Using Text Mining and Link Analysis for Software Mining
Abstract
Many data mining techniques are these days in use for ontology learning – text mining, Web mining, graph mining, link analysis, relational data mining, and so on. In the current state-of-the-art bundle there is a lack of “software mining” techniques. This term denotes the process of extracting knowledge out of source code. In this paper we approach the software mining task with a combination of text mining and link analysis techniques. We discuss how each instance (i.e. a programming construct such as a class or a method) can be converted into a feature vector that combines the information about how the instance is interlinked with other instances, and the information about its (textual) content. The so-obtained feature vectors serve as the basis for the construction of the domain ontology with OntoGen, an existing system for semi-automatic data-driven ontology construction.
Cite
Text
Grcar et al. "Using Text Mining and Link Analysis for Software Mining." European Conference on Machine Learning, 2007. doi:10.1007/978-3-540-68416-9_1Markdown
[Grcar et al. "Using Text Mining and Link Analysis for Software Mining." European Conference on Machine Learning, 2007.](https://mlanthology.org/ecmlpkdd/2007/grcar2007ecml-using/) doi:10.1007/978-3-540-68416-9_1BibTeX
@inproceedings{grcar2007ecml-using,
title = {{Using Text Mining and Link Analysis for Software Mining}},
author = {Grcar, Miha and Grobelnik, Marko and Mladenic, Dunja},
booktitle = {European Conference on Machine Learning},
year = {2007},
pages = {1-12},
doi = {10.1007/978-3-540-68416-9_1},
url = {https://mlanthology.org/ecmlpkdd/2007/grcar2007ecml-using/}
}