Combining the language model and inference network approaches to retrieval

摘要

The inference network retrieval model, as implemented in the InQuery search engine, allows for richly structured queries. However, it incorporates a form of ad hoc tf.idf estimates for word probabilities. Language modeling offers more formal estimation techniques. In this paper we combine the language modeling and inference network approaches into a single framework. The resulting model allows structured queries to be evaluated using language modeling estimates. We explore the issues involved, such as combining beliefs and smoothing of proximity nodes. Experimental results are presented comparing the query likelihood model, the InQuery system, and our new model. The results reaffirm that high quality structured queries outperform unstructured queries and show that our system consistently achieves higher average precision than InQuery.