Approximate Answering of Graph Queries
It addresses the problem of handling incomplete and evolving knowledge graphs for users needing reliable query answers, but is incremental as it surveys existing methods.
This chapter reviews methods for answering queries on incomplete knowledge graphs as if they were complete, covering query types, datasets, and limitations of existing approaches.
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.