Managing Provenance Data in Knowledge Graph Management Platforms
Open accessAbstract
Abstract Knowledge Graphs (KGs) present factual information about domains of interest. They are used in a wide variety of applications and in different domains, serving as powerful backbones for organizing and extracting knowledge from complex data. In both industry and academia, a variety of platforms have been proposed for managing Knowledge Graphs. To use the full potential of KGs within these platforms, it is essential to have proper provenance management to understand where certain information in a KG stems from. This plays an important role in increasing trust and supporting open science principles. It enables reproducibility and updatability of KGs. In this paper, we propose a framework for provenance management of KG generation within a web portal. We present how our framework captures, stores, and retrieves provenance information. Our provenance representation is aligned with the standardized W3C Provenance Ontology. Through our framework, we can rerun the KG generation process over the same or different source data. With this, we support four applications: reproducibility, altered rerun, undo operation, and provenance retrieval. In summary, our framework aligns with the core principles of open science. By promoting transparency and reproducibility, it enhances the reliability and trustworthiness of research outcomes.
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Graph-structured memory is particularly useful in a multi-agent setting, as it allows storing memories represented as nodes and edges encoding relationships, or user-agent-resource interactions.
“Graph-structured memory is particularly useful in a multi-agent setting, as it allows storing memories represented as nodes and edges encoding relationships, or user-agentresource interactions [36].”
From Memory Fabric for Conversational AI Agents: Enabling Shared and Persistent Memory Across Users (2025) · cites this paper as [36]
Cites
A Review of Relational Machine Learning for Knowledge Graphs
Nickel, M., Murphy, K. P., Tresp, V., & Gabrilovich, E. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2483592 · Cited by 1,684 · see citing works
The Open Provenance Model core specification (v1.1)
Moreau, L., Clifford, B., Freire, J., Futrelle, J., Gil, Y., Groth, P., Kwaśnikowska, N., Miles, S., Paolo Missier, B., Myers, J., et al. (2010) · Future Generation Computer Systems · doi:10.1016/j.future.2010.07.005 · Cited by 706 · see citing works
Provenance for Computational Tasks: A Survey
Freire, J., Koop, D., Santos, E., & Silva, C. (2008) · Computing in Science & Engineering · doi:10.1109/mcse.2008.79 · Cited by 511 · see citing works
A survey on provenance: What for? What form? What from?
Herschel, M., Diestelkämper, R., & Ben Lahmar, H. (2017) · The VLDB Journal · doi:10.1007/s00778-017-0486-1 · Cited by 300 · see citing works
The W3C PROV family of specifications for modelling provenance metadata
Missier, P., Belhajjame, K., & Cheney, J. (2013) · International Conference on Extending Database Technology (EDBT) · doi:10.1145/2452376.2452478 · Cited by 202 · see citing works
Issues in Automatic Provenance Collection
Braun, U., Garfinkel, S., Holland, D. A., Muniswamy‐Reddy, K., & Seltzer, M. (2006) · Lecture notes in computer science · doi:10.1007/11890850_18 · Cited by 91 · see citing works
A systematic review of provenance systems
Pérez, B., Rubio, J., & Sáenz-Adán, C. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-018-1164-3 · Cited by 66 · see citing works
metaphactory: A platform for knowledge graph management
Haase, P., Herzig, D. M., Kozlov, A., Nikolov, A., & Trame, J. (2019) · Semantic Web · doi:10.3233/sw-190360 · Cited by 63 · see citing works
Wood, E. C., Glen, A. K., Kvarfordt, L. G., Womack, F., Acevedo, L., Yoon, T. S., Ma, C., Flores, V., Sinha, M., Chodpathumwan, Y., et al. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04932-3 · Cited by 48 · see citing works
Ozymandias: a biodiversity knowledge graph
Page, R. (2019) · PeerJ · doi:10.7717/peerj.6739 · Cited by 42 · see citing works
A knowledge-graph platform for newsrooms
Berven, A., Christensen, O. A., Moldeklev, S., Opdahl, A. L., & Villanger, K. J. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103321 · Cited by 31 · see citing works
Samuel, S., & König‐Ries, B. (2022) · Journal of Biomedical Semantics · doi:10.1186/s13326-021-00253-1 · Cited by 21 · see citing works
Self-citationSy, M. F., Roman, B., Kerrien, S., Mendez, D. M., Genet, H., Wajerowicz, W., Dupont, M., Lavriushev, I., Machon, J., Pirman, K., et al. (2022) · Semantic Web · doi:10.3233/sw-222974 · Cited by 9 · see citing works
Hogan, A. (2020) · Springer eBooks · doi:10.1007/978-3-030-51580-5_2 · Cited by 9 · see citing works
Majumdar, R., Meyer, R., & Wang, Z. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41036-9_3 · Cited by 3 · see citing works
Scalable Scientific Workflows Management System SWFMS
Abdul, M. (2016) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2016.071137 · Cited by 1 · see citing works
Flagged first, then by citations
1 of the 17 references couldn’t be matched to a known paper.
