Improving Query Suggestion for Digital Libraries

The users of a digital library often have difficulty in formulating query expression that could represent his or her information requirements exactly. Query suggestion can provide some recommended query expression and help the user build a proper expression. Query suggestion for digital libraries requires higher precision ratio and novelty ratio than that of web search engines. Based on case studies, we found that there are four main types of query suggestion within digital library environments, namely spelling suggestion, hot keyword suggestion, personalized suggestion and semantic suggestion. These approaches are, however, hardly to ensure high precision ratio and novelty ratio expected by digital library users to date. The paper proposed an improved query suggestion approach for digital library and its main advantages lie in computing semantic relations, finding hot concepts and ranking candidate concepts. Semantic similarity between user´s input and a candidate keyword is calculated by Relative Information Loss (RIL). Hot keywords are indentified by a new algorithm, which involving clicked time, novelty clicked time, result record number, novelty result record. The rank degree of a keyword is evaluated by its RIL and hot degree. Finally, a software component that takes advantage of DBpedia Ontology, Jena, Ajax and SPARQL in align with these new improvements is developed and deployed on a digital library named iDLib. Better user experience with our new query suggestion software component proves the feasibility and efficiency of the improvement.