Full-text Support for Publish/Subscribe Ontology Systems
This work addresses scalability and efficiency challenges for users in semantic web and data streaming domains, though it appears incremental as it builds on existing SPARQL and publish/subscribe frameworks.
The authors tackled the problem of efficiently filtering streaming ontology data against millions of user subscriptions in a publish/subscribe system by proposing a SPARQL extension for full-text support and a main-memory indexing algorithm, achieving low complexity and minimal filtering time.
We envision a publish/subscribe ontology system that is able to index millions of user subscriptions and filter them against ontology data that arrive in a streaming fashion. In this work, we propose a SPARQL extension appropriate for a publish/subscribe setting; our extension builds on the natural semantic graph matching of the language and supports the creation of full-text subscriptions. Subsequently, we propose a main-memory subscription indexing algorithm which performs both semantic and full-text matching at low complexity and minimal filtering time. Thus, when ontology data are published matching subscriptions are identified and notifications are forwarded to users.