Foundational challenges in automated Semantic Web data and ontology cleaning

The challenges involved in applying automated reasoning systems (ARS) to Semantic Web data cleaning and to cleaning-agent design are discussed. The main challenges involves in the cleaning of data and ontology, also known as knowledge database (KDB), include logical complexity, poor ontologies, ontology robustness, semantic heterogeneity, lack of a model theory for the Semantic Web and inconsistent information. A reasonable Ontology Web Language (ROWL) is proposed, which is based on a certified generic framework and simplifies the design of a certified ad hoc reasoner to provide ATP features. ROWL will use general-purpose cleaning agents, which can adopt both the ATP synthesized from ROWL ontology's information and the agent's behavior scheme.