Section: Research Program

Elements on Text Mining

Keywords:

knowledge discovery form large collection of texts, text mining, information extraction, document annotation, ontologies

The objective of a text mining process is to extract useful knowledge units from large collections of texts [80] , [88] . The text mining process shows specific characteristics due to the fact that texts are complex objects written in natural language. The information in a text is expressed in an informal way, following linguistic rules, making text mining a particular task. To avoid information dispersion, a text mining process has to take into account –as much as possible– paraphrases, ambiguities, specialized vocabulary, and terminology. This is why the preparation of texts for text mining is usually dependent on linguistic resources and methods.

From a KDDK perspective, text mining is aimed at extracting knowledge units from texts with the help of background knowledge encoded within an ontology (also useful for annotation and relating notions present in texts). Text mining is especially useful in the context of semantic web for ontology engineering [86] , [85] , [84] . In the Orpailleur team, the focus is put on the mining of real-world texts in application domains such as biology and medicine, using mainly symbolic data mining methods. Accordingly, the text mining process may be involved in a loop used to enrich and to extend linguistic resources. In turn, linguistic and ontological resources can be exploited to guide a “knowledge-based text mining process”.