An important field in computer science today is artificial intelligence. The new approaches used by computer scientists in this field seek answers to many of the problems that have so far not been solved through traditional approaches to software engineering. One of the concepts studied and implemented for a variety of tasks in artificial intelligence today is neural networks; have proven effective in offering an approach to some problems in the field, but they also have some flaws. Traditional neural networks, which "learn" by modifying the values, or weights, contained in the nodes of a directed graph, suffer from several problems that make their application to a given problem difficult and cumbersome. They require large amounts and frequent application of training – the material by which their knowledge banks are kept accurate – making them difficult to maintain. Many neural network systems suffer from a kind of information overload where they can lose data after being trained on a large dataset, which may not be desirable, as it could damage the reliability of the results produced. The various shortcomings of traditional neural networks, coupled with their encouraging success in some areas, have prompted research into alternative network models. A new type of neural network proposed by Dr. Anthony Beavers, the dynamic associative network (DAN), offers an answer to some of the problems of traditional neural networks. Instead of changing node weights during training, DANs simply add new interconnected nodes when necessary. They do not need to be continuously trained to maintain their correct behavior, unlike traditional neural networks, and they do not result in data loss. Recent research suggests that… in the middle of the paper… feature detectors will be in a particular partially activated state, called a signature. This signature will be compared to all other unique signatures constructed from the InPhO database and, using the rich partial matching properties of dynamic associative networks, determine which elements of the ontology have the most in common with the document. This set of elements will be further analyzed to identify the appropriate position of the Document within the hierarchy of philosophy. The project as proposed has no significant economic, environmental, health and safety or political implications. One goal is to minimize the influence (and number of hours required) of the philosophy professionals who currently help curate the taxonomy, which could have social consequences, as their dedication of time and effort would no longer be necessary.