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rswk_00%3a%22Google %2f suchmaschine %2f ranking %28BVB%29%22 2
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rswk_00%3a%22Google %2f suchmascheine %2f ranking %28BVB%29%22 2
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Lee, J.H.; Kim, M.H.: Ranking documents in thesaurus-based Boolean retrieval systems (1994) 0.03
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Abstract

Investigates document ranking methods in thesaurus-based Boolean retrieval systems and proposes a new thesaurus-based ranking algorithm, the Extended Relevance (E-Relevance) algorithm. The E-Relevance algorithm integrates the extended Boolean model and the thesaurus-based relevance algorithm. Since the E-Relevance algorithm has all the desirable properties of previous thesauri-based ranking algorithms. It also ranks documents effectively by uisng terms dependence information from the thesaurus. Through performance comparison shows that the proposed algorithm achieved higher retrieval effectiveness than the others proposed earlier
Lee, J.H.; Kim, M.H.; Lee, Y.J.: Information retrieval based on conceptual distance in is-a hierarchies (1993) 0.01
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Abstract

There have been several document ranking methods to calculate the conceptual distance or closeness between a Boolean query and a document. Though they provide good retrieval effectiveness in many cases, they do not support effective weighting schemes for queries and documents and also have several problems resulting from inappropriate evaluation of Boolean operators. We propose a new method called Knowledge-Based Extended Boolean Model (KB-EBM) in which Salton's extended Boolean model is incorporated. KB-EBM evaluates weighted queries and documents effectively, and avoids the problems of the previous methods. KB-EBM provides high quality document rankings by using term dependence information from is-a hierarchies. The performance experiments show that the proposed method closely simulates human behaviour