Topic Tracker: By maintaining the profile of users and based on the different topics browsed by the user, the system predicts other topics of interest for the user. 5. Scientific advances: Facilitate conceptual research of biomedical literature and work on hypotheses about the causes of rare diseases. Challenges of Text Mining • The main challenge faced in Text Mining is the complexity arising from the natural language itself. Natural language is not free from the problem of ambiguity; it contains words that have the capacity to be understood in two or more possible meanings or ways. Ambiguity gives a natural language its flexibility and usability; it therefore cannot be entirely eliminated from natural language. • Semantic analysis methods used for text mining purposes are computationally expensive and operate on the order of a few words per second. This poses a challenge as to how semantic analysis can be made efficient and scalable for very large corpora of text. • Multilingual text is another setback: text refinement algorithms are needed to refine multilingual text documents and produce language-independent intermediate forms. • Additionally, legal aspects associated with copyright laws and database usage may pose a barrier to data mining if permission from the owner is not obtained.