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

Tracking #: 2504-3718

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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. The represented knowledge is often context-dependent, leading to the construction of contextualized KGs. The multidimensional and hierarchical nature of context invites comparison with the multidimensional OLAP cube model from data analysis. Traditional systems for online analytical processing (OLAP) employ cube models to represent numeric values for further analysis using dedicated query operations. In this paper, along with an adaptation of the OLAP cube model for KGs, we introduce an adaptation of the traditional OLAP query operations for the purposes of performing analysis over 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 -- a management summary -- of the knowledge.
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