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

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. CCS CONCEPTS • Information systems → Data mining; • Computing methodologies → Neural networks.

Knowledge Graphs

“…GNNs have been used to perform classification over graphs encoding compounds, objects in images, documents, and so on; as well as to predict traffic, build recommender systems, verify software, and so on [145]. Given labelled examples, GNNs can even replace graph algorithms; for example, GNNs have been used to find central nodes in knowledge graphs in a supervised manner [98,99,117].…”

Matching Algorithms: Fundamentals, Applications and Challenges

“…Therefore, it is worth exploring how to effectively fuse these heterogeneous data to obtain the preference list. A number of works have been proposed to explore this trend in retrieval matching [147], [148], [149], user-item matching [150], [151], [152], entityrelation matching [153], [154], and image matching [155], [156]. However, extracting the decisive features from the problematic data such as data with missing values, noise, or outliers to complete the task of preference list inference poses a great challenge [157].…”

Property-Based Semantic Similarity Criteria to Evaluate the Overlaps of Schemas

“…In 2020, Giunchiglia and Fumagalli carried out a preliminary exploration on quantitative evaluation [34], but the evaluation method regarded the importance of all entity types as the same, without considering the differences among them. In the same year, Park tried to measure the importance of nodes in a knowledge graph [35]. However, this study mainly focused on the influence of inputs from different sources on nodes and did not pay attention to the influence of the relationship between nodes in the knowledge graph.…”