OGIR: an ontology‐based grid information retrieval framework

Abstract

PurposeA grid information retrieval model has benefits for sharing resources and processing mass information, but cannot handle conceptual heterogeneity without integration of semantic information. The purpose of this research is to propose a concept‐based retrieval mechanism to catch the user's query intentions in a grid environment. This research re‐ranks documents over distributed data sources and evaluates performance based on the user judgment and processing time.Design/methodology/approachThis research uses the ontology lookup service to build the concept set in the ontology and captures the user's query intentions as a means of query expansion for searching. The Globus toolkit is used to implement the grid service. The modification of the collection retrieval inference (CORI) algorithm is used for re‐ranking documents over distributed data sources.FindingsThe experiments demonstrate that this proposed approach successfully describes the user's query intentions evaluated by user judgment. For processing time, building a grid information retrieval model is a suitable strategy for the ontology‐based retrieval model.Originality/valueMost current semantic grid models focus on construction of the semantic grid, and do not consider re‐ranking search results from distributed data sources. The significance of evaluation from the user's viewpoint is also ignored. This research proposes a method that captures the user's query intentions and re‐ranks documents in a grid based on the CORI algorithm. This proposed ontology‐based retrieval mechanism calculates the global relevance score of all documents in a grid and displays those documents with higher relevance to users.