Knowledge Graph OLAP: A Multidimensional Model and Query Operations for Contextualized Knowledge Graphs

Tracking #: 2269-3482

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Christoph Schuetz
Loris Bozzato
Bernd Neumayr
Michael Schrefl
Luciano Serafini

Responsible editor: 
Harald Sack

Submission type: 
Full Paper
A knowledge graph (KG) represents real-world entities and their relationships with each other. The thus represented knowledge is often context-dependent, leading to the construction of contextualized KGs. Due to the multidimensional and hierarchical nature of context, the multidimensional OLAP cube model from data analysis is a natural fit for the representation of contextualized KGs. Traditional systems for online analytical processing (OLAP) employ cube models to represent numeric values for further processing using dedicated query operations. In this paper, along with an adaptation of the OLAP cube model for KGs, we introduce an adaptation of traditional OLAP query operations for the purposes of working with contextualized KGs. In particular, we decompose the roll-up operation from traditional OLAP into a merge and an abstraction operation. The merge operation corresponds to the selection of knowledge from different contexts whereas abstraction replaces entities with more general entities. The result of such a query is a more abstract, high-level view on the contextualized KG.
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