Name
Abstract | ||
|---|---|---|
Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. We propose a solution for automatically semantifying biological assays. Our solution juxtaposes the problem of automated semantification as classification versus clustering where the two methods are on opposite ends of the method complexity spectrum. Characteristically modeling our problem, we find the clustering solution significantly outperforms a deep neural network state-of-the-art classification approach. This novel contribution is based on two factors: 1) a learning objective closely modeled after the data outperforms an alternative approach with sophisticated semantic modeling; 2) automatically semantifying biological assays achieves a high performance F1 of nearly 83%, which to our knowledge is the first reported standardized evaluation of the task offering a strong benchmark model. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-031-08421-8_14 | International Conference of the Italian Association for Artificial Intelligence (AI*IA) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Anteghini | 1 | 1 | 1.39 |
| Jennifer D'Souza | 2 | 1 | 2.05 |
| Vitor A. P. Martins dos Santos | 3 | 1 | 1.05 |
| Sören Auer | 4 | 5711 | 418.56 |