Divided we stand out! forging cohorts for numeric outlier detection in large scale knowledge graphs (Conod)

Abstract

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.