Full-Text Support for Publish/Subscribe Ontology Systems
- Cite this paper as:
- Zervakis L., Tryfonopoulos C., Skiadopoulos S., Koubarakis M. (2016) Full-Text Support for Publish/Subscribe Ontology Systems. In: Sack H., Blomqvist E., d'Aquin M., Ghidini C., Ponzetto S., Lange C. (eds) The Semantic Web. Latest Advances and New Domains. ESWC 2016. Lecture Notes in Computer Science, vol 9678. Springer, Cham
In this work, we envision a publish/subscribe ontology system that is able to index large numbers of expressive continuous queries and filter them against RDF data that arrive in a streaming fashion. To this end, we propose a SPARQL extension that supports the creation of full-text continuous queries and propose a family of main-memory query indexing algorithms which perform matching at low complexity and minimal filtering time. We experimentally compare our approach against a state-of-the-art competitor (extended to handle indexing of full-text queries) both on structural and full-text tasks using real-world data. Our approach proves two orders of magnitude faster than the competitor in all types of filtering tasks.