INFORMATION RETRIEVAL BASED ON CONCEPTUAL DISTANCE IN IS‐A HIERARCHIES

TLDR

Existing document ranking methods compute conceptual distance between Boolean queries and documents, but they lack effective weighting schemes and suffer from issues with Boolean operator evaluation. The authors propose a Knowledge‑Based Extended Boolean Model (kb‑ebm) that incorporates Salton’s extended Boolean framework. kb‑ebm extends Salton’s model by integrating term‑dependence information from is‑a hierarchies to weight queries and documents. Experiments show kb‑ebm effectively evaluates weighted queries and documents, avoids prior problems, and yields high‑quality rankings that closely simulate human behavior.

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.