A multi-modal network architecture for knowledge discovery

Craig M Vineyard,Thomas P Caudell,Shawn E Taylor,Irene Dubicka,Stephen J Verzi,Michael L Bernard

doi:10.1186/2190-8532-1-20

Craig M Vineyard, Thomas P Caudell + Show 4 more

Open Access

https://doi.org/10.1186/2190-8532-1-20

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Abstract

The collection and assessment of national security related information often involves an arduous process of detecting relevant associations between people, events, and locations—typically within very large data sets. The ability to more effectively perceive these connections could greatly aid in the process of knowledge discovery. This same process—pre-consciously collecting and associating multimodal information—naturally occurs in mammalian brains. With this in mind, this effort sought to draw upon the neuroscience community’s understanding of the relevant areas of the brain that associate multi-modal information for long-term storage for the purpose of creating a more effective, and more automated, association mechanism for the analyst community. Using the biology and functionality of the hippocampus as an analogy for inspiration, we have developed an artificial neural network architecture to associate k-tuples (paired associates) of multimodal input records. The architecture is composed of coupled unimodal self-organizing neural modules that learn generalizations of unimodal components of the input record. Cross modal associations, stored as a higher-order tensor, are learned incrementally as these generalizations are formed. Graph algorithms are then applied to the tensor to extract multi-modal association networks formed during learning. Doing so yields a potential novel approach to data mining for intelligence-related knowledge discovery. This paper describes the neurobiology, architecture, and operational characteristics, as well as provides a simple intelligence-based example to illustrate the model’s functionality.

Highlights

Intelligence analysts are hampered by the need to sift through very large amounts of constantly changing data in order to forage for “nuggets” of information that may support or discredit an existing hypothesis
In this paper we present an artificial neural network architecture that learns these types of association inspired by the hippocampus
Conclusions and future work In this paper we first presented an artificial neural network computational architecture with functionality inspired by the neural processes of hippocampus

Summary

Introduction

Intelligence analysts are hampered by the need to sift through very large amounts of constantly changing data in order to forage for “nuggets” of information that may support or discredit an existing hypothesis. The collection and assessment of national security related information often involves an arduous process of “connecting the dots” within very large data sets This process has proven to be extremely difficult, especially when analysts need to piece together information cues associated with various individuals, groups, events, and places, along with such items as communication and transportation logs [1]. Developing a system that assists analysts with knowledge discovery by helping to uncover associations, as well as help marshal evidence by assembling individual pieces of evidence into a single context, would be a great advancement to the analyst community This is true with the increasing need to more rapidly detect associations across various information modes for threat identification and determination in real-time, security-related contexts—for example, in situations involving time critical targets of national importance where rapid assessments must be made as to the type and degree of threat that may or may not exist

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