1.1 Text Mining: Aims, Challenges, and Solutions

With an overwhelming amount of biomedical knowledge recorded in texts, it is not surprising that there is so much interest in techniques that can identify, extract, manage, integrate, and exploit this knowledge, and can discover new, hidden, or unsuspected knowledge. In the past few years, there has been an upsurge of research papers on the topic of text mining from biomedical literature. The primary goal of text mining is to retrieve knowledge that is hidden in text, and to present the distilled knowledge to users in a concise form. The advantage of text mining is that it enables scientists to efficiently and systematically collect, maintain, interpret, curate, and discover knowledge needed for research or education.

A commonly accepted definition of text mining, to which we also adhere, is provided by Hearst ^[1] She characterizes text mining as the process of discovering and extracting knowledge from unstructured data, contrasting it with data mining, which discovers knowledge from structured data. Under this view, text mining comprises three major activities: information retrieval, to gather relevant texts; information extraction, to identify and extract a range of specific types of information from texts of interest; and data mining, to find associations among the pieces of information extracted from many different texts.

In this book, we leave aside information retrieval aspects. These have been widely investigated and reported, and most biologists use information retrieval, in some form or another, in their daily work.