An Indexing Network: Model and Applications

Internet data are heterogeneous, redundant, disordered, and exponentially growing. Finding the right information from them becomes an ever-challenging issue. Existing technologies such as inverted index and keyword matching can list user webpage matching with given search keywords. They cannot recognize potential relations among webpages to meet some rising user needs, e.g., exploratory search and personalized search. We propose an indexing network model that organizes information in webpages at three levels: words, webpages, and categories, thereby leading to a semantic association graph. Words are used as the description of webpages and categories. Webpage classification is used to gather similar webpages together. Hyperlinks imply the wisdom of the webpage creator, which can help us generate semantic relations among categories. With a clear organizational structure, an indexing network can provide support for many important applications including intelligent information retrieval, recommendation and decision support. In order to provide access to interfaces for the proposed indexing network, an indexing network algebra is defined. Finally, to validate the proposed model, an indexing network is generated based on 30 million webpages and its structure is analyzed. We also give methods to achieve "browsing navigation" and "personalized search" based on the generated network. Results reveal that the use of an indexing network can greatly facilitate exploratory information retrieval and personalized search.