Abstract
Retrieving relevant information is a crucial component of cased-based reasoning systems for Internet applications such as search engines. The task is to use user-defined queries to retrieve useful information according to certain measures. Even though techniques exist for locating exact matches, finding relevant partial matches might be a problem. It may not be also easy to specify query requests precisely and completely - resulting in a situation known as a fuzzy-querying. It is usually not a problem for small domains, but for large repositories such as World Wide Web, a request specification becomes a bottleneck. Thus, a flexible retrieval algorithm is required, allowing for imprecise or fuzzy query specification or search. In this chapter, first we will present the role of the fuzzy logic in the Internet. Then we will present an intelligent model that can mine the Internet to conceptually match and rank homepages based on predefined linguistic formulations and rules defined by experts or based on a set of known homepages. The Fuzzy Conceptual Matching (FCM) model will be used for intelligent information and knowledge retrieval through conceptual matching of both text and images (here defined as “Concept”). The FCM can also be used for constructing fuzzy ontology or terms related to the context of the query and search to resolve the ambiguity. This model can be used to calculate conceptually the degree of match to the object or query. We will also present the integration of our technology into commercial search engines such as Google ™ and Yahoo! as a framework that can be used to integrate our model into any other commercial search engines, or development of the next generation of search engines.
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Nikravesh, M. et al. (2005). Enhancing the Power of Search Engines and Navigations Based on Conceptual Model: Web Intelligence. In: Nikravesh, M., Zadeh, L.A., Kacprzyk, J. (eds) Soft Computing for Information Processing and Analysis. Studies in Fuzziness and Soft Computing, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32365-1_3
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