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Explaining the Complexity of Life with Topic Maps
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Research Center for Environmental Health, Germany |
Description
Over the last few years it has become clear that the organisation of life is even more complex than previously imagined. For example, if we only count the number of genes in different organisms, we can scarcely explain the differences between a human being and a fruit fly.
In order to understand the complexity of life, biologists can no longer constrain themselves to single DNA sequences, proteins and metabolites. These must instead be seen as building blocks for high-level biological networks that have an almost infinite number of putative interactions. Finding the correct relationships in such networks is one of the keys to understanding, for example, complex diseases caused by a multitude of environmental and genetic factors.
This requires analysing staggering amounts of data. Data sources in the emerging research field of Systems Biology are estimated to have grown to around five petabytes (five quadrillion bytes, or 5,000 terabytes). But apart from the scale, the challenges faced by systems biologists are the same as those faced in other fields of information and knowledge management:
- How to retrieve knowledge that spans multiple resources?
- How to merge knowledge from different domains and ontologies?
- How to harvest semantic structures from unstructured text?
This presentation looks at how a Topic Maps-based approach has been applied to build an semantic information portal that provides seamless, real-time integration of 500 genome databases and 16 million abstracts.
The information comes from the biomedical domain, but the underlying concepts and technical implementation are applicable to Enterprise Information Systems in general, and the research group plans to open source the implementation.
