Summary
Objectives:
To summarize current outstanding research in the field of knowledge representation
and management.
Method:
Synopsis of the articles selected for the IMIA Yearbook 2010.
Results:
Four interesting papers, dealing with structured knowledge, have been selected for
the section knowledge representation and management. Combining the newest techniques
in computational linguistics and natural language processing with the latest methods
in statistical data analysis, machine learning and text mining has proved to be efficient
for turning unstructured textual information into meaningful knowledge. Three of the
four selected papers for the section knowledge representation and management corroborate
this approach and depict various experiments conducted to. extract meaningful knowledge
from unstructured free texts such as extracting cancer disease characteristics from
pathology reports, or extracting protein-protein interactions from biomedical papers,
as well as extracting knowledge for the support of hypothesis generation in molecular
biology from the Medline literature. Finally, the last paper addresses the level of
formally representing and structuring informa- tion within clinical terminologies
in order to render such information easily available and shareable among the health
informatics com- munity.
Conclusions:
Delivering common powerful tools able to automati- cally extract meaningful information
from the huge amount of elec- tronically unstructured free texts is an essential step
towards promot- ing sharing and reusability across applications, domains, and institutions
thus contributing to building capacities worldwide.
Keywords
Structured knowledge - natural language processing (NLP) - text mining - knowledge
extraction - knowledge representation
