Grammar-like functional rules for representing query optimization alternatives
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Iterative dynamic programming: a new class of query optimization algorithms
ACM Transactions on Database Systems (TODS)
Making Biomedical Ontologies and Ontology Repositories Work
IEEE Intelligent Systems
Concept-based querying in mediator systems
The VLDB Journal — The International Journal on Very Large Data Bases
Proceedings of the 17th international conference on World Wide Web
A semantic web middleware for virtual data integration on the web
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Querying distributed RDF data sources with SPARQL
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
A flexible rule-based method for interlinking, integrating, and enriching user data
ICWE'10 Proceedings of the 10th international conference on Web engineering
Federating queries in SPARQL 1.1: Syntax, semantics and evaluation
Web Semantics: Science, Services and Agents on the World Wide Web
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In this dissertation novel methods developed in the Semantic Web community throughout the past couple of years are used to implement a large-scale data integration system for distributed, heterogeneous data with rich semantics. While the proposed system, called SemWIQ (Semantic Web Integrator and Query Engine) is primarily designed for sharing data for scientific collaboration, it is regarded as a base technology useful for many other Semantic Web applications which use ontologies to describe highly-structured data. At its core, SemWIQ is based on the mediator-wrapper architecture and a specialized SPARQL query processor which is able to virtually integrate data using declarative queries (Note: SPARQL is a standardized declarative query language for the Semantic Web). Combined with latest research towards new graphical user interfaces for the Web of Data, the proposed approach is very well suited for large-scale collaborative knowledge sharing in research as well as in the industry.