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Found 10 papers, 7 papers with code
Neural sentence embedding models for semantic similarity estimation in the biomedical domain
BACKGROUND: In this study, we investigated the efficacy of current state-of-the-art neural sentence embedding models for semantic similarity estimation of sentences from biomedical literature.
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Sentence Embeddings For Biomedical Texts
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Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphs
SAFRAN yields new state-of-the-art results for fully interpretable link prediction on the established general-purpose benchmark FB15K-237 and the large-scale biomedical benchmark OpenBioLink.
Neural graph embeddings as explicit low-rank matrix factorization for link prediction
Learning good quality neural graph embeddings has long been achieved by minimizing the point-wise mutual information (PMI) for co-occurring nodes in simulated random walks.
Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes
Recently, link prediction algorithms based on neural embeddings have gained tremendous popularity in the Semantic Web community, and are extensively used for knowledge graph completion.
Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems.
OpenBioLink: A benchmarking framework for large-scale biomedical link prediction
SUMMARY: Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks.
Global and local evaluation of link prediction tasks with neural embeddings
We focus our attention on the link prediction problem for knowledge graphs, which is treated herein as a binary classification task on neural embeddings of the entities.
Breaking-down the Ontology Alignment Task with a Lexical Index and Neural Embeddings
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems.
Fast and scalable learning of neuro-symbolic representations of biomedical knowledge
In this work we address the problem of fast and scalable learning of neuro-symbolic representations for general biological knowledge.
