Scientific Knowledge Graphs Should Preserve Negative Evidence

Douglas Ward; Peter Torres; Kyle Peterson

Abstract

Reliable deployment in experimental campaigns under costly pressure-temperature conditions depends on more than obtaining a strong benchmark result. This conceptual analysis uses negative and null evidence to study how claims travel from data to model output and then to action. Its central thesis is that the unit of assurance must be the linked history of sample preparation, pressure-temperature path, instrument output, code version, fit, and scientific claim. The reviewed evidence shows recurring risks from hidden distribution change, correlated evaluation error, missing provenance, and optimization objectives that omit downstream costs. In response, the article proposes a layered evaluation program combining controlled perturbations, subgroup and scenario analysis, repeated runs, calibration or selective prediction, and monitoring after release. It also asks who can inspect, override, and learn from failures. By integrating the assigned target papers with established scholarship, the synthesis clarifies which findings transfer across domains and which remain local to a benchmark, dataset, or experimental apparatus. The goal is a testable research program for bounded, traceable, and revisable systems.

Keywords

scientific knowledge graphs should preserve negative evidencescientificnegativeexperimentalpressure-temperaturebenchmarkoutput

References

Ma, Mingjun, et al. "MuSK: Multi-Scale Knowledge Learning for Provenance-Graph Anomaly Detection." *Computer Networks* 289 (2026): 112728.
Li, Yuanhao, et al. "BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models." *arXiv preprint arXiv:2605.09134* (2026).
Chen, Huawei, et al. "Synthesis and Stability of High-Energy-Density Niobium Nitrides under High-Pressure Conditions." *Inorganic Chemistry* 64.1 (2025): 692-700.
King, Samuel T., and Peter M. Chen. "Backtracking Intrusions." *Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles*, 2003, pp. 223-236.
Milajerdi, Sadegh M., et al. "HOLMES: Real-Time APT Detection through Correlation of Suspicious Information Flows." *2019 IEEE Symposium on Security and Privacy*, 2019, pp. 1137-1152.
Han, Xueyuan, et al. "UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats." *Network and Distributed System Security Symposium*, 2020.
Pasquier, Thomas, et al. "Runtime Analysis of Whole-System Provenance." *Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security*, 2017, pp. 1601-1614.
Schlichtkrull, Michael, et al. "Modeling Relational Data with Graph Convolutional Networks." *The Semantic Web*, Springer, 2018, pp. 593-607.
Hamilton, William L., Rex Ying, and Jure Leskovec. "Inductive Representation Learning on Large Graphs." *Advances in Neural Information Processing Systems*, vol. 30, 2017.
Velickovic, Petar, et al. "Graph Attention Networks." *International Conference on Learning Representations*, 2018.
Chandola, Varun, Arindam Banerjee, and Vipin Kumar. "Anomaly Detection: A Survey." *ACM Computing Surveys*, vol. 41, no. 3, 2009, article 15.
Sommer, Robin, and Vern Paxson. "Outside the Closed World: On Using Machine Learning for Network Intrusion Detection." *2010 IEEE Symposium on Security and Privacy*, 2010, pp. 305-316.
Liao, Xiaojing, et al. "Acing the IOC Game: Toward Automatic Discovery and Analysis of Open-Source Cyber Threat Intelligence." *Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security*, 2016, pp. 755-766.

Publication details

Journal: Frontiers in Integrative Science
Volume: 1 (2026)
Issue: 1 · Forthcoming issue
Article number: fis20260003
License: CC BY 4.0

[1] Ma, Mingjun, et al. "MuSK: Multi-Scale Knowledge Learning for Provenance-Graph Anomaly Detection." *Computer Networks* 289 (2026): 112728.

[2] Li, Yuanhao, et al. "BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models." *arXiv preprint arXiv:2605.09134* (2026).

[3] Chen, Huawei, et al. "Synthesis and Stability of High-Energy-Density Niobium Nitrides under High-Pressure Conditions." *Inorganic Chemistry* 64.1 (2025): 692-700.

[4] King, Samuel T., and Peter M. Chen. "Backtracking Intrusions." *Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles*, 2003, pp. 223-236.

[5] Milajerdi, Sadegh M., et al. "HOLMES: Real-Time APT Detection through Correlation of Suspicious Information Flows." *2019 IEEE Symposium on Security and Privacy*, 2019, pp. 1137-1152.

[6] Han, Xueyuan, et al. "UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats." *Network and Distributed System Security Symposium*, 2020.

[7] Pasquier, Thomas, et al. "Runtime Analysis of Whole-System Provenance." *Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security*, 2017, pp. 1601-1614.

[8] Schlichtkrull, Michael, et al. "Modeling Relational Data with Graph Convolutional Networks." *The Semantic Web*, Springer, 2018, pp. 593-607.

[9] Hamilton, William L., Rex Ying, and Jure Leskovec. "Inductive Representation Learning on Large Graphs." *Advances in Neural Information Processing Systems*, vol. 30, 2017.

[10] Velickovic, Petar, et al. "Graph Attention Networks." *International Conference on Learning Representations*, 2018.

[11] Chandola, Varun, Arindam Banerjee, and Vipin Kumar. "Anomaly Detection: A Survey." *ACM Computing Surveys*, vol. 41, no. 3, 2009, article 15.

[12] Sommer, Robin, and Vern Paxson. "Outside the Closed World: On Using Machine Learning for Network Intrusion Detection." *2010 IEEE Symposium on Security and Privacy*, 2010, pp. 305-316.

[13] Liao, Xiaojing, et al. "Acing the IOC Game: Toward Automatic Discovery and Analysis of Open-Source Cyber Threat Intelligence." *Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security*, 2016, pp. 755-766.