Enhancing semantic digital library query using a content and service inference model (CSIM)

Although digital library (DL) information is becoming increasingly annotated using metadata, semantic query with respect to the structure of metadata has seldom been addressed. The correlation of the two important aspects of DL--content and services--can generate additional semantic relationships. This study proposes a content and service inference model (CSIM) to derive 15 relationships between content and services, and defines functions to manipulate these relationships. Adding the manipulation functions to query predicates facilitates the description of structural semantics of DL content. Moreover, in search for DL services, inferences concerning CSIM relationships can be made to reuse DL service components. Highly promising with experimental results demonstrates that CSIM outperforms the conventional keyword-based method in both content and service queries. Applying CSIM in DL significantly improves semantic queries and alleviates the administrative load when developing novel DL services such as DL query interface, library resource-planning and virtual union catalog system.