document: Web object retrieval The primary function of current Web search engines is essentially relevance ranking at the document level . However , myriad structured information about real-world objects is embedded in static Web pages and online Web databases . Document-level information retrieval can unfortunately lead to highly inaccurate relevance ranking in answering object-oriented queries . In this paper , we propose a paradigm shift to enable searching at the object level . In traditional information retrieval models , documents are taken as the retrieval units and the content of a document is considered reliable . However , this reliability assumption is no longer valid in the object retrieval context when multiple copies of information about the same object typically exist . These copies may be inconsistent because of diversity of Web site qualities and the limited performance of current information extraction techniques . If we simply combine the noisy and inaccurate attribute information extracted from different sources , we may not be able to achieve satisfactory retrieval performance . In this paper , we propose several language models for Web object retrieval , namely an unstructured object retrieval model , a structured object retrieval model , and a hybrid model with both structured and unstructured retrieval features . We test these models on a paper search engine and compare their performances . We conclude that the hybrid model is the superior by taking into account the extraction errors at varying levels .
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