Facts from text--is text mining ready to deliver?

Abstract

The mining of information from scientific literature using computational tools has tremendous potential for knowledge discovery, but how close are we to realizing this potential?

Figure 1. Medline Article Deluge

This figure shows the exploding number of articles available from Medline over the past 65 years (data retrieved from the SRS server at the European Bioinformatics Institute; www.ebi.ac.uk/). In 2003, about 560,000 articles were added to Medline, and from 2000 to 2003, 2 million articles. (Articles already registered for 2005 are given as well.)

Figure 2. Zipf's Law

Zipf's eponymous law is illustrated by the analysis of 30,000 Medline abstracts (4,952,878 occurrences of words; 144,841 different words). Frequent terms account for a large portion of the text, but a large fraction of terms appear at a low frequency and often only once (69,782 words appear only once). Zipf was a linguistic professor at Harvard University [3].

Figure 3. GOAnnotator

The illustrated software tool brings together data from text mining and from databases to support curators in the GO annotation of proteins (Couto FM, Lee V, Dimmer E, Camon E, Apweiler R, et al., unpublished data). Here a protein is shown in conjunction with the GO terms that have been gathered from various databases and attributed to the protein through electronic annotation. Both are evaluated against similar GO terms extracted from text documents. The curator looks into the evidence and decides whether any of the GO terms extracted from the documents should be assigned to the protein.