Bottom-up Anytime Discovery of Generalised Multimodal Graph Patterns for Knowledge Graphs

TL;DR

This paper introduces an anytime bottom-up algorithm for discovering generalized multimodal graph patterns in knowledge graphs, aiding scholars in exploring and analyzing complex data through interactive queries and metadata.

Contribution

It presents a novel bottom-up, anytime discovery algorithm for generalized graph patterns in knowledge graphs, integrated with an interactive exploration tool.

Findings

Effective pattern discovery demonstrated with domain experts

Enhanced data exploration through interactive facet browser

Method supports uncovering new insights in heterogeneous knowledge graphs

Abstract

Vast amounts of heterogeneous knowledge are becoming publicly available in the form of knowledge graphs, often linking multiple sources of data that have never been together before, and thereby enabling scholars to answer many new research questions. It is often not known beforehand, however, which questions the data might have the answers to, potentially leaving many interesting and novel insights to remain undiscovered. To support scholars during this scientific workflow, we introduce an anytime algorithm for the bottom-up discovery of generalized multimodal graph patterns in knowledge graphs. Each pattern is a conjunction of binary statements with (data-) type variables, constants, and/or value patterns. Upon discovery, the patterns are converted to SPARQL queries and presented in an interactive facet browser together with metadata and provenance information, enabling scholars to explore, analyse, and share queries. We evaluate our method from a user perspective, with the help of domain experts in the humanities.