Deep Web Selection Based on Entity Association

Each subject has a vast amount of deep web resources nowadays. It becomes tremendously hard to retrieve the required integrated information from all the related deep web resources for a subject, which drives out the technology on how to select the suitable web resources. In the medical field, the associations among entities are very extensive, as it can improve our overall level of health when all the related association information can be integrated and used. To improve the entities and their associations integration efficiency, we provide a data resource selection method based on the entities and their associations. The entity and association abstract matrix used in the method is built on the weighted entity scores and their association information in the entity and association diagram. Also, based on the entity and association query intention, it provides a new way to calculate the data resource association. After a large amount of tests and experiments on the data sets in the medical field, the new method is shown to provide a better precision and recall, and it can greatly support the study of information integration in the medical field.