Multiple Inheritance Problem in Semantic Spreading Activation Networks

Semantic networks inspired by semantic information processing by the brain frequently do not improve the results of text classification. This counterintuitive fact is explained here by the multiple inheritance problem, which corrupts real-world knowledge representation attempts. After a review of early work on the use of semantic networks in text classification, our own heuristic solution to the problem is presented. Significance testing is used to contrast results obtained with pruned and entire semantic networks applied to medical text classification problems. The algorithm has been motivated by the process of spreading neural activation in the brain. The semantic network activation is propagated throughout the network until no more changes to the text representation are detected. Solving the multiple inheritance problem for the purpose of text classification is similar to embedding inhibition in the spreading activation process – a crucial mechanism for a healthy brain.