Pre-Conference Talk by XIAO Hanhua | How to Avoid Jumping to Conclusions: Measuring the Robustness of Outstanding Facts in Knowledge Graphs

An outstanding fact (OF) is a striking claim by which some entities stand out from their peers on some attribute. OFs serve data journalism, fact-checking, and recommendation. However, one could jump to conclusions by selecting truthful OFs while intentionally or inadvertently ignoring lateral contexts and data that render them less striking. This jumping conclusion bias from unstable OFs may disorient the public, including voters and consumers, raising concerns about fairness and transparency in political and business competition. It is thus ethically imperative for several stakeholders to measure the robustness of OFs with respect to lateral contexts and data. Unfortunately, a capacity for such inspection for OFs mined from knowledge graphs (KGs) is missing. In this paper, we propose a methodology that inspects the robustness of OFs in KGs by perturbation analysis. We define (1) entity perturbation, which detects outlying contexts by perturbing context entities in the OF; and (2) data perturbation, which considers plausible data that render an OF less striking. We compute expected strikingness scores of OFs over perturbation relevance distributions and assess an OF as robust if its measured strikingness does not deviate significantly from the expected. We devise a suite of exact and sampling algorithms for perturbation analysis on large KGs. Extensive experiments reveal that our methodology accurately and efficiently detects frail OFs generated by existing mining approaches on KGs. We also show the effectiveness of our approaches via case and user studies. 

This is a Pre-Conference talk for 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024).
 

Xiao Hanhua is a PhD Candidate in Computer Science at the SMU School of Computing and Information Systems, supervised by Assistant Prof. Li Yuchen.  His research is focused on knowledge graph mining.