PhD ThesisBiological databases have become an integral part of the life sciences, being used
to store, organise and share ever-increasing quantities and types of data. Biological
databases are typically centred around raw data, with individual entries being
assigned to a single piece of biological data, such as a DNA sequence. Although essential,
a reader can obtain little information from the raw data alone. Therefore,
many databases aim to supplement their entries with annotation, allowing the current
knowledge about the underlying data to be conveyed to a reader. Although annotations
come in many di erent forms, most databases provide some form of free text
annotation.
Given that annotations can form the foundations of future work, it is important that a
user is able to evaluate the quality and correctness of an annotation. However, this is
rarely straightforward. The amount of annotation, and the way in which it is curated,
varies between databases. For example, the production of an annotation in some
databases is entirely automated, without any manual intervention. Further, sections
of annotations may be reused, being propagated between entries and, potentially,
external databases. This provenance and curation information is not always apparent
to a user.
The work described within this thesis explores issues relating to biological annotation
quality. While the most valuable annotation is often contained within free text, its lack
of structure makes it hard to assess. Initially, this work describes a generic approach
that allows textual annotations to be quantitatively measured. This approach is based
upon the application of Zipf's Law to words within textual annotation, resulting in a
single value, . The relationship between the value and Zipf's principle of least e ort
provides an indication as to the annotations quality, whilst also allowing annotations
to be quantitatively compared.
Secondly, the thesis focuses on determining annotation provenance and tracking any
subsequent propagation. This is achieved through the development of a visualisation
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framework, which exploits the reuse of sentences within annotations. Utilising this
framework a number of propagation patterns were identi ed, which on analysis appear
to indicate low quality and erroneous annotation.
Together, these approaches increase our understanding in the textual characteristics
of biological annotation, and suggests that this understanding can be used to increase
the overall quality of these resources