Name
Abstract | ||
|---|---|---|
Domain modelling transforms informal requirements written in natural language in the form of problem descriptions into concise and analyzable domain models. As the manual construction of these domain models is often time-consuming, error-prone, and labor-intensive, several approaches already exist to automate domain modelling. However, the current approaches suffer from lower accuracy of extracted... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MODELS-C53483.2021.00090 | 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) |
Keywords | DocType | ISBN |
Knowledge engineering,Analytical models,Transforms,Switches,Manuals,Machine learning,Feature extraction | Conference | 978-1-6654-2484-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rijul Saini | 1 | 1 | 3.06 |
| Gunter Mussbacher | 2 | 12 | 9.02 |
| Jin L.C. Guo | 3 | 1 | 2.37 |
| Jörg Kienzle | 4 | 732 | 69.38 |