Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors

Sotiris Kotitsas, Dimitris Pappas, Ion Androutsopoulos, Ryan McDonald, Marianna Apidianaki


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.
Anthology ID:
W19-5032
Volume:
Proceedings of the 18th BioNLP Workshop and Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
298–308
Language:
URL:
https://aclanthology.org/W19-5032
DOI:
10.18653/v1/W19-5032
Bibkey:
Cite (ACL):
Sotiris Kotitsas, Dimitris Pappas, Ion Androutsopoulos, Ryan McDonald, and Marianna Apidianaki. 2019. Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 298–308, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors (Kotitsas et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-5032.pdf
Code
 SotirisKot/Content-Aware-N2V
Data
IS-APART-OF