Automatic functional annotation of predicted active sites - European ...

contexts. This summary of text has also been compared with an ”unstructured knowledge
database”, where information is present, but difficult to retrieve due to the complexity of
natural language. According to Sidhu,
”[...] it is generally acknowledged that only 20 per cent of biological knowledge
and data is available in a structured format or a database. The remaining
80 per cent of biological information is hidden in the unstructured, free text
of scientific publications.” [SDC06]
In context of information extraction, the data to be extracted from an article are
words (keywords) regarding biological concepts that could summarise the key message
of the article.
At first glance, abstract texts have a high density of keywords but a
low coverage of information, while full-texts cover a larger but disperse quantity of data
[FKY + 01] [YHF + 02] [SPIBA03] [SWS + 04] [NBD + 06].
Another key distinction between abstract texts and full-texts is the availability of
data resources. Biomedical abstract texts can be publicly downloaded from MEDLINE
without restriction, while full-texts from various journals are only available for subscribed
customers.
Although some full-text articles are accessible through various initiatives
[BMC08] [Plo08] [PMC08], the extraction of information from a whole document is expected
to be much more complex than from an abstract text. For example, a biological
feature of a residue may be expressed over several sentences, requiring a co-reference
resolution of the residue and the feature.
2.2 Protein structure data mining
Data mining is an analytic method to identify valid, and novel patterns in data. A general
data mining solution does not exist. Instead human data mining expertise and human
domain expertise are required to solve each specific data mining problem. A data mining
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