Characterizing and Detecting Integrity Issues in OWL Instance Data

We view OWL instance data evaluation as a process in which instance data is checked for conformance with application requirements. We previously identified some integrity issues raised by applications demanding closed world reasoning. In this paper, we present a formal characterization of those integrity issues using autoepistemic operators, and a practical SPARQL-based issue checking approach that is a sound approximation for detecting integrity issues. Key words: Instance Data, Evaluation, Autoepistemic Description Logics, OWL, SPARQL

• Type: Conference
• Year: 2008
• Where: OWLED
• Authors: Jiao Tao, Li Ding, Jie Bao, Deborah L. McGuinness
• Keywords: Instance Data, Integrity Issues, Natural Language Processing, OWL Instance Data, OWLED 2008