Überprüfung von Integritätsbedingungen in Deduktiven Datenbanken
This addresses the problem of maintaining consistency in large, complex knowledge bases for database and AI systems, representing an incremental improvement over existing logical programming approaches.
This work tackles the problem of detecting integrity constraint violations in deductive databases by interpreting them as operations on SLDNF proofs, introducing proof trees as a more convenient data structure that enables set-oriented analysis and clearer proof representation. The approach determines minimal conditions for when database changes affect constraint validity and allows reuse of old proofs, reducing computational effort compared to previous methods.
Advancements in computer science and AI lead to the development of larger, more complex knowledge bases. These are susceptible to contradictions, particularly when multiple experts are involved. To ensure integrity during changes, procedures are needed. This work addresses the problem from a logical programming perspective. Integrity violations can be interpreted as special operations on proofs of integrity constraints, with SLDNF proofs being the focus. We define a proof tree as a special data structure and demonstrate the implication of the existence of an SLDNF proof through such a tree. Proof trees are more convenient than SLDNF trees and allow set-oriented considerations of proofs. They also present the proof structure more clearly, enabling further applications. Using this structure, we determine a minimal set of conditions that specify when a change in the knowledge base affects the validity of an integrity constraint. Additionally, this approach allows for the reuse of large parts of the old proof when searching for a new one, which reduces the effort compared to previous approaches.