Ontology Embedding
15 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Ontology Embedding
Most implemented papers
OWL2Vec*: Embedding of OWL Ontologies
Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web.
Dual Box Embeddings for the Description Logic EL++
OWL ontologies, whose formal semantics are rooted in Description Logic (DL), have been widely used for knowledge representation.
Ontology-guided Semantic Composition for Zero-Shot Learning
China-UK-ZSL/Resources_for_KZSL
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Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relationship with some side information.
OntoED: Low-resource Event Detection with Ontology Embedding
231sm/Reasoning_In_EE
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ACL 2021
Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types.
MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning
xueping/mipo
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Hence, some recent works train healthcare representations by incorporating medical ontology, by self-supervised tasks like diagnosis prediction, but (1) the small-scale, monotonous ontology is insufficient for robust learning, and (2) critical contexts or dependencies underlying patient journeys are barely exploited to enhance ontology learning.
OntoProtein: Protein Pretraining With Gene Ontology Embedding
zjunlp/ontoprotein
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ICLR 2022
We construct 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.
Disentangled Ontology Embedding for Zero-shot Learning
zjukg/dozsl
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In this paper, we focus on ontologies for augmenting ZSL, and propose to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects.
From axioms over graphs to vectors, and back again: evaluating the properties of graph-based ontology embeddings
bio-ontology-research-group/ontology-graph-projections
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Several approaches have been developed that generate embeddings for Description Logic ontologies and use these embeddings in machine learning.
Lattice-preserving $\mathcal{ALC}$ ontology embeddings with saturation
bio-ontology-research-group/cate
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Although some approaches aim to embed more descriptive DLs like $\mathcal{ALC}$, those methods require the existence of individuals, while many real-world ontologies are devoid of them.
Embedding Ontologies via Incorporating Extensional and Intensional Knowledge
sky-fish23/eike
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Extensional knowledge provides information about the concrete instances that belong to specific concepts in the ontology, while intensional knowledge details inherent properties, characteristics, and semantic associations among concepts.
