Posets and Bounded Probabilities for Discovering Order-inducing Features in Event Knowledge Graphs

TL;DR

This paper introduces a probabilistic method for automatically discovering event knowledge graphs from uncurated data, utilizing partial orders and poset bounds to efficiently infer process structures.

Contribution

It develops a novel inference algorithm based on statistical principles that automates EKG discovery, overcoming reliance on heuristics or manual analysis.

Findings

The proposed algorithm effectively prunes the search space using bounds.

It converges rapidly to solutions consistent with manual EKGs.

The method handles complex hypothesis spaces with non-convexity.

Abstract

Event knowledge graphs (EKG) extend the classical notion of a trace to capture multiple, interacting views of a process execution. In this paper, we tackle the open problem of automating EKG discovery from uncurated data through a principled probabilistic framing based on the outcome space resulting from featured-derived partial orders on events. From this we derive an EKG discovery algorithm based on statistical inference rather than an ad hoc or heuristic-based strategy, or relying on manual analysis from domain experts. This approach comes at the computational cost of exploring a large, non-convex hypothesis space. In particular, solving the maximum likelihood term in our objective function involves counting the number of linear extensions of posets, which in general is #P-complete. Fortunately, bound estimates suffice for model comparison, and admit incorporation into a bespoke branch-and-bound algorithm. We establish an upper bound on our objective function which we show to be antitonic w.r.t. search depth for branching rules that are monotonic w.r.t. model inclusion. This allows pruning of large portions of the search space, which we show experimentally leads to rapid convergence toward optimal solutions that are consistent with manually built EKGs.