Finding Minimum Connected Subgraphs with Ontology Exploration on Large RDF Data

TL;DR

This paper introduces RECON, a system that efficiently finds approximate minimum connected subgraphs in large knowledge graphs, leveraging ontology refinement to handle disconnections, and achieves high accuracy with sub-second response times.

Contribution

RECON is a novel system that provides fast, accurate approximate solutions for the NP-hard problem of finding minimum connected subgraphs in large KGs, incorporating ontology-based input refinement.

Findings

RECON outperforms five existing approaches in accuracy and speed.

RECON operates with a small memory footprint on graphs with hundreds of millions of edges.

RECON achieves sub-second response times in large-scale knowledge graphs.

Abstract

In this paper, we study the following problem: given a knowledge graph (KG) and a set of input vertices (representing concepts or entities) and edge labels, we aim to find the smallest connected subgraphs containing all of the inputs. This problem plays a key role in KG-based search engines and natural language question answering systems, and it is a natural extension of the Steiner tree problem, which is known to be NP-hard. We present RECON, a system for finding approximate answers. RECON aims at achieving high accuracy with instantaneous response (i.e., sub-second/millisecond delay) over KGs with hundreds of millions edges without resorting to expensive computational resources. Furthermore, when no answer exists due to disconnection between concepts and entities, RECON refines the input to a semantically similar one based on the ontology, and attempt to find answers with respect to the refined input. We conduct a comprehensive experimental evaluation of RECON. In particular we compare it with five existing approaches for finding approximate Steiner trees. Our experiments on four large real and synthetic KGs show that RECON significantly outperforms its competitors and incurs a much smaller memory footprint.