[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1007/978-3-031-35969-9_7guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
Article

Introduction to Ontologies for Defense Business Analytics

Published: 23 July 2023 Publication History

Abstract

In recent years, ontologies have become the leading solution for capturing corporate knowledge. Stored explicitly in documentation or tacitly in the minds of Subject Matter Experts (SMEs), enterprise knowledge, in all its forms, can be optimized into a tangible representation that gives way to more advanced business analytics. This paper seeks to review the benefits and challenges of developing ontologies through the lens of the government defense sector. To further demonstrate the learning curve in adapting to ontologies and graph-based knowledge structures in general, this paper will also provide a use-case experiment where business Subject Matter Experts (SMEs) were trained to design an ontology of the Operating Materials and Supplies (OM&S) domain at Naval Information Warfare Center (NIWC) Pacific by-hand.

References

[1]
Al-Aswadi FN, Chan HY, and Gan KH Automatic ontology construction from text: a review from shallow to deep learning trend Artif. Intell. Rev. 2019 53 6 3901-3928
[2]
Alatrish E Comparison some of ontology editors J. Manag. Inf. Syst. 2013 8 2 18-24
[3]
Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Sci. Am. 284(5),  34–43 (2001). http://www.jstor.org/stable/26059207
[4]
Booz Allen Hamilton Inc.: Overcoming Obstacles to Data Integration for Defense. Perspectives (2022). https://www.boozallen.com/insights/defense/overcoming-obstacles-to-data-integration-for-defense.html
[5]
Bowman, M., Lopez, A., Tecuci, G.: Ontology development for military applications. In: Proceedings of the SouthEastern Regional ACM Conference, Atlanta, GA (2001)
[6]
Buraga, S., Cojocaru, L., Nichifor, O.: Survey on web ontology editing tools. Transactions Automatic Control Computer Science, pp. 1–6 (2006)
[7]
Chergui W, Zidat S, and Marir F An approach to the acquisition of tacit knowledge based on an ontological model J. King Saud Univ. – Comput. Inf. Sci. 2020 32 7 818-828
[8]
Delen D and Ram S Research challenges and opportunities in business analytics J. Bus. Analytics 2018 1 1 2-12
[9]
DIA Public Affairs.: GAMECHANGER: Where policy meets AI. Defense Intelligence Agency (2022)
[10]
Drissi, A., Khemiri, A., Sassi, S., Chbeir, R.: A new automatic ontology construction method based on machine learning techniques: application on financial corpus. In: Proceedings of the 13th International Conference on Management of Digital EcoSystems, pp. 57–61. Association for Computing Machinery (2021).
[11]
Ehrlinger, L., Wolfram, W.: Towards a definition of knowledge graphs. In: 12th International Conference on Semantic Systems (2016)
[12]
Eppler, M., Burkhard, R.: Knowledge visualization: towards a new discipline and its fields of application. Università della Svizzera italiana (2004)
[13]
Galkin, M., Auer, S., Kim, H., Scerri, S.: Integration strategies for enterprise knowledge graphs. In: 2016 IEEE Tenth International Conference on Semantic Computing (ICSC), pp. 242–245. Laguna Hills, CA, USA (2016). 
[14]
Grootendorst M KeyBERT: Minimal keyword extraction with BERT Zenodo 2020
[15]
Hogan, A et al.: Knowledge Graphs. Assoc. Comput. Mach. 54(4), 3447772 (2021). 
[16]
Honnibal, M., Montani, I., Van Landeghem, S., Boyd, A.: spaCy: Industrial-strength Natural Language Processing in Python (2020).
[17]
JAIC Public Affairs: Meet the Gamechanging App That Uses AI to Simplify DoD Policy Making. Chief Digital and Artificial Intelligence Office (2021)
[18]
Jasper, R., Uschold, M. A framework for understanding and classifying ontology applications. In: Proceedings 12th International Workshop on Knowledge Acquisition, Modelling, and Management KAW 99, pp. 16–21 (1999)
[19]
Jepsen TC Just what is an ontology, anyway? IT Profess. 2009 11 5 22-27
[20]
Katsos G Department of defense terminology program J. Force Q. 2018 88 124-127
[21]
Kulmanov M, Smaili F, Gao X, and Hoehndorf R Semantic similarity and machine learning with ontologies Brief. Bioinform. 2021 22 4 1-18
[22]
Lin, G.: Meet Advana: How the department of defense solved its data interoperability challenges. Government Technology Insider (2021)
[23]
Mohammad A and Al-Saiyd N Guidelines for tacit knowledge acquisition J. Theor. Appl. Inf. Technol. 2012 38 1 110-118
[24]
Musen, M.: The Protégé project: a look back and a look forward. AI Matters. Assoc. Comput. Mach. Specific Interest Group Artif. Intell. 1(4), 25757003 (2015). 
[25]
Noy N, Gao Y, Jain A, Narayanan A, Patterson A, and Taylor J Industry-scale knowledge graphs: lessons and challenges: five diverse technology companies show how it’s done Assoc. Comput. Mach. 2019 17 2 48-75
[26]
Noy, N., McGuinness, D.: Ontology development 101: a guide to creating your first ontology.  Stanford Knowledge Systems Lab (2001)
[27]
Office of the Under Secretary of Defense (Comptroller) (OUSD(C)): Advana – Common Enterprise Data Repository for the Department of Defense. Department of Defense Financial Management Regulation (DoD FMR) 1(10) (2020)
[28]
Princeton University About WordNet 2010 WordNet Princeton University
[29]
Sherman, J.: Guidance on Software Development and Open Source Software. U.S. Department of Defense (2022)
[30]
Smith E The role of tacit and explicit knowledge in the workplace J. Knowl. Manag. 2001 5 4 311-321
[31]
Valeontis, K., Mantzari, E.: The linguistic dimension of terminology: principles and methods of term formation. In: 1st Athens International Conference on Translation and Interpretation Translation: Between Art and Social Science, pp. 13–14 (2006)
[32]
Yu, J., McCluskey, K., Mukherjee, S.: Tax knowledge graph for a smarter and more personalized TurboTax. arXiv (2020)

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings
HCI in Business, Government and Organizations: 10th International Conference, HCIBGO 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023, Proceedings, Part I
Jul 2023
517 pages
ISBN:978-3-031-35968-2
DOI:10.1007/978-3-031-35969-9
  • Editors:
  • Fiona Nah,
  • Keng Siau

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 23 July 2023

Author Tags

  1. Ontologies
  2. Knowledge Graphs
  3. Ontology Learning
  4. Business Analytics
  5. Knowledge Representation

Qualifiers

  • Article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 0
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 25 Dec 2024

Other Metrics

Citations

View Options

View options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media