---
res:
  bibo_abstract:
  - "Ensemble methods are widely employed to improve generalization in machine learning.
    This has also prompted the adoption of ensemble learning for the knowledge graph
    embedding (KGE) models in performing link prediction. Typical approaches to this
    end train multiple models as part of the ensemble, and the diverse predictions
    are then averaged. However, this approach has some significant drawbacks. For
    instance, the computational overhead of training multiple models increases latency
    and memory overhead. In contrast, model merging approaches offer a promising alternative
    that does not require training multiple models. In this work, we introduce model
    merging, specifically weighted averaging, in\r\nKGE models. Herein, a running
    average of model parameters from a training epoch onward is maintained and used
    for predictions. To address this, we additionally propose an approach that selectively
    updates the running average of the ensemble model parameters only when the generalization
    performance improves on a validation dataset. We evaluate these two different
    weighted averaging approaches on link prediction tasks, comparing the state-of-the-art
    benchmark ensemble approach. Additionally, we evaluate the weighted averaging
    approach considering literal-augmented KGE models and multi-hop query answering
    tasks as well. The results demonstrate that the proposed weighted averaging approach
    consistently improves performance across diverse evaluation settings.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Rupesh
      foaf_name: Sapkota, Rupesh
      foaf_surname: Sapkota
      foaf_workInfoHomepage: http://www.librecat.org/personId=89326
  - foaf_Person:
      foaf_givenName: Caglar
      foaf_name: Demir, Caglar
      foaf_surname: Demir
  - foaf_Person:
      foaf_givenName: Arnab
      foaf_name: Sharma, Arnab
      foaf_surname: Sharma
  - foaf_Person:
      foaf_givenName: Axel-Cyrille
      foaf_name: Ngonga Ngomo, Axel-Cyrille
      foaf_surname: Ngonga Ngomo
  bibo_doi: https://doi.org/10.1145/3731443.3771365
  dct_date: 2025^xs_gYear
  dct_language: eng
  dct_publisher: ACM@
  dct_subject:
  - Knowledge Graphs
  - Embeddings
  - Ensemble Learning
  dct_title: Parameter Averaging in Link Prediction@
...