MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals

TL;DR

MultiImport is an end-to-end model that infers node importance in knowledge graphs by effectively integrating multiple input signals, including external data, using attentive graph neural networks, leading to significant performance improvements.

Contribution

The paper introduces MultiImport, a novel latent variable model that simultaneously considers multiple signals for node importance estimation in knowledge graphs, addressing limitations of previous methods.

Findings

Outperforms existing methods with up to 23.7% higher NDCG@100.

Effectively handles conflicting and overlapping signals.

Demonstrates robustness on real-world knowledge graphs.

Abstract

Given multiple input signals, how can we infer node importance in a knowledge graph (KG)? Node importance estimation is a crucial and challenging task that can benefit a lot of applications including recommendation, search, and query disambiguation. A key challenge towards this goal is how to effectively use input from different sources. On the one hand, a KG is a rich source of information, with multiple types of nodes and edges. On the other hand, there are external input signals, such as the number of votes or pageviews, which can directly tell us about the importance of entities in a KG. While several methods have been developed to tackle this problem, their use of these external signals has been limited as they are not designed to consider multiple signals simultaneously. In this paper, we develop an end-to-end model MultiImport, which infers latent node importance from multiple, potentially overlapping, input signals. MultiImport is a latent variable model that captures the relation between node importance and input signals, and effectively learns from multiple signals with potential conflicts. Also, MultiImport provides an effective estimator based on attentive graph neural networks. We ran experiments on real-world KGs to show that MultiImport handles several challenges involved with inferring node importance from multiple input signals, and consistently outperforms existing methods, achieving up to 23.7% higher NDCG@100 than the state-of-the-art method.