Un interessante progetto di rielaborazione delle informazioni ottenute dalle citazioni bibliografiche, basato sull’estrapolazione dei concetti salienti, analizzati dal punto di vista semantico e riorganizzati in un grafico che rappresenti visivamente le relazioni tra le informazioni oggetto della ricerca nella banca dati.

Un estratto della presentazione:
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I would like to introduce an application we’re working on, which is still not up for production, but is — but is under development and we are hoping to bring out at some point. It is called Semantic MEDLINE. And the idea is that this is an information management application that manipulates information in addition to documents. So the particular way that we’ll use this is to demonstrate a way of managing the results of PubMed searches. So, in a way it helps the user decide what to read from the results of a large PubMed search, which as you well know can easily be hundreds, tens of thousands of citations. In addition, this connects knowledge from various sources and integrates application interfaces.
So a rough overview of how it might work, it sits totally on top of PubMed/MEDLINE. So the idea ultimately will be to have all the resources available, but in addition, after retrieval you will summarize with natural language processing applications that produce a visual graphic network of relationships. And then you can — and this is specifically the aspect that we call Semantic MEDLINE. Once you have done that you can then choose some particular relationship that you are interested in, and this maintains links to the documents that produced that information in addition to other information that I’ll put into greater detail in just a second.

So this is seamless integration of NLM’s technologies, including information retrieval (the familiar PubMed/MEDLINE) and natural language processing, which is under development, a program called SemRep that I have been a responsible for for a number of years, which is now beginning to come into fruition, to be applicable in a practical way. And this represents the content of text with semantic predications. The term semantic predications is loved by linguists (which I am one), and I’ll explain it in greater detail and specificness in just a minute. So on top of that there is a process called abstraction summarization, which takes the information that is extracted from text and boils it down to sort of the most salient…what the system considers the most important information, which you can then choose and navigate around in. And finally this information is visualized with indicative links to source text and additional information.
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