Multilingual document mining and navigation using self-organizing maps
One major approach for information finding in the WWW is to navigate through some Web directories and browse them until the goal pages were found. However, such directories are generally constructed manually and may have disadvantages of narrow coverage and inconsistency. Besides, most of existing directories provide only monolingual hierarchies that organized Web pages in terms that a user may not be familiar with. In this work, we will propose an approach that could automatically arrange multilingual Web pages into a multilingual Web directory to break the language barriers in Web navigation. In this approach, a self-organizing map is constructed to train each set of monolingual Web pages and obtain two feature maps, which reveal the relationships among Web pages and thematic keywords, respectively, for such language. We then apply a hierarchy generation process on these maps to obtain the monolingual hierarchy for these Web pages. A hierarchy alignment method is then applied on these monolingual hierarchies to discover the associations between nodes in different hierarchies. Finally, a multilingual Web directory is constructed according to such associations. We applied the proposed approach on a set of Web pages and obtained interesting result that demonstrates the feasibility of our method in multilingual Web navigation.
self-organizing map hierarchy alignment multilingual web page navigation multilingual text mining