Name
Abstract | ||
|---|---|---|
Bio-ontologies such as the Gene Ontology and ChEBI are characterized by large sizes and relatively low expressivity. However, ongoing efforts aim to increase the formalisation of these ontologies by adding full definitions (equivalent classes). This increase in complexity results in a decrease of performance for standard reasoning tasks. In this paper, we explore the contribution which modularization can play in the evolution of bio-ontologies. In particular, we focus on ChEBI, the ontology of chemical entities of biological interest. ChEBI consists of around 25,000 classes, organised into a structure-based chemical classification and enriched with a role-based classification of their biologically properties. Ontology modularization - partitioning large ontologies into smaller, more manageable chunks - provides the only feasible mechanism for sustainably maintaining the large-scale and ever-growing ontologies in the biomedical domain. We evaluate available ontology partitioning tools. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.3233/978-1-60750-799-4-63 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
modularity,modularization,bio-ontologies,performance,ChEBI | Ontology (information science),Systems engineering,Computer science,Modular programming | Conference |
Volume | ISSN | Citations |
230 | 0922-6389 | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Janna Hastings | 1 | 714 | 62.06 |
| Colin R. Batchelor | 2 | 180 | 13.20 |
| Christoph Steinbeck | 3 | 1092 | 94.06 |
| Stefan Schulz | 4 | 1092 | 127.03 |