Abstract
Systems for providing context to users can experience latency and data fragmentation, which may arise when systems operate reactively by executing synchronous queries across siloed data sources after a user interaction. An asynchronous process can periodically synchronize data from various sources, such as email and calendars, to build and maintain a localized vector knowledge graph that can model entities and their relationships. A proactive compute engine may use system events, for example, the arrival of a new message, as triggers to query the graph, pre-compute a relevant context payload, and cache the result. This approach may reduce latency associated with on-demand data retrieval, as pre-computed contextual information can be delivered to a user interface on a computing device, such as a smartphone or wearable device, with reduced delay when the user interacts with the triggering item.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Singhal, Parnika and Singhal, Dhruv, "Proactive Context Generation via Asynchronous Knowledge Graph Synchronization", Technical Disclosure Commons, (July 28, 2026)
https://www.tdcommons.org/dpubs_series/11201