Leveraging Structural Knowledge for Hierarchically-Informed Keyword Weight Propagation in the Web

This paper proposes a novel keyword weight propagation technique to properly enrich the data nodes in web hierarchies by leveraging the context provided by neighbor entries in a given structure and shows that this approach significantly improves the precision.

Abstract

Although web navigation hierarchies, such as Yahoo.com and Open Directory Project, enable effective browsing, their individual nodes cannot be indexed for search independently. This is because contents of the individual nodes in a hierarchy are related to the contents of their neighbors, ancestors, and descendants in the structure. In this paper, we show that significant improvements in precision can be obtained by leveraging knowledge about the structure of hierarchical web content. In particular, we propose a novel keyword weight propagation technique to properly enrich the data nodes in web hierarchies. Our approach relies on leveraging the context provided by neighbor entries in a given structure. We leverage this information for developing relative-content preserving keyword propagation schemes. We compare the results obtained through proposed hierarchically-informed keyword weight (pre-) propagation schemes to existing state-of-the-art score and keyword propagation techniques and show that our approach significantly improves the precision.