Spreading Activation Methods

Spreading Activation Methods

Alexander Troussov (IBM, Ireland), Eugene Levner (Holon Institute of Technology and Bar-Ilan University, Israel), Cristian Bogdan (KTH – Royal Institute of Technology, Sweden), John Judge (IBM, Ireland) and Dmitri Botvich (Waterford Institute of Technology, Ireland)
DOI: 10.4018/978-1-60566-908-3.ch008


Spreading activation (also known as spread of activation) is a method for searching associative networks, neural networks or semantic networks. The method is based on the idea of quickly spreading an associative relevancy measure over the network. The goal is to give an expanded introduction to the method. The authors will demonstrate and describe in sufficient detail that this method can be applied to very diverse problems and applications. They present the method as a general framework. First they will present this method as a very general class of algorithms on large (or very large) so-called multidimensional networks which will serve a mathematical model. Then they will define so-called micro-applications of the method including local search, relationship/association search, polycentric queries, computing of dynamic local ranking, etc. Finally they will present different applications of the method including ontology-based text processing, unsupervised document clustering, collaborative tagging systems, etc.
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Spreading Activation Methods Framework (Sam Framework)

In this section we’ll introduce spreading activation (a phenomenon observed in the nervous systems of living organisms) as a cognitive psychology phenomenon, provide a description of graph algorithms imitating this phenomenon, and we will trace the roots of activation spread method back to numerical simulations in physics.

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