Our Team

We're a passionate group of experts who have worked together on complex graph problems for years:

• 3 dedicated researchers with backgrounds in knowledge representation

• 2 backend and 1 data engineers with extensive graph database experience

• 2 data scientists who specialize in graph analytics

• 1 graph scientist focused on optimization algorithms

• 1 graph network expert

Company Name (if applicable)

Project details

If you ask ChatGPT, Claude or other no-internet systems who is the Pope - you'll get an outdated answer. Current graph systems can be built quite fast, but there might an issue with getting time-relevant results and "time-travel". This is especially important in fields of news, science, etc - some information can get updated quite fast and reasoning models relying on graph systems might not get actual results.

Anyone who's worked with temporal knowledge graphs knows the challenge of interpreting and explaining time-dependent relationships and reasoning paths. In complex logic systems like MeTTa, inference behavior can become opaque—particularly when forward/backward chaining, multi-rule propagation, or context-sensitive reasoning is involved. We've experienced these challenges firsthand, and they've motivated us to develop this proposal. Our vision is to create an open, extensible framework that transforms complex temporal graph structures into natural language explanations while maintaining logical consistency through TMS principles—making AGI reasoning more transparent, traceable, and trustworthy.

We're focusing on three key areas that we believe will make the most significant impact:

1. Natural Language Generation for Temporal Graph Structures with Dependency Tracking

We're determined to overcome the limitations of traditional graph visualization and technical query languages. Our approach leverages advanced natural language generation techniques to express temporal relationships in intuitive human language while incorporating TMS dependency tracking.

We're particularly excited about:

• Developing specialized templates and linguistic patterns that accurately convey temporal intervals, sequences, and causality

• Implementing contextual explanation strategies that adapt to different user knowledge levels and domains

• Creating narrative frameworks that can explain complex temporal reasoning paths as coherent stories

• Integrating TMS-based justification structures that explain not just what the system believes, but why it believes it

We're also exploring transformer-based models fine-tuned specifically for temporal relationship verbalization. Our preliminary tests show promising results in generating explanations that distinguish between different temporal representations (timestamps, intervals, durations) and accurately convey their significance.

For complex temporal reasoning chains, we're adapting sequence-to-sequence models to transform graph traversal paths into step-by-step explanations—early results suggest this approach significantly improves user comprehension of machine reasoning compared to traditional graph visualizations.

Additionally, we're investigating techniques to generate counterfactual explanations that help users understand how temporal changes would affect outcomes ("If event X had occurred 3 months earlier, Y would likely have happened instead of Z"). These counterfactual explanations will be supported by TMS contextual reasoning capabilities that can maintain multiple belief sets simultaneously.

2. Temporal Query Translation and Interpretation with Consistency Maintenance

We're frustrated by the gap between natural human questions about time and the structured query languages needed to extract answers from temporal graphs. Our goal is to create a bidirectional translation system that converts natural language temporal queries into precise graph queries and translates results back into explanatory language while ensuring logical consistency.

We're exploring:

• Advanced temporal expression recognition that captures nuanced time references in natural language

• Query intent classification to distinguish between point-in-time queries, interval queries, sequence queries, and causal queries

• Natural language interfaces that allow non-technical users to explore temporal knowledge through conversation

• **Browsable, observable logic workflows that explicitly answer crucial questions like:

• What rules were triggered?

• Why did a given conclusion hold—or not hold?

• Where did a contradiction arise?**

One of our most ambitious goals is to develop explanation frameworks for temporal inference—using narrative structures to explain how the system reasoned from temporal facts to derive new knowledge. We're developing techniques to verbalize temporal logic operations and probabilistic reasoning in ways that maintain accuracy while remaining intuitive to users.

Through TMS integration, our system will provide first-class treatment of queries and their results, enabling developers and researchers to:

• Assert rules that generate other assertions

• Assert rules that issue queries

• Observe query results as first-class knowledge base elements

3. Benchmarks for Temporal Graph Explainability with TMS Evaluation

We're concerned about the lack of standardized evaluation methods for assessing the quality of temporal explanations. Our team is designing a comprehensive benchmark suite that evaluates aspects that truly matter: clarity, accuracy, consistency maintenance, and utility of explanations for temporal knowledge graphs.

