Divided We Stand Out! Forging Cohorts fOr Numeric Outlier Detection in Large Scale Knowledge Graphs (CONOD)

  • Hajira Jabeen
  • Rajjat Dadwal
  • Gezim Sejdiu
  • Jens Lehmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11313)


With the recent advances in data integration and the concept of data lakes, massive pools of heterogeneous data are being curated as Knowledge Graphs (KGs). In addition to data collection, it is of utmost importance to gain meaningful insights from this composite data. However, given the graph-like representation, the multimodal nature, and large size of data, most of the traditional analytic approaches are no longer directly applicable. The traditional approaches could collect all values of a particular attribute, e.g. height, and try to perform anomaly detection for this attribute. However, it is conceptually inaccurate to compare one attribute representing different entities, e.g. the height of buildings against the height of animals. Therefore, there is a strong need to develop fundamentally new approaches for the outlier detection in KGs. In this paper, we present a scalable approach, dubbed CONOD, that can deal with multimodal data and performs adaptive outlier detection against the cohorts of classes they represent, where a cohort is a set of classes that are similar based on a set of selected properties. We have tested the scalability of CONOD on KGs of different sizes, assessed the outliers using different inspection methods and achieved promising results.


Knowledge graph Cluster Outlier Blocking Cohort RDF DBpedia 



This work was partly supported by the EU Horizon2020 projects WDAqua (GA no. 642795), Boost4.0 (GA no. 780732) and BigDataOcean (GA no. 732310).


  1. 1.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)CrossRefGoogle Scholar
  2. 2.
    Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, pp. 253–262. ACM (2004)Google Scholar
  3. 3.
    Debattista, J., Lange, C., Auer, S.: A preliminary investigation towards improving linked data quality using distance-based outlier detection. In: Li, Y.F., et al. (eds.) JIST 2016. LNCS, vol. 10055, pp. 116–124. Springer, Cham (2016). Scholar
  4. 4.
    Fleischhacker, D., Paulheim, H., Bryl, V., Völker, J., Bizer, C.: Detecting errors in numerical linked data using cross-checked outlier detection. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 357–372. Springer, Cham (2014). Scholar
  5. 5.
    Grubbs, F.E.: Procedures for detecting outlying observations in samples. Technometrics 11(1), 1–21 (1969)CrossRefGoogle Scholar
  6. 6.
    Kliegr, T.: Linked hypernyms: enriching DBpedia with targeted hypernym discovery. Web Semant. Sci. Serv. Agents World Wide Web 31, 59–69 (2015)CrossRefGoogle Scholar
  7. 7.
    Lehmann, J., et al.: DBpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6(2), 167–195 (2015)Google Scholar
  8. 8.
    Lehmann, J., et al.: Distributed semantic analytics using the SANSA stack. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10588, pp. 147–155. Springer, Cham (2017). Scholar
  9. 9.
    Leys, C., Ley, C., Klein, O., Bernard, P., Licata, L.: Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49(4), 764–766 (2013)CrossRefGoogle Scholar
  10. 10.
    McGill, R., Tukey, J.W., Larsen, W.A.: Variations of box plots. Am. Stat. 32(1), 12–16 (1978)Google Scholar
  11. 11.
    Melo, A., Theobald, M., Völker, J.: Correlation-based refinement of rules with numerical attributes. In: FLAIRS Conference (2014)Google Scholar
  12. 12.
    Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Stat. 33(3), 1065–1076 (1962)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Paulheim, H.: Identifying wrong links between datasets by multi-dimensional outlier detection. In: WoDOOM, pp. 27–38 (2014)Google Scholar
  14. 14.
    Wienand, D., Paulheim, H.: Detecting incorrect numerical data in DBpedia. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 504–518. Springer, Cham (2014). Scholar
  15. 15.
    Zaharia, M., et al.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation (2012)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of BonnBonnGermany
  2. 2.Fraunhofer IAISSankt AugustinGermany

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