History repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909184909377536 |
|---|---|
| author | Gastinger, Julia Meilicke, Christian Errica, Federico Sztyler, Timo Schuelke, Anett Stuckenschmidt, Heiner |
| author_facet | Gastinger, Julia Meilicke, Christian Errica, Federico Sztyler, Timo Schuelke, Anett Stuckenschmidt, Heiner |
| contents | Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in Knowledge Graphs for future timesteps based on a history of Knowledge Graphs. To this day, standardized evaluation protocols and rigorous comparison across TKG models are available, but the importance of simple baselines is often neglected in the evaluation, which prevents researchers from discerning actual and fictitious progress. We propose to close this gap by designing an intuitive baseline for TKG Forecasting based on predicting recurring facts. Compared to most TKG models, it requires little hyperparameter tuning and no iterative training. Further, it can help to identify failure modes in existing approaches. The empirical findings are quite unexpected: compared to 11 methods on five datasets, our baseline ranks first or third in three of them, painting a radically different picture of the predictive quality of the state of the art. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16726 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | History repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting Gastinger, Julia Meilicke, Christian Errica, Federico Sztyler, Timo Schuelke, Anett Stuckenschmidt, Heiner Machine Learning Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in Knowledge Graphs for future timesteps based on a history of Knowledge Graphs. To this day, standardized evaluation protocols and rigorous comparison across TKG models are available, but the importance of simple baselines is often neglected in the evaluation, which prevents researchers from discerning actual and fictitious progress. We propose to close this gap by designing an intuitive baseline for TKG Forecasting based on predicting recurring facts. Compared to most TKG models, it requires little hyperparameter tuning and no iterative training. Further, it can help to identify failure modes in existing approaches. The empirical findings are quite unexpected: compared to 11 methods on five datasets, our baseline ranks first or third in three of them, painting a radically different picture of the predictive quality of the state of the art. |
| title | History repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.16726 |