@inproceedings{kotitsas-etal-2019-embedding,
title = "Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors",
author = "Kotitsas, Sotiris and
Pappas, Dimitris and
Androutsopoulos, Ion and
McDonald, Ryan and
Apidianaki, Marianna",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5032",
doi = "10.18653/v1/W19-5032",
pages = "298--308",
abstract = "Network Embedding (NE) methods, which map network nodes to low-dimensional feature vectors, have wide applications in network analysis and bioinformatics. Many existing NE methods rely only on network structure, overlooking other information associated with the nodes, e.g., text describing the nodes. Recent attempts to combine the two sources of information only consider local network structure. We extend NODE2VEC, a well-known NE method that considers broader network structure, to also consider textual node descriptors using recurrent neural encoders. Our method is evaluated on link prediction in two networks derived from UMLS. Experimental results demonstrate the effectiveness of the proposed approach compared to previous work.",
}
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%0 Conference Proceedings
%T Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors
%A Kotitsas, Sotiris
%A Pappas, Dimitris
%A Androutsopoulos, Ion
%A McDonald, Ryan
%A Apidianaki, Marianna
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 18th BioNLP Workshop and Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F kotitsas-etal-2019-embedding
%X Network Embedding (NE) methods, which map network nodes to low-dimensional feature vectors, have wide applications in network analysis and bioinformatics. Many existing NE methods rely only on network structure, overlooking other information associated with the nodes, e.g., text describing the nodes. Recent attempts to combine the two sources of information only consider local network structure. We extend NODE2VEC, a well-known NE method that considers broader network structure, to also consider textual node descriptors using recurrent neural encoders. Our method is evaluated on link prediction in two networks derived from UMLS. Experimental results demonstrate the effectiveness of the proposed approach compared to previous work.
%R 10.18653/v1/W19-5032
%U https://aclanthology.org/W19-5032
%U https://doi.org/10.18653/v1/W19-5032
%P 298-308
Markdown (Informal)
[Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors](https://aclanthology.org/W19-5032) (Kotitsas et al., BioNLP 2019)
ACL