Approximate Answering of Graph Queries

TL;DR

This paper reviews methods for approximate answering of graph queries on incomplete and evolving knowledge graphs, discussing their capabilities, limitations, and the types of queries they support.

Contribution

It provides a comprehensive overview of various approaches for query answering on incomplete knowledge graphs, highlighting their expressiveness and inference capabilities.

Findings

Summarizes different query types and datasets used for evaluation.

Analyzes the limitations of current methods.

Classifies approaches based on expressiveness and inference.

Abstract

Knowledge graphs (KGs) are inherently incomplete because of incomplete world knowledge and bias in what is the input to the KG. Additionally, world knowledge constantly expands and evolves, making existing facts deprecated or introducing new ones. However, we would still want to be able to answer queries as if the graph were complete. In this chapter, we will give an overview of several methods which have been proposed to answer queries in such a setting. We will first provide an overview of the different query types which can be supported by these methods and datasets typically used for evaluation, as well as an insight into their limitations. Then, we give an overview of the different approaches and describe them in terms of expressiveness, supported graph types, and inference capabilities.