DOE program managers are routinely called upon to identify peer reviewer candidates for grant and field work proposals. Each proposal requires a minimum of three reviewers and often more to cover separate aspects of the proposal. To generate reviewer candidates, program managers draw upon their subject matter expertise and manually scour journal literature. Although this process is facilitated by the availability of electronic journals, it is labor intensive and represents a major cost.
In non-technical language, the OSTI Reviewer Finder works as follows. First a semantic technique is used to find a core set of papers that are directly related to the proposal in question. This can also be done for groups of proposals or other topic specific needs.
Second, a sophisticated semantic algorithm, developed by OSTI, is used to find all those papers that are closely related to the core papers. In fact these papers are ranked according to the degree of closeness. This means the pool of related papers can be made larger or smaller as needed.
This two step process may be repeated when a proposal involves several distinct topics, methods or other aspects, which often happens. For example, in addition to subject matter experts one might want to have reviewers who are experts in the methods used.
All of the authors of the core and closely related papers are potential reviewers, so they are abstracted and listed. However, some authors must be excluded, for various reasons. For example, it is common practice...Read more...
OSTI's current services accelerate science through what is largely a kind of card file. We point people to particular pieces of literature or data that meet certain search criteria. From there, people can build on what those pieces of information tell them and achieve new discoveries and inventions.
Some of what the users achieve involves combining the information they get with other knowledge of their own that isn't represented in databases. This of course requires some thought from the users. But other achievements could result entirely from information that the users retrieve through OSTI, with no additional input whatsoever--namely inferences made directly from that information alone. Right now, such inferences still generally require user involvement. But software programs designed and tested in the last several years can automate some inferences from text and data tables. In biology and medicine, these programs have already turned up connections in the literature that could accelerate our understanding, and thus treatment, of some poorly-understood diseases. Among the most recent inferential programs is Semantic Medline, which displays conceptual interconnections across multiple search results in a single graph, thus showing the searcher how his query's terms relate to other concepts, some of which he may not already know.
If it were permanently left to unaided human users to make these inferences themselves, very few would ever be made, since no user knows every fact mentioned in the entire science and technology literature. Computers, on the other hand, can check large sets of literature for explicit links between concepts and infer chains of such links to reveal unsuspected relations in the physical world. The text-analysis software currently...Read more...