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Liu, C., Fan, H., Kim, M., Zhou, T., Yang, P., Zhao, L., Wang, Y., Che, Z., Liu, C., Li, B., et al. (2026) · Advanced Science · doi:10.1002/advs.202522900 · Cited by 20 · see citing works
Tracing Data Origins in Smart Cities: An IoT Perspective
Imankulova, B., Yafooz, W. M. S., Alshammari, K., Alshemali, B., Yahya, A. E., & Almisreb, A. A. (2025) · IEEE Access · doi:10.1109/access.2025.3591277 · Cited by 3 · see citing works
An interpretable knowledge recommendation method for civil dispute mediation
Wang, N., Cui, S., Zhang, J., Wang, R., & Yu, Y. (2025) · Data & Knowledge Engineering · doi:10.1016/j.datak.2025.102527 · Cited by 1 · see citing works
Lehmacher, C., & Paetzold-Byhain, K. (2025) · IEEE Access · doi:10.1109/access.2025.3609662 · Cited by 1 · see citing works
Memory Fabric for Conversational AI Agents: Enabling Shared and Persistent Memory Across Users
Tiwari, A., & Gupta, V. (2025) · TechRxiv · doi:10.36227/techrxiv.176523350.08289935/v1
Knowledge prompting: How knowledge engineers use generative AI
Koutsiana, E., Walker, J., Nwachukwu, M., Zhang, B.-H., Meroño-Peñuela, A., & Simperl, E. (2024) · Journal of Web Semantics · doi:10.1016/j.websem.2025.100873 · Cited by 4 · see citing works
The Chairs for Databases and Information Systems at the Friedrich-Schiller-Universität Jena
Ahmed, W., Mustafa, T. A., Bachinger, S. T., Fröbe, M., Hagen, M., König‐Ries, B., Mauch, M., Reimer, J. H., Naik, D. S., Ostrowski, A., et al. (2026) · Datenbank-Spektrum · doi:10.1007/s13222-026-00534-y
Self-citationShi, D., Shen, L., & Zurada, J. (2026) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70086
AI agents accelerate the discovery and engineering of food molecules
Weng, C., Zhao, C., Lu, B., Ai, L., & Yao, Y. (2026) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2026.106188
- Authors
- Kleinsteuber, E., Al Mustafa, T., Zander, F., König-Ries, B., & Babalou, S.
- Affiliations
- Friedrich Schiller University Jena (DE)
- German Centre for Integrative Biodiversity Research (DE)
- Corresponding author
- Babalou, Samira
- Venue
- Datenbank-Spektrum
- Published
- 2024-02-05
- Volume
- 24
- Issue
- 1
- Pages
- 43-52
- ISSN
- 1610-1995 (ISSN-L)
- 1618-2162
- OpenAlex
- W4391556845
- Cited by
- 8
- References
- 17
- Citations by year
- 2026: 4 · 2025: 4
- FWCI
- 1.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Datenbank-Spektrum (publishedVersion, cc-by)
- Funders
- Friedrich-Schiller-Universität Jena
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Keywords
- knowledge graphs, provenance management, knowledge graph construction, reproducibility, open science, web portal
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