Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors
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.
Cite
@article{arxiv.1906.05939,
title = {Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors},
author = {Sotiris Kotitsas and Dimitris Pappas and Ion Androutsopoulos and Ryan McDonald and Marianna Apidianaki},
journal= {arXiv preprint arXiv:1906.05939},
year = {2019}
}
Comments
Proceedings of the 18th Workshop on Biomedical Natural Language Processing (BioNLP 2019) of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, 2019