Parallel Automated Knowledge Acquisition of Case-Based Semantic Networks from Relational Databases

The number of databases that are accessible over networks within organizations is increasing. This paper presents a methodology for automatically converting the data in these databases into a useful knowledge base of case-based semantic networks that can be accessed through a browsing facility. A parallel processing strategy has been implemented for this knowledge acquisition process to support its scalability to large databases. This methodology has potential application in the development of organizational intranets. It can also be used for retrospective browsing of the context of interesting patterns discovered by data mining. The database examples used in this paper are from clinical laboratories that provide data to a hospital infection control committee. Even though the results presented here use a single domain, the methodology can be used with no changes to explore the construction of multidomain knowledge bases.