Skip to main content
SpringerLink
Log in

Integrative KnowGen: Integrative Knowledge Base Generation for Criminology as a Domain of Choice

  • Conference paper
  • First Online:
Digital Technologies and Applications (ICDTA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 455))

Included in the following conference series:

Abstract

Knowledge bases are essential in developing expert intelligent systems to solve domain specific problems. The low availability of detailed knowledge bases is a byproduct of the fact that addition and reasoning of information entities is largely a manual process. This paper proposes an architecture for automatic generation of knowledge bases from various heterogenous sources. Criminology has been chosen as a domain of choice because extensive knowledge in this area can help in curbing crimes in our society by creating preventive and predictive analysis systems. The proposed architecture utilizes seed criminology ontology as a base and adds information via multiple inputs i.e., Google’s news API, criminology journals, e-books, web portals and available datasets. The approach incorporates classification using LSTM and alignment of the instances using transformers. Furthermore, reasoning has been done using description logics and a large extensive knowledge base has been obtained. The model yields an average accuracy of 95.35% which surpasses the previous approaches.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Deepak, G., Ahmed, A., Skanda, B.: An intelligent inventive system for personalised webpage recommendation based on ontology semantics. Int. J. Intell. Syst. Technol. Appl. 18(1–2), 115–132 (2019)

    Google Scholar 

  2. Kumar, A., Deepak, G., Santhanavijayan, A.: HeTOnto: a novel approach for conceptualization, modeling, visualization, and formalization of domain centric ontologies for heat transfer. In: International Conference on Electronics, Computing and Communication Technologies (CONECCT), July 2020, pp. 1–6 (2020)

    Google Scholar 

  3. Pushpa, C.N., Deepak, G., Kumar, A., Thriveni, J. Venugopal, K.R.: OntoDisco: improving web service discovery by hybridization of ontology focused concept clustering and interface semantics. In: International Conference on Electronics, Computing and Communication Technologies (CONECCT), July 2020, pp. 1–5. IEEE (2020)

    Google Scholar 

  4. Saito, I., Nishida, K., Asano, H., Tomita, J.: Commonsense knowledge base completion and generation. In: Proceedings of the 22nd Conference on Computational Natural Language Learning, October 2018. pp. 141–150 (2018)

    Google Scholar 

  5. Dognin, P.L., Padhi, I., Melnyk, I., Das, P.: ReGen: Reinforcement Learning for Text and Knowledge Base Generation Using Pretrained Language Models (2021)

    Google Scholar 

  6. Althoff, T., Dong, X.L., Murphy, K., Alai, S., Dang, V., Zhang, W.: Timemachine: timeline generation for knowledge-base entities. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2015, pp. 19–28 (2015)

    Google Scholar 

  7. Inokuchi, M., et al.: Design procedure of knowledge base for practical attack graph generation. In: Proceedings of the 2019 ACM Asia Conference on Computer and Communications Security, July 2019, pp. 594–601 (2019)

    Google Scholar 

  8. Song, L., Zhao, L.: Question generation from a knowledge base with web exploration. arXiv preprint arXiv:1610.03807 (2016)

  9. Yeh, S.H., Huang, H.H., Chen, H.H.: Precise description generation for knowledge base entities with local pointer network. In: International Conference on Web Intelligence (WI), December 2018, pp. 214–221 (2018)

    Google Scholar 

  10. Pertsas, V., Constantopoulos, P.: Scholarly ontology: modelling scholarly practices. Int. J. Digit. Libr. 18(3), 173–190 (2016)

    Article  Google Scholar 

  11. Qiao, L., et al.: An ontology-based modelling and reasoning framework for assembly sequence planning. Int. J. Adv. Manuf. Technol. 94(9), 4187–4197 (2018)

    Article  Google Scholar 

  12. Sterckx, L., Demeester, T., Deleu, J., Develder, C.: Knowledge base population using semantic label propagation. Knowl.-Based Syst. 108, 79–91 (2016)

    Article  Google Scholar 

  13. Fauqueur, J., Thillaisundaram, A., Togia, T.: Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns. arXiv preprint arXiv:1907.01417 (2019)

