Combining automatic and manual index representations in probabilistic retrieval

Results from research in information retrieval have suggested that significant improvements in retrieval effectiveness can be obtained by combining results from multiple index representations, query formulations, and search strategies. The inference net model of retrieval, which was designed from this point of view, treats information retrieval as an evidential reasoning process where multiple sources of evidence about document and query content are combined to estimate relevance probabilities. In this article, we use a system based on this model to study the retrieval effectiveness benefits of combining the types of document and query information that are found in typical commercial databases and information services. The results indicate that substantial real benefits are possible.