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Scalable Extraction and Adoption of Shapes for Improving Data Quality and Query Processing in Knowledge Graphs

A thesis proposing techniques to improve data quality, efficient data access, and interoperability in Knowledge Graphs.

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Scalable Extraction and Adoption of Shapes for Improving Data Quality and Query Processing in Knowledge Graphs

By Kashif Rabbani
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The resource proposes Quality Shapes Extraction (QSE) and SHACTOR to enhance data quality in Knowledge Graphs. It also introduces 'shapes statistics' for optimizing SPARQL query processing over KGs.

The approach is demonstrated on both synthetic and real-world datasets, showing potential improvements in query performance.

Abstract

The resource proposes Quality Shapes Extraction (QSE) and SHACTOR to enhance data quality in Knowledge Graphs. It also introduces 'shapes statistics' for optimizing SPARQL query processing over KGs. The approach is demonstrated on both synthetic and real-world datasets, showing potential improvements in query performance.

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data-qualityknowledge-graphssparql-query-processingshapes-extractionKnowledge GraphsStructured ContentContent EngineeringSemantic Interoperability
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