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First Online: 17 September 2023

Ethical Statement

: While TKG Forecasting has the potential to enable predictions for complex and dynamic systems, we argue that inconsistencies in experimental procedures and evaluation settings can lead to distorted comparisons among models, and ultimately, misinterpretation of results. Therefore, with our work, we want to highlight the importance of transparency and reproducibility in scientific research, as well as the importance of rigorous and reliable scientific practice. In this context we have identified inconsistencies in evaluation settings and provided a unified evaluation protocol. We ensure transparency by providing a URL to a GitHub repository containing our evaluation code. Within this repository, we use forked submodules to explicitly link to the original assets. Additionally, we report the training details, such as hyperparameters, in the supplementary material of our work.

: While we have not focused on increasing the interpretability of individual models, we acknowledge the importance of explainability and interpretability in the field. Therefore, we note that among the compared models, xERTE [] and TLogic [] address some aspects of explainability and interpretability.

: We did not evaluate the predictions of existing models on bias and fairness as it was out of scope for this work. However, we recognize that it is essential to increase fairness in the comparison of TKG Forecasting models. Therefore, we highlight inconsistencies and provide a unified evaluation protocol to improve comparability and fairness for existing models.

: In terms of data collection and use, we used publicly available research datasets for our evaluation. We did not use the data for profiling individuals, and it does not contain offensive content. However, it is important to note that even publicly available data can be subject to privacy regulations, and we have taken measures to ensure that our data usage complies with applicable laws and regulations.

: As this study focuses purely on evaluation of existing models, it does not induce direct risk. However, we recognize that TKG Forecasting models can have real-world consequences, especially when applied in domains such as finance and healthcare. Therefore, as the results in Sect.  show, we want to stress again that predictions can be unreliable and incomplete, and that these limitations have to be acknowledged when using them for decision making.

Conference Information

Conference Acronym: ECML PKDD

Conference Name: Joint European Conference on Machine Learning and Knowledge Discovery in Databases

Conference City: Turin

Conference Country: Italy

Conference Year: 2023

Conference Start Date: 18 September 2023

Conference End Date: 22 September 2023

Conference Number: 23

Conference ID: ecml2023

Conference URL: https://2023.ecmlpkdd.org/

Peer Review Information (provided by the conference organizers)

Type: Double-blind

Conference Management System: CMT

Number of Submissions Sent for Review: 829

Number of Full Papers Accepted: 196

Number of Short Papers Accepted: 0

Acceptance Rate of Full Papers: 24% - The value is computed by the equation "Number of Full Papers Accepted / Number of Submissions Sent for Review * 100" and then rounded to a whole number.

Average Number of Reviews per Paper: 3.63

Average Number of Papers per Reviewer: 4.5

External Reviewers Involved: Yes

Additional Info on Review Process: Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.