Improved document ranking in ontology‐based document search engine using evidential reasoning
Wenhu Tang, Long Yan, Zhen Yang, Qinghua Wu · IET Software · 2013
This study presents a novel approach to document ranking in an ontology‐based document search engine (ODSE) using evidential reasoning (ER). Firstly, a domain ontology model, used for query expansion, and a connection interface to an ODSE are developed. A multiple attribute decision making (MADM) tree model is proposed to organise expanded query terms. Then, an ER algorithm, based on the Dempster–Shafer theory, is used for evidence combination in the MADM tree model. The proposed approach is discussed in a generic frame for document ranking, which is evaluated using document queries in the domain of electrical substation fault diagnosis. The results show that the proposed approach provides a suitable solution to document ranking and the precision at the same recall levels for ODSE searches have been improved significantly with ER embedded, in comparison with a traditional keyword‐matching search engine, an ODSE without ER and a non‐randomness‐based weighting model.