LitLinker
 |
Description - LitLinker
The explosive growth in biomedical literature has made it difficult for researchers to keep up with the advancements, even in their own narrow specializations and to explore connections to their own work from other parts of the literature. LitLinker is a text mining system that incorporates knowledge based technologies, natural language processing techniques and data mining algorithms to mine the biomedical literature for new, potential causal links between biomedical terms. Click on the LitLinker icon to the right to try out LitLinker (coming soon).
Text Mining
The design of LitLinker is based on the Swanson's open discovery approach.
LitLinker starts with a provided starting concept, which specifies the
concept that the researchers wants to investigate. Next, LitLinker goes
through a text mining process to find a set of terms (linking concepts) that
are correlated with the starting concept. For each of the linking concepts,
LitLinker uses the same text-mining process to identify a set of terms
(target concepts) that are correlated with the linking concepts. Finally,
LitLinker groups and ranks the target concepts by the number of linking
concepts that connect the target concept to starting concep.
Text Mining Interface
LitLinker returns a complex set of data with connections between medical
concepts that are new to users. Because these connections are new, one
of the most important aspects of the LitLinker interface must be the
ability to examine how the connections were generated. The interface
must help the user understand the text-mining process and allow them to
examine how the terms are connected in the scientific literature. In
order for users to understand how the connections were generated an
important aspect of the interface must be helping users understand the
difference between the three types of terms and how they are each
involved in the text-mining process. While helping the user form a
conceptual model of how the connections were generated we must also keep
the interface simple enough that it will not overwhelm the user. This is
a challenge and a great opportunity to apply information visualization
techniques to the text-mining process.
Description •
Publications •
People •
Contact