Our benchmarks will measure:

• Explanation fidelity across different temporal relationship types (point-in-time, intervals, sequences)

• Comprehension metrics based on user understanding after reading explanations

• Explanation efficiency at different levels of temporal reasoning complexity

• Cross-domain applicability from historical to financial to scientific contexts

• Robustness to different temporal granularities (seconds to decades)

• Consistency maintenance under revision of temporal facts

• Quality of contradiction detection and resolution

Beyond simple factual explanations, we're focusing on complex temporal reasoning tasks like explaining causal chains, temporal anomalies, and counterfactual scenarios—the kinds of explanations that distinguish truly intelligent systems from mere information retrievers.

We're particularly inspired by the need for AI systems to explain their temporal reasoning in fields like medicine, finance, and historical analysis, where understanding the sequence and timing of events is crucial for trust and decision-making.

Our project naturally aligns with the Hyperon framework by providing essential explainability tools that will enhance temporal reasoning capabilities in MeTTa. We're committed to exploring compatibility with the MORK system to enable real-time generation of natural language explanations for temporal inference paths. The integration of TMS principles will significantly improve interpreter correctness, accelerate theorem prover debugging, and enable interactive, human-understandable AI development.

1. Wu, H., Cheng, J., Huang, S., Ke, Y., Lu, Y., & Xu, Y. (2014). Path problems in temporal graphs. Proceedings of the VLDB Endowment, 7(9), 721-732.
   
   

2. Paranjape, A., Benson, A. R., & Leskovec, J. (2017). Motifs in temporal networks. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining.
   
   

3. Rozemberczki, B., Scherer, P., Kiss, O., Sarkar, R., & Ferenci, T. (2022). PyTorch geometric temporal: Spatiotemporal signal processing with neural machine learning models. arXiv preprint.
   
   

4. Casteigts, A., Flocchini, P., Quattrociocchi, W., & Santoro, N. (2012). Time-varying graphs and dynamic networks. International Journal of Parallel, Emergent and Distributed Systems, 27(5), 387-408.
   
   

5. Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., & Bronstein, M. (2020). Temporal graph networks for deep learning on dynamic graphs. arXiv preprint.
   
   

6. Huang, Haixing, Jinghe Song, Xuelian Lin, Shuai Ma, and Jinpeng Huai. "Tgraph: A temporal graph data management system." In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 2469-2472. 2016.
   
   

7. Rahman, Aniq Ur, and Justin P. Coon. "A Primer on Temporal Graph Learning." arXiv preprint arXiv:2401.03988 (2024).
   
   

8. Sahili, Zahraa Al, and Mariette Awad. "Spatio-temporal graph neural networks: A survey." arXiv preprint arXiv:2301.10569 (2023).
   
   

9. Rossetti, G., & Cazabet, R. (2018). Community discovery in dynamic networks: A survey. ACM Computing Surveys, 51(2), 1-37.
   
   

10. Exploring Temporal and Spatial Graph Databases

11. Michail, Othon. "An introduction to temporal graphs: An algorithmic perspective." Internet Mathematics 12, no. 4 (2016): 239-280.https://www.internetmathematicsjournal.com/api/v1/articles/1606-an-introduction-to-temporal-graphs-an-algorithmic-perspective.pdf

12. ATGL: Awesome Temporal Graph Learning - GitHub, accessed May 2, 2025, https://github.com/MGitHubL/Awesome-Temporal-Graph-Learning

13. Casteigts, Arnaud, Kitty Meeks, George B. Mertzios, and Rolf Niedermeier. "Temporal graphs: Structure, algorithms, applications (dagstuhl seminar 21171)." Dagstuhl Reports 11, no. 3 (2021): 16-46.

14. Michail, Othon. "An introduction to temporal graphs: An algorithmic perspective." Internet Mathematics 12, no. 4 (2016): 239-280.

Open Source Licensing

Background & Experience

This proposal is one of five parts of a unified toolkit to be developed in parallel across a team of 14-16 developers and scientists. We have already a case of successful grant completion for DeepFunding.

Our team includes current employees from Yandex and Intel, former Linkedin, 3 university lecturers, and 7 PhDs. We're proud of our 10+ presidential awards, several patents and 30+ publications, including multiple technical books from a well known publishing house.

If we are awarded funding for 4 out of 5 proposals, we are committed to developing the 5th one at no additional cost.

We believe it’s better to have one toolkit than a kit of tools, that need to be duct-taped together.

We aim to deliver a robust, extensible, modular, production-ready ecosystem that can evolve with future RFPs, enabling seamless adoption, innovation, and collaboration. This approach will maximize the utility of knowledge graph fundamentals and pull in other innovative features and technologies from DeepFunding.