Skip to main content

Nested Conceptual Graphs for Information Fusion Traceability

  • Conference paper
  • First Online:
Graph-Based Representation and Reasoning (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12879))

Included in the following conference series:

  • 412 Accesses


InSyTo is a toolbox of algorithms for information fusion and query relying on the conceptual graphs formalism and subgraph isomorphism search. InSyTo was used in order to develop many applications in different domains. Although the framework was used in several application domain and well received by end-users, they highlighted an urgent need for traceability within the information fusion process. We propose here an improvement of the toolbox, in order to embed traceability feature inside the fusion algorithm. The underlying conceptual graph representation of the information was extended from basic conceptual graph to Nested Typed Graphs. A lineage nested graph is added to each concept of the initial information graph, that contains it’s processing history through the several processing steps. The lineage graph contains the information concerning the initial sources of each elementary information item (concept), as well as the fusion operations that were applied on them. The main advantage of this new development is the capacity of having a trustworthy framework aware of the current observed situation, as well as the interpretations that were used to build this situation from elementary observations coming from different sources. In this paper, after presenting the context of our work, we recall of the InSyTo toolbox approach and functionalities. We then define the new information representation and operations that we proposed for a matter of traceability handling.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
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


  1. Assawamekin, N., Sunetnanta, T., Pluempitiwiriyawej, C.: Ontology-based multiperspective requirements traceability framework. Knowl. Inf. Syst. 25(3), 493–522 (2010)

    Article  Google Scholar 

  2. Laudy, C., Ganascia, J.G.: Using maximal join for information fusion. In: Graph Structures for Knowledge Representation and Reasoning (GKR 2009) Collocated with the Twenty-first International Joint Conference on Artificial Intelligence (IJCAI-09) (2009)

    Google Scholar 

  3. Chein, M., Mugnier, M.L.: Graph-Based Knowledge Representation: Computational Foundations of Conceptual Graphs. Springer, Heidelberg (2008).

    Book  MATH  Google Scholar 

  4. Fossier, S., Laudy, C., Pichon, F.: Managing uncertainty in conceptual graph-based soft information fusion. In: Proceedings of the 16th International Conference on Information Fusion, pp. 930–937. IEEE (2013)

    Google Scholar 

  5. Genest, D., Salvat, E.: A platform allowing typed nested graphs: how CoGITo became CoGITaNT. In: Mugnier, M.-L., Chein, M. (eds.) ICCS-ConceptStruct 1998. LNCS, vol. 1453, pp. 154–161. Springer, Heidelberg (1998).

    Chapter  Google Scholar 

  6. Gotel, O.C., Finkelstein, C.: An analysis of the requirements traceability problem. In: Proceedings of IEEE International Conference on Requirements Engineering, pp. 94–101. IEEE (1994)

    Google Scholar 

  7. Hamdan, W., Khazem, R., Rebdawi, G., Croitoru, M., Gutierrez, A., Buche, P.: On ontological expressivity and modelling argumentation schemes using COGUI. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXXI, pp. 5–18. Springer, Cham (2014).

    Chapter  Google Scholar 

  8. Laudy, C.: Semantic Knowledge Representations for Soft Data Fusion – Efficient Decision Support Systems - Practice and Challenges From Current to Future. Chiang Jao Publisher (2011)

    Google Scholar 

  9. Laudy, C., Mattioli, J., Mattioli, L.: Semantic information fusion algebraic framework applied to content marketing. In: Proceedings of the 21st International Conference on Information Fusion, FUSION 2013, Cambridge, UK, 10–13 July 2018, pp. 2338–2345 (2018).

  10. Laudy, C., Ruini, F., Zanasi, A., Przybyszewski, M., Stachowicz, A.: Using social media in crisis management: SOTERIA fusion center for managing information gaps. In: 20th International Conference on Information Fusion, FUSION 2017, Xi’an, China, 10–13 July 2017, pp. 1–8. IEEE (2017). 10.23919/ICIF.2017.8009880,

  11. Lebo, T., et al.: PROV-O: the PROV ontology (2013)

    Google Scholar 

  12. Sharma, S., Henderson, J., Ghosh, J.: CERTIFAI: counterfactual explanations for robustness, transparency, interpretability, and fairness of artificial intelligence models. arXiv preprint arXiv:1905.07857 (2019)

  13. Smuha, N.A.: The EU approach to ethics guidelines for trustworthy artificial intelligence. Comput. Law Rev. Int. 20(4), 97–106 (2019)

    Article  Google Scholar 

  14. Sowa, J.F.: Conceptual Structures. Information Processing in Mind and Machine, Addison-Wesley, Reading (1984)

    MATH  Google Scholar 

  15. Ware, C.: Information Visualization: Perception for Design. Morgan Kaufmann (2019)

    Google Scholar 

  16. Zhang, Y., Witte, R., Rilling, J., Haarslev, V.: An ontology-based approach for traceability recovery. In: 3rd International Workshop on Metamodels, Schemas, Grammars, and Ontologies for Reverse Engineering (ATEM 2006), Genoa, pp. 36–43 (2006)

    Google Scholar 

Download references


This work was partially performed under the 883347 H2020 project EFFECTOR, which has received funding from the European Union’s Horizon 2020 Program. This paper reflects only the authors’ view, and the European Commission is not liable to any use that may be made of the information contained therein.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Claire Laudy .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Laudy, C., Jacobé de Naurois, C. (2021). Nested Conceptual Graphs for Information Fusion Traceability. In: Braun, T., Gehrke, M., Hanika, T., Hernandez, N. (eds) Graph-Based Representation and Reasoning. ICCS 2021. Lecture Notes in Computer Science(), vol 12879. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86981-6

  • Online ISBN: 978-3-030-86982-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics