WEB INFORMATION ACQUISITION BY PERSONAL SEARCH ENGINE BASED ON SVM
Rujing Wang, Deji Wang · International Journal of Information Acquisition · 2005
The quantity of web information is growing exponentially with time, and the challenge of acquiring information efficiently by personal search engine is increasingly complex. Personal preference is not easily described but can be observed from the examples given. Supervised Clustering with Support Vector Machines have been introduced to learn personal preference, however, it is not specialized and cannot be applied to the domain knowledge. In this paper, we introduce the ontology and semantic similarity into SVM as similar measurement. Experiments on agricultural information acquisition validate that it is highly effective.