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Ontologies are critical for organizing and interpreting complex domain-specific knowledge, with applications in data integration, functional prediction, and knowledge discovery. As the manual curation of ontology annotations becomes increasingly infeasible due to the exponential growth of biomedical and genomic data, natural language processing (NLP)-based systems have emerged as scalable alternatives. Evaluating these systems requires robust semantic similarity metrics that account for hierarchical and partially correct relationships often present in ontology annotations. This study explores the integration of graph-based and language-based embeddings to enhance the performance of semantic similarity metrics. Combining embeddings generated via Node2Vec and large language models (LLMs) with traditional semantic similarity metrics, we demonstrate that hybrid approaches effectively capture both structural and semantic relationships within ontologies. Our results show that combined similarity metrics outperform individual metrics, achieving high accuracy in distinguishing child–parent pairs from random pairs. This work underscores the importance of robust semantic similarity metrics for evaluating and optimizing NLP-based ontology annotation systems. Future research should explore the real-time integration of these metrics and advanced neural architectures to further enhance scalability and accuracy, advancing ontology-driven analyses in biomedical research and beyond.
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Graph Embeddings for Outage Prediction
This paper discusses how the risk of electricity grid outages is predicted using machine learning on historical data enhanced by graph embeddings of the distribution network. The process of graph creation using different embedding approaches is described. Several graph constructing strategies are used to create a graph, which is then transformed into the form acceptable for ML algorithm training. The impact of incorporating different graph embeddings on outage risk prediction is evaluated. The method used for graph embeddings is Node2Vec. The grid search is performed to find optimal hyperparameters of Node2Vec. The resulting accuracy metrics for a set of different hyperparameters are presented. The resulting metrics are compared against base scenario, where no graph embeddings were used.
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- Award ID(s):
- 1636772
- PAR ID:
- 10381125
- Date Published:
- Journal Name:
- 2021 North American Power Symposium (NAPS)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/naps52732.2021.9654696
