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Making Sense of Fuzzy Biomedical Knowledge

Talk, was published by Mara Hartsperger and Volker Stümpflen at 2011-01-24

We present scalable technologies capable to interpret fuzzy information (literature) and combine them with structured data in a uniform semantic way.

Millions of scientific publications still bury high-level knowledge essential for the understanding of complex diseases like Diabetes or Alzheimer. Today it is fairly simple to generate, with high-throughput experiments, GB of data per day. But reading 30.000 Alzheimer related publications for the interpretation of the data is impossible for humans. We present scalable technologies capable to interpret fuzzy information (literature) and combine them with structured data in a uniform semantic way.

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As a former information scientist, I am fascinated since 1999 by the capabilities for building Topic Maps-based knowledge systems having the potential to augment human mind. One can model arbitrary knowledge organization systems, deal with semantic heterogeneity, collocate all facts about one subject in one logical place, and with TMQL have semantic retrieval on federated semantic networks. Therefore I expect bright prospects for business concepts building on the exchange of such knowledge snippets via semantic knowledge services.

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Alexander Sigel
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Topic Maps Lab auf der Cebit 2011
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