# On a Generalized Framework for Time-Aware Knowledge Graphs

arXiv: https://arxiv.org/abs/2207.09964 · HTML: https://mlsanity.com/paper/2207.09964/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Learning Sequence Encoders for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/1809.03202/) (2018)
- [Efficiently Embedding Dynamic Knowledge Graphs](https://mlsanity.com/paper/1910.06708/) (2019)

## Built on by (most cited first, 4 of 4)

- [GKG-LLM: A Unified Framework for Generalized Knowledge Graph Construction](https://mlsanity.com/paper/2503.11227/) (2025)
- [Logic Augmented Generation](https://mlsanity.com/paper/2411.14012/) (2024)
- [GeoRDF2Vec Learning Location-Aware Entity Representations in Knowledge Graphs](https://mlsanity.com/paper/2504.17099/) (2025)
- [Knowledge Graph Completion for Action Prediction on Situational Graphs -- A Case Study on Household Tasks](https://mlsanity.com/paper/2508.13675/) (2025)

## Abstract (arXiv)

Knowledge graphs have emerged as an effective tool for managing and standardizing semistructured domain knowledge in a human- and machine-interpretable way. In terms of graph-based domain applications, such as embeddings and graph neural networks, current research is increasingly taking into account the time-related evolution of the information encoded within a graph. Algorithms and models for stationary and static knowledge graphs are extended to make them accessible for time-aware domains, where time-awareness can be interpreted in different ways. In particular, a distinction needs to be made between the validity period and the traceability of facts as objectives of time-related knowledge graph extensions. In this context, terms and definitions such as dynamic and temporal are often used inconsistently or interchangeably in the literature. Therefore, with this paper we aim to provide a short but well-defined overview of time-aware knowledge graph extensions and thus faciliate future research in this field as well.