Efficient Knowledge Graph Construction Based on Optimized Plans
Freund M, Schmid SJ, Harth A (2025)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2025
Publisher: IOS Press Ebooks
Edited Volumes: Studies on the Semantic Web Volume 62: Linking Meaning: Semantic Technologies Shaping the Future of AI
Series: Studies on the Semantic Web
ISBN: 9781643686165
DOI: 10.3233/SSW250005
Abstract
Purpose:
Existing approaches for generating Knowledge Graphs (KGs) from file-based, non-RDF data using declarative mappings are either limited by language-specific engines or lack optimization with language-independent relational algebra backends, resulting in suboptimal performance. This research proposes an integrated framework that tightly couples logical and physical plan optimizations, enabling high-performance, language-agnostic RDF graph construction.
Methodology:
We formalize the KG construction process using relational algebra with a dedicated RDF term generation function within the projection operator, resulting in one of two canonicalized logical plans, one with a join and one without. We then introduce tightly coupled physical operators used to define concrete execution pipelines. We propose and evaluate two optimizations, logical-level constant-folding to reduce redundant computations and a physical-level heuristic scheduling strategy to optimize concurrent execution. We implemented the optimizations in a new backend engine called konverter and benchmarked the engine with an RML frontend against two comparable engines, Morph-KGC and FlexRML.
Findings:
Empirical results show that constant-folding improves performance by approximately 7.4% and heuristic scheduling by approximately 14.7% compared to a worst case scenario, with minimal additional memory overhead. Overall, konverter outperforms the current state-of-the-art in performance FlexRML, reducing execution time by 61.5% and peak memory usage by 25.1%, though it currently only supports CSV files, a limitation we aim to address in future work.
Value:
The proposed framework and optimizations provide a formal and practically validated approach to optimizing the execution of declarative mappings for KG construction. The konverter engine demonstrates the potential for building high-performance, language-agnostic engines for enterprise KG construction.
Authors with CRIS profile
Michael Freund
Sebastian Josef Schmid Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme Andreas Harth Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme
Sebastian Josef Schmid Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme Andreas Harth Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme
Involved external institutions
Germany (DE)How to cite
APA:
Freund, M., Schmid, S.J., & Harth, A. (2025). Efficient Knowledge Graph Construction Based on Optimized Plans. In Studies on the Semantic Web Volume 62: Linking Meaning: Semantic Technologies Shaping the Future of AI. IOS Press Ebooks.
MLA:
Freund, Michael, Sebastian Josef Schmid, and Andreas Harth. "Efficient Knowledge Graph Construction Based on Optimized Plans." Studies on the Semantic Web Volume 62: Linking Meaning: Semantic Technologies Shaping the Future of AI. IOS Press Ebooks, 2025.
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