  14. Pushpa, C.N., Deepak, G., Thriveni, J., Venugopal, K.R.: A hybridized framework for ontology modeling incorporating latent semantic analysis and content based filtering. Int. J. Comput. Appl. 150(11), 33–41 (2016)

    Google Scholar 

  15. Deepak, G., Gulzar, Z., Leema, A.A.: An intelligent system for modeling and evaluation of domain ontologies for crystallography as a prospective domain with a focus on their retrieval. Comput. Electric. Eng. 96, 107604 (2021)

    Article  Google Scholar 

  16. Hybridised, K.C.N.: OntoKnowNHS: Ontology Driven Knowledge Centric Novel Hybridised Semantic Scheme for Image Recommendation Using Knowledge Graph. Knowledge Graphs and Semantic Web, 138

    Google Scholar 

  17. Ojha, R., Deepak, G.: Metadata driven semantically aware medical query expansion. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M.A. (eds.) Knowledge Graphs and Semantic Web: Third Iberoamerican Conference and Second Indo-American Conference, KGSWC 2021, Kingsville, Texas, USA, November 22–24, 2021, Proceedings, pp. 223–233. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-91305-2_17

    Chapter  Google Scholar 

  18. Yethindra, D.N., Deepak, G.: A semantic approach for fashion recommendation using logistic regression and ontologies. In: 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), September 2021, pp. 1–6. IEEE (2021)

    Google Scholar 

  19. Adithya, V., Deepak, G.: HBlogRec: a hybridized cognitive knowledge scheme for blog recommendation infusing XGBoosting and semantic intelligence. In: 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), July 2021, pp. 1–6. IEEE (2021)

    Google Scholar 

  20. Surya, D., Deepak, G., Santhanavijayan, A.: KSTAR: a knowledge based approach for socially relevant term aggregation for web page recommendation. In: Motahhir, S., Bossoufi, B. (eds.) Digital Technologies and Applications: Proceedings of ICDTA 21, Fez, Morocco, pp. 555–564. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_50

    Chapter  Google Scholar 

  21. Krishnan, N., Deepak, G.: Towards a novel framework for trust driven web URL recommendation incorporating semantic alignment and recurrent neural network. In: 2021 7th International Conference on Web Research (ICWR), May 2021, pp. 232–237. IEEE (2021)

    Google Scholar 

  22. Rithish, H., Deepak, G., Santhanavijayan, A.: Automated assessment of question quality on online community forums. In: Motahhir, S., Bossoufi, B. (eds.) Digital Technologies and Applications: Proceedings of ICDTA 21, Fez, Morocco, pp. 791–800. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_72

    Chapter  Google Scholar 

  23. Deepak, G., Kasaraneni, D.: OntoCommerce: an ontology focused semantic framework for personalised product recommendation for user targeted e-commerce. Int. J. Comput. Aided Eng. Technol. 11(4–5), 449–466 (2019)

    Article  Google Scholar 

  24. Roopak, N., Deepak, G.: KnowGen: a knowledge generation approach for tag recommendation using ontology and honey bee algorithm. In: Musleh Al-Sartawi, A.M.A., Razzaque, A., Kamal, M.M. (eds.) EAMMIS 2021. LNNS, vol. 239, pp. 345–357. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77246-8_33

    Chapter  Google Scholar 

  25. Deepak, G., Santhanavijayan, A.: UQSCM-RFD: a query–knowledge interfacing approach for diversified query recommendation in semantic search based on river flow dynamics and dynamic user interaction. Neural Comput. Appl. 1–25 (2021)

    Google Scholar 

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

    Article  Google Scholar 

  27. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Gurunameh Singh Chhatwal

  2. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India

    Gerard Deepak

Authors
  1. Gurunameh Singh Chhatwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gerard Deepak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gerard Deepak .

Editor information

Editors and Affiliations

  1. Ecole Nationale des Sciences Appliquées, Fez, Morocco

    Saad Motahhir

  2. Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Badre Bossoufi

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chhatwal, G.S., Deepak, G. (2022). Integrative KnowGen: Integrative Knowledge Base Generation for Criminology as a Domain of Choice. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2022. Lecture Notes in Networks and Systems, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-031-02447-4_49

Download citation

Publish with us

Policies and ethics

Search

Navigation