4ASTRA.

References

  • Abdelkawi A, Zafar H, Maleshkova M, Lehmann J (2019) Complex query augmentation for question answering over knowledge graphs. In: OTM Confederated International Conferences” On the Move to Meaningful Internet Systems” (pp. 571-587). Springer, Cham

  • Abujabal A, Yahya M, Riedewald M, Weikum G (2017) Automated template generation for question answering over knowledge graphs. In: Proceedings of the 26th international conference on world wide web (pp. 1191-1200)

  • Bordes A, Weston J, Usunier N (2014) Open question answering with weakly supervised embedding models. In: Joint European conference on machine learning and knowledge discovery in databases (pp. 165-180). Springer, Berlin, Heidelberg

  • Chen Y, Kuang J, Cheng D, Zheng J, Gao M, Zhou A (2019) AgriKG: an agricultural knowledge graph and its applications. In: International conference on database systems for advanced applications (pp. 533-537). Springer, Cham

  • Dubey M, Dasgupta S, Sharma A, Höffner K, Lehmann J (2016) Asknow: a framework for natural language query formalization in sparql. In: European Semantic Web Conference (pp. 300-316). Springer, Cham

  • Gaurav D, Tiwari SM, Goyal A, Gandhi N, Abraham A (2020) Machine intelligence-based algorithms for spam filtering on document labeling. Soft Comput 24(13):9625–9638

    Article  Google Scholar 

  • Gaurav D, Shandilya S, Tiwari SM, Goyal A (2020) A machine learning method for recognizing invasive content in memes. Knowledge graph and semantic web conference (KGSWC). Int Conf (In Press)

  • Gharibi M, Zachariah A, Rao P (2020) FoodKG: a tool to enrich knowledge graphs using machine learning techniques. Front Big Data 3:12. https://doi.org/10.3389/fdata

    Article  Google Scholar 

  • Graefe G, McKenna WJ (1993) The volcano optimizer generator: Extensibility and efficient search. In: Proceedings of IEEE 9th international conference on data engineering (pp. 209-218). IEEE

  • Gubichev A (2015) Query Processing and Optimization in Graph Databases (Doctoral dissertation, Technische Universität München)

  • Hogan A, Blomqvist E, Cochez M, d’Amato C, de Melo G, Gutierrez C, Zimmermann A (2020) Knowledge graphs

  • Lin ZQ, Xie B, Zou YZ, Zhao JF, Li XD, Wei J, Yin G (2017) Intelligent development environment and software knowledge graph. J Comput Sci Technol 32(2):242–249

    Article  Google Scholar 

  • Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space

  • Mishra S, Jain S (2019) An intelligent knowledge treasure for military decision support. Int J Web-Based Lear Teach Technol (IJWLTT) 14(3):55–75

    Article  Google Scholar 

  • Moerkotte G, Neumann T (2008) Dynamic programming strikes back. In: Proceedings of the 2008 ACM SIGMOD international conference on Management of data (pp. 539-552)

  • Neumann T (2011) Efficiently compiling efficient query plans for modern hardware. Proc VLDB Endow 4(9):539–550

    Article  Google Scholar 

  • Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 701-710)

  • Powers DM (2020) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

  • Ruan T, Huang Y, Liu X, Xia Y, Gao J (2019) QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research. BMC Med Inform Decis Mak 19(1):82

    Article  Google Scholar 

  • Selinger PG, Astrahan MM, Chamberlin DD, Lorie RA, Price TG (1979) Access path selection in a relational database management system. In: Proceedings of the 1979 ACM SIGMOD international conference on management of data (pp. 23-34)

  • Sheng M, Li A, Bu Y, Dong J, Zhang Y, Li X, Xing C (2020) DSQA: a domain specific QA system for smart health based on knowledge graph. In: International conference on web information systems and applications (pp. 215-222). Springer, Cham

  • Tiwari S, Al-Aswadi FN, Gaurav D (2021) Recent trends in knowledge graphs: theory and practice. Soft Comput 25(13):8337–8355

    Article  Google Scholar 

  • Tiwari S, Gaurav D, Srivastava A, Rai C, Abhishek K (2021) A preliminary study of knowledge graphs and their construction. In: Emerging technologies in data mining and information security: proceedings of IEMIS 2020, Volume 3 (pp. 11-20). Springer Singapore

  • Unger C, Bühmann L, Lehmann J, Ngonga Ngomo AC, Gerber D, Cimiano P (2012) Template-based question answering over RDF data. In: Proceedings of the 21st international conference on World Wide Web (pp. 639-648)

  • Villazón-Terrazas B, Ortiz-Rodríguez F, Tiwari SM, Shandilya SK. (2020) Knowledge Graphs and Semantic Web, ISBN: 978-3030653835

  • Walter S, Unger C, Cimiano P, Bar D (2012) Evaluation of a layered approach to question answering over linked data. In: International Semantic Web Conference (pp. 362-374). Springer, Berlin, Heidelberg

  • Wang H, Zhang F, Wang J, Zhao M, Li W, Xie X, Guo M (2018) Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM international conference on information and knowledge management (pp. 417-426)

  • Xu K, Feng Y, Huang S, Zhao D (2015) Question answering via phrasal semantic parsing. In: International conference of the cross-language evaluation forum for european languages (pp. 414-426). Springer, Cham

  • Yahya M, Berberich K, Elbassuoni S, Ramanath M, Tresp V, Weikum G (2012) Natural language questions for the web of data. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning (pp. 379-390)

  • Yao X, Van Durme B (2014) Information extraction over structured data: question answering with freebase. In: Proceedings of the 52nd annual meeting of the association for computational linguistics (Volume 1: Long Papers) (pp. 956-966)

  • Zou L, Huang R, Wang H, Yu JX, He W, Zhao D (2014) Natural language question answering over RDF: a graph data driven approach. In: Proceedings of the 2014 ACM SIGMOD international conference on Management of data (pp. 313-324)

Download references

Funding

This study was not funded by any grant

Author information

Authors and Affiliations

  1. Jaypee Institute of Information Technology, Noida, India

    Shivansh Gupta

  2. Universidad Autonoma de Tamaulipas, Ciudad Victoria, Mexico

    Sanju Tiwari & Fernando Ortiz-Rodriguez

  3. Vidyabharti Trust College of BCA, Bardoli, Gujarat, India

    Ronak Panchal

Authors
  1. Shivansh Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanju Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fernando Ortiz-Rodriguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ronak Panchal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanju Tiwari.

Ethics declarations

Conflict of interest

There is no conflict of interest.

Human and animal participants

No animals were involved. This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gupta, S., Tiwari, S., Ortiz-Rodriguez, F. et al. KG4ASTRA: question answering over Indian Missiles Knowledge Graph. Soft Comput 25, 13841–13855 (2021). https://doi.org/10.1007/s00500-021-06233-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-06233-y

Keywords

Search

Navigation