EpiphaNet: An Interactive Tool to Support Biomedical Discoveries

Background. EpiphaNet (http://epiphanet.uth.tmc.edu) is an interactive knowledge discovery system, which enables researchers to explore visually sets of relations extracted from MEDLINE using a combination of language processing techniques. In this paper, we discuss the theoretical and methodological foundations of the system, and evaluate the utility of the models that underlie it for literature‐based discovery. In addition, we present a summary of results drawn from a qualitative analysis of over six hours of interaction with the system by basic medical scientists. Results: The system is able to simulate open and closed discovery, and is shown to generate associations that are both surprising and interesting within the area of expertise of the researchers concerned. Conclusions: EpiphaNet provides an interactive visual representation of associations between concepts, which is derived from distributional statistics drawn from across the spectrum of biomedical citations in MEDLINE. This tool is available online, providing biomedical scientists with the opportunity to identify and explore associations of interest to them

Weather Information Priorities for Commercial Pilots and Dispatchers

The Next Generation Air Traffic System’s (NextGen) goal is to increase capacity three-fold (JPDO, 2007). Given that approximately 70 percent of system delays can be attributed to weather, planning is focused on reducing weather-related delays by at least fifty percent (Leader, 2007). NextGen plans to integrate information from multiple sources, providing the same information to pilots, controllers, and dispatchers. However, different stakeholders may require different information at different times. This research identifies information needed by dispatchers and commercial pilots for pre-flight and in-flight planning and decision-making

Logical Leaps and Quantum Connectives: Forging Paths through Predication Space

The Predication-based Semantic Indexing (PSI) approach encodes both symbolic and distributional information into a semantic space using a permutation-based variant of Random Indexing. In this paper, we develop and evaluate a computational model of abductive reasoning based on PSI. Using distributional information, we identify pairs of concepts that are likely to be predicated about a common third concept, or middle term. As this occurs without the explicit identification of the middle term concerned, we refer to this process as a “logical leap”. Subsequently, we use further operations in the PSI space to retrieve this middle term and identify the predicate types involved. On evaluation using a set of 1000 randomly selected cue concepts, the model is shown to retrieve with accuracy concepts that can be connected to a cue concept by a middle term, as well as the middle term concerned, using nearestneighbor search in the PSI space. The utility of quantum logical operators as a means to identify alternative paths through this space is also explored