Search Results for author: Asan Agibetov

Found 10 papers, 7 papers with code

Neural sentence embedding models for semantic similarity estimation in the biomedical domain

1 code implementation1 Oct 2021 Kathrin Blagec, Hong Xu, Asan Agibetov, Matthias Samwald

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.

Semantic Similarity Semantic Textual Similarity +4

Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphs

1 code implementation10 Dec 2020 Simon Ott, Laura Graf, Asan Agibetov, Christian Meilicke, Matthias Samwald

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.

Clustering Knowledge Graphs +1

Neural graph embeddings as explicit low-rank matrix factorization for link prediction

no code implementations16 Nov 2020 Asan Agibetov

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.

Graph Embedding Link Prediction

Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes

1 code implementation15 May 2020 Asan Agibetov, Matthias Samwald

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.

Benchmarking Link Prediction

OpenBioLink: A benchmarking framework for large-scale biomedical link prediction

1 code implementation10 Dec 2019 Anna Breit, Simon Ott, Asan Agibetov, Matthias Samwald

SUMMARY: Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks.

Benchmarking BIG-bench Machine Learning +1

Global and local evaluation of link prediction tasks with neural embeddings

no code implementations27 Jul 2018 Asan Agibetov, Matthias Samwald

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.

Binary Classification Knowledge Graphs +1

Fast and scalable learning of neuro-symbolic representations of biomedical knowledge

no code implementations30 Apr 2018 Asan Agibetov, Matthias Samwald

In this work we address the problem of fast and scalable learning of neuro-symbolic representations for general biological knowledge.

Entity Embeddings Link Prediction +1

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