Logo PTI Logo FedCSIS

Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

Analyzing longitudinal Data in Knowledge Graphs utilizing shrinking pseudo-triangles

, ,

DOI: http://dx.doi.org/10.15439/2022F45

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 323–327 ()

Full text

Abstract. This paper aims to analyze longitudinal data in knowledge graphs. Knowledge graphs play a central role for linking different data. While multiple layers for data from different sources are considered, there is only very limited research on longitudinal data in knowledge graphs. However, knowledge graphs are widely used in big data integration, especially for connecting data from different domains. Few studies have investigated the questions how multiple layers and time points within graphs impact methods and algorithms developed for single-purpose networks. This manuscript investigates the impact of a modeling of longitudinal data in multiple layers on retrieval algorithms. In particular, (a) we propose a first draft of a generic model for longitudinal data in multi-layer knowledge graphs, (b) we develop an experimental environment to evaluate a generic retrieval algorithm on random graphs inspired by computational social sciences. We present a knowledge graph generated on German job advertisements comprising data from different sources, both structured and unstructured, on data between 2011 and 2021. The data is linked using text mining and natural language processing methods. We further (c) present two different shrinking techniques for structured and unstructured layers in knowledge based on graph structures like triangles and pseudo-triangles. The presented approach (d) shows that on the one hand, the initial research questions, on the other hand the graph structures and topology have a great impact on the structures and efficiency for additional data stored. Although the experimental analysis of random graphs allows us to make some basic observations, we will (e) make suggestions for additional research on particular graph structures that have a great impact on the analysis of knowledge graph structures.

References

  1. D. Suárez, J. M. Dı́az-Puente, and M. Bettoni, “Risks identification and management related to rural innovation projects through social networks analysis: A case study in spain,” Land, vol. 10, no. 6, p. 613, 2021.
  2. L. M. Berhan, A. L. Adams, W. L. McKether, and R. Kumar, “Board 14: Social networks analysis of african american engineering students at a pwi and an hbcu–a comparative study,” in 2019 ASEE Annual Conference & Exposition, 2019.
  3. C. Rollinger, “Amicitia sanctissime colenda,” Freundschaft und soziale Netzwerke in der Späten Republik, 2014.
  4. J. Dörpinghaus and A. Stefan, “Knowledge extraction and applications utilizing context data in knowledge graphs,” in 2019 Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, 2019, pp. 265–272.
  5. G. Rossetti, S. Citraro, and L. Milli, “Conformity: A path-aware homophily measure for node-attributed networks,” IEEE Intelligent Systems, vol. 36, no. 1, pp. 25–34, 2021.
  6. A. Callahan, V. Polony, J. D. Posada, J. M. Banda, S. Gombar, and N. H. Shah, “Ace: the advanced cohort engine for searching longitudinal patient records,” Journal of the American Medical Informatics Association, vol. 28, no. 7, pp. 1468–1479, 2021.
  7. X. Xu, X. Xu, Y. Sun, X. Liu, X. Li, G. Xie, and F. Wang, “Predictive modeling of clinical events with mutual enhancement between longitudinal patient records and medical knowledge graph,” in 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 2021, pp. 777–786.
  8. S. Auer and H. Herre, “A versioning and evolution framework for rdf knowledge bases,” in International Andrei Ershov Memorial Conference on Perspectives of System Informatics. Springer, 2006, pp. 55–69.
  9. F. Zablith, G. Antoniou, M. d’Aquin, G. Flouris, H. Kondylakis, E. Motta, D. Plexousakis, and M. Sabou, “Ontology evolution: a process-centric survey,” The knowledge engineering review, vol. 30, no. 1, pp. 45–75, 2015.
  10. M. Javed, Y. M. Abgaz, and C. Pahl, “Ontology change management and identification of change patterns,” Journal on Data Semantics, vol. 2, no. 2, pp. 119–143, 2013.
  11. Y. Roussakis, I. Chrysakis, K. Stefanidis, G. Flouris, and Y. Stavrakas, “A flexible framework for understanding the dynamics of evolving rdf datasets,” in International Semantic Web Conference. Springer, 2015, pp. 495–512.
  12. N. Arndt, P. Naumann, N. Radtke, M. Martin, and E. Marx, “Decentralized collaborative knowledge management using git,” Journal of Web Semantics, vol. 54, pp. 29–47, 2019.
  13. S. Cardoso, C. Reynaud-Delaı̂tre, M. Da Silveira, Y.-C. Lin, A. Gross, E. Rahm, and C. Pruski, “Evolving semantic annotations through multiple versions of controlled medical terminologies,” Health and technology, vol. 8, no. 5, pp. 361–376, 2018.
  14. A. Eibeck, A. Chadzynski, M. Q. Lim, K. Aditya, L. Ong, A. Devanand, G. Karmakar, S. Mosbach, R. Lau, I. A. Karimi et al., “A parallel world framework for scenario analysis in knowledge graphs,” Data-Centric Engineering, vol. 1, 2020.
  15. M. Stops, A.-C. Bächmann, R. Glassner, M. Janser, B. Matthes, L.-J. Metzger, C. Müller, and J. Seitz, “Machbarkeitsstudie kompetenz-kompass: Teilprojekt 2: Beobachtung von kompetenzanforderungen in stellenangeboten.” [Online]. Available: https://www.bmas.de/DE/Service/Publikationen/Forschungsberichte/fb-553-machbarkeitsstudie-kompetenz-kompass.html
  16. Bertelsmann Stiftung and Burning Glass Technologies, “Digitalization in the german labor market: Analyzing demand for digital skills in job vacancies.”
  17. S. Köhne-Finster, I. Leppelmeier, R. Helmrich, D. Deden, A. Geduldig, B. Güntürk-Kuhl, P. Martin, C. Neuber-Pohl, M. Schandock, R. S. Schreiber, and M. Tiemann, Berufsbildung 4.0 - Fachkräftequalifikationen und Kompetenzen für die digitalisierte Arbeit von morgen: Säule 3: Monitoring- und Projektionssystem zu Qualifizierungsnotwendigkeiten für die Berufsbildung 4.0, 1st ed., ser. Wissenschaftliche Diskussionspapiere. Leverkusen: Verlag Barbara Budrich, 2020, vol. Heft 214.
  18. A. Bhola, K. Halder, A. Prasad, and M.-Y. Kan, “Retrieving skills from job descriptions: A language model based extreme multi-label classification framework,” in Proceedings of the 28th International Conference on Computational Linguistics, D. Scott, N. Bel, and C. Zong, Eds. Stroudsburg, PA, USA: International Committee on Computational Linguistics, 2020, pp. 5832–5842.
  19. D. Fensel, U. Şimşek, K. Angele, E. Huaman, E. Kärle, O. Panasiuk, I. Toma, J. Umbrich, and A. Wahler, Introduction: What Is a Knowledge Graph? Cham: Springer International Publishing, 2020, pp. 1–10. [Online]. Available: https://doi.org/10.1007/978-3-030-37439-6 1
  20. L. Ehrlinger and W. Wöß, “Towards a definition of knowledge graphs.” SEMANTiCS (Posters, Demos, SuCCESS), vol. , no. 48, 2016.
  21. M. A. Rodriguez and P. Neubauer, “The graph traversal pattern,” in Graph data management: Techniques and applications. IGI Global, 2012, pp. 29–46.
  22. ——, “Constructions from dots and lines,” Bulletin of the American Society for Information Science and Technology, vol. 36, no. 6, pp. 35–41, 2010.
  23. C. C. Aggarwal and C. Zhai, “An introduction to text mining,” in Mining text data. Springer, 2012, pp. 1–10.