Self-organizing Maps in Web Mining and Semantic Web

The nature inspired approaches represent a new trend in computer science in general and in the Semantic Web, due to their scalability and robustness. Neural networks represent one category of nature inspired solutions. The self-organizing map (SOM) is a very popular unsupervised neural network model (Kohonen, et al., 2000). It is a data mining and visualization method for complex high dimensional data sets. In the first part of the chapter, we present how the SOM model can be applied in Web mining, by giving sets of documents as input data space for SOM. The result of applying SOM on a set of documents is a map of documents, which is organized in a meaningful manner so that documents with similar content appear at nearby locations on the twodimensional map display. From the information retrieval point of view, our implemented SOM-based system creates document maps that are readily organized for browsing. A document map also clusters the data, resulting in an approximate model of the data distribution in the high dimensional document space. Some experimental results are included, where a couple of meaningful clusters have been discovered by our system in a subset of the “20 newsgroups” data set (Lang, K., 1995). The clustering capability of our system allows users to find out quickly what is new in a Web site of interest by comparing the clusters obtained from the site at different moments in time. In the rest of the chapter, we focus on how a more complex SOM based unsupervised neural network model is used for enriching a domain ontology. Building complete and reliable domain ontologies is the basis for the success of the Semantic Web. The ontology enrichment process consists in the addition of new concepts which will be attached as hyponyms for the existent nodes of the ontology (Pekar and Staab, 2002). The names of the new concepts are terms represented linguistically by common noun phrases. The enrichment process can also add new instances to existent concepts of the ontology. In this case, the process is also known in the literature as ontology population or named entity classification, where the named entities are represented linguistically by proper names of people, organizations, locations etc. (Cimiano and Völker, 2005). In both cases, the process is algorithmically the same, the only difference being the grammatical category of the linguistic entities to be classified: common noun phrases representing terms for new concepts to be added or proper noun phrases representing named entities, i.e. new instances for the existent 22