Skyline Queries over Knowledge Graphs

With the continuously growing amount of data offered in the form of knowledge graphs, users are often overwhelmed by the amount of potentially relevant information and entities. Hence, helping users find relevant data is a problem that becomes more and more important. Skyline queries are typically used in multi-criteria decision making applications to find a set of objects that are of interest to a user. This type of queries has been extensively studied over relational data in the database community. But only little attention has yet been paid to investigating if and how the skyline principle can help identifying sets of interesting entities in knowledge graphs. In this paper, we therefore show how the skyline principle can be applied to RDF knowledge graphs and help the user find interesting entities. In particular, we present algorithms using commonly used standard interfaces for accessing RDF data and a lightweight extension of existing interfaces (SkyTPF) to process skyline queries. Our experiments show that the proposed algorithms enable efficient and scalable skyline query processing over knowledge graphs.