Keyword Weight Propagation for Indexing Structured Web Content

When documents are atomically structured, it is possible to assign them keyword vectors to support indexing. Most web content, however, have non-atomic structures. These include navigational/semantic hierarchies on the web. Although they are especially effective for browsing, such structures make it hard for individual nodes to be properly indexed. This is because, in many cases, their contents have to be inferred from the contents of their neighbors, ancestors, and descendants in the structure. In this paper, we propose a novel keyword and keyword weight propagation technique to properly enrich the data nodes in structured content. In particular, our approach first relies on understanding the context provided by the relative content relationships between entries in the structure. We then leverage this information for relative-content preserving keyword propagation. Experiments show that we observe a significant improvement (10−15%) in precision with the proposed keyword propagation algorithm.