Section: Scientific Foundations
Elements on Text Mining
- Text mining
is a process for extracting knowledge units from large collections of texts, units that can be interpreted and reused within knowledge-based systems.
The objective of a text mining process is to extract new and useful knowledge units in a large set of texts [66] , [74] [62] . 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 the mining process more complex. 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, the text mining process is aimed at extracting new knowledge units from texts with the help of background knowledge encoded within an ontology and which is useful to relate notions present in a text, to guide and to help the text mining process. Text mining is especially useful in the context of semantic web, for manipulating textual documents by their content.
The studies on text mining carried out in the Orpailleur team hold on real-world texts in application domains such as astronomy, biology and medicine, using mainly symbolic data mining methods [13] . This is in contrast with text analysis approaches dealing with specific language phenomena. The language in texts is considered as a way for presenting and accessing information, and not as an object to be studied for its own. 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”.
