Knowledge Graph OLAP: A multidimensional model and query operations for contextualized knowledge graphs

Description

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 OLAP cube model from multidimensional data analysis. Traditional systems for online analytical processing (OLAP) employ multidimensional 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. Keywords: Contextualized Knowledge Repository, knowledge graph management system, knowledge graph summarization, Resource Description Framework, ontologies Journal-First Track: Präsentation des Artikels mit dem Titel „Knowledge Graph OLAP: A multidimensional model and query operations for contextualized knowledge graphs“ (10.3233/SW-200419), publiziert 2021 im Journal Semantic Web

Period	07 Oct 2022
Event title	EDOC 2022 - 26th Enterprise Design, Operations and Computing Conference
Event type	Conference
Location	ItalyShow on map