Contextualized knowledge repositories for the Semantic Web
Journal of Web Semantics
We propose Contextualized Knowledge Repository (CKR): an adaptation of the well studied theories of context for the Semantic Web. A CKR is composed of a set of OWL 2 knowledge bases, which are embedded in a context by a set of qualifying attributes (time, space, topic, etc.) specifying the boundaries within which the knowledge base is assumed to be true. Contexts of a CKR are organized by a hierarchical coverage relation, which enables an effective representation of knowledge and a flexible
... od for its reuse between the contexts. The paper defines the syntax and the semantics of CKR; shows that concept satisfiability and subsumption are decidable with the complexity upper bound of 2NExpTime, and it also provides a sound and complete natural deduction calculus that serves to characterize the propagation of knowledge between contexts. mation contained in more specific contexts. For example, the facts in FWC should be lifted up into WN and FB. This lifting should be done without spoiling locality of knowledge; overlapping and varying domains: objects can be present in multiple contexts, but not necessarily in all contexts, e.g., a player can exist in both the FWC context and in the NFL contexts, but many players present in NFL will not be present in FWC; inconsistency tolerance: two contexts may possibly contain contradicting facts. For instance NN of Italy could assert that "Cassano is the best player of the world", while at the same time the world news report that "Rooney is the best player of the world", without making the whole system inconsistent; complexity invariance: the qualification of knowledge by context should not increase the complexity. Based on these requirements, we propose a framework called Contextualized Knowledge Repository (CKR), build on top of the expressive description logic SROIQ  that is behind OWL 2 . The CKR framework is tailored for the Semantic Web, but it is rooted in the foundations of contextual knowledge representation laid down by previous research in artificial intelligence. Adopting the context as a box paradigm, a CKR knowledge base is composed of units, called contexts, each qualified by a set of dimensional attributes that specify its contextual boundaries. Contexts are organized by a hierarchical coverage relation that regulates the propagation of knowledge between them. After brief preliminaries (Sect. 2) the paper defines the syntax and semantics of CKR (Sect. 3); then it provides a sound and complete natural deduction calculus that serves to characterize the propagation of knowledge between contexts (Sect. 4); and finally it shows that concept satisfiability and subsumption are decidable with the complexity upper bound of 2NExpTime, i.e., same as for SROIQ (Sect. 5); related work is then discussed and concluding remarks added in Sects. 6, 7. Detailed proofs of all statements are attached in the appendix.