Knowledge Discovery in Biology and Biotechnology Texts: A Review of Techniques, Evaluation Strategies, and Applications

Abstract

Arguably, the richest source of knowledge (as opposed to fact and data collections) about biology and biotechnology is captured in natural-language documents such as technical reports, conference proceedings and research articles. The automatic exploitation of this rich knowledge base for decision making, hypothesis management (generation and testing) and knowledge discovery constitutes a formidable challenge. Recently, a set of technologies collectively referred to as knowledge discovery in text (KDT) has been advocated as a promising approach to tackle this challenge. KDT comprises three main tasks: information retrieval, information extraction and text mining. These tasks are the focus of much recent scientific research and many algorithms have been developed and applied to documents and text in biology and biotechnology. This article introduces the basic concepts of KDT, provides an overview of some of these efforts in the field of bioscience and biotechnology, and presents a framework of commonly used techniques for evaluating KDT methods, tools and systems.

KEYWORDS:

• knowledge discovery in text
• text mining
• information retrieval
• information extraction
• bioinformatics

Notes

¹A confusion matrix (sometimes referred to as contingency table) is used to record and analyze the relationship between two or more variables. Usually, the variables are categorical variables. In the IR case the variables involved are relevancy (‘Doc d_k Relevant?’) and retrieval (‘Doc d_k Retrieved?’), each associated with the value set {Yes, No}. The cells of the matrix record the frequency of the various value co-occurrences. In Table 2, we are looking at individual documents, here the frequency for a particular value combination can either be zero or one.