3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in ontology-embedding-3
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Use these libraries to find ontology-embedding-3 models and implementations
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A novel ontology embedding method named Box2EL for the DL EL++ is proposed, which represents both concepts and roles as boxes, and models inter-concept relationships using a bumping mechanism, theoretically prove the soundness of Box2EL and conduct an extensive experimental evaluation.
This study proposes to model the compositional and expressive semantics of class labels by an OWL (Web Ontology Language) ontology, and further develop a new ZSL framework with ontology embedding.
This paper proposes a random walk and word embedding based ontology embedding method named OWL2Vec*, which encodes the semantics of an OWL ontology by taking into account its graph structure, lexical information and logical constructors.
Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from data-rich to data-poor event types and can be applied to new unseen event types, by establishing linkages to existing ones.
MIPO (Mutual Integration of Patient Journey and Medical Ontology) is proposed, a robust end-to-end framework that employs a Transformer-based architecture for representation learning and consistently outperforms baseline methods under both sufficient and limited data conditions.
This work proposes OntoProtein, the first general framework that makes use of structure in GO (Gene Ontology) into protein pre-training models, and constructs a novel large-scale knowledge graph that consists of GO and its related proteins, and gene annotation texts or protein sequences describe all nodes in the graph.
This paper proposes to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects of Zero-shot Learning.
This work analyzes, qualitatively and quantitatively, several graph projection methods that have been used to embed ontologies, and demonstrates the effect of the properties of graph projections on the performance of predicting axioms from ontology embeddings.
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