The progress of information and communication technologies has greatly increased the quantity of data to process. Thus, managing data heterogeneity is a problem nowadays. In the 1980s, the concept of a Federated Database Architecture (FDBA) was introduced as a collection of components to unite loosely coupled federation. Semantic web technologies mitigate the data
heterogeneity problem, however due to the data structure heterogeneity the integration of several ontologies is still a complex task. For tackling this problem, we propose a loosely coupled federated ontology architecture (FOWLA). Our
approach allows the coexistence of various ontologies sharing common data dynamically at query execution through logical rules. We have illustrated the advantages of adopting our approach through several examples and benchmarks.
We also compare our approach with other existing initiatives.
9. TarcisioMENDESDEFARIAS–t.mendesdefarias@active3D.net–Ph.D.Candidate
ResearchGroupChecksem–LaboratoryLE2I(UMRCNRS6306)–UniversityofBurgundy
RELATED WORK
o Correndo et al. [2] and Makris et al. [3]
– SPARQL query rewriting approaches for data interoperability
– Graph pattern rewriting based on ontology alignments
– Semantic interoperability over various ontologies
o Main drawbacks
– Cases of several source and target ontologies are ignored
– Impossible to write queries using terms from different
ontologies
– No inference capabilities
9
[2]Makris et al. Ontology mapping and SPARQL rewriting for querying federated RDF data sources. In Proceedings of the 2010
International Conference on On the Move to Meaningful Internet Systems: Part II, OTM’10, pages 1108–1117, Berlin (2010).
[3] Correndo et al. Sparql query rewriting for implementing data integration over linked data. In Proceedings of the 2010 EDBT/ICDT
Workshops, pages 4:1–4:11, New York, NY, USA. ACM (2010).
25. TarcisioMENDESDEFARIAS–t.mendesdefarias@active3D.net–Ph.D.Candidate
ResearchGroupChecksem–LaboratoryLE2I(UMRCNRS6306)–UniversityofBurgundy
EVALUATION
Number of rules Characteristics
KB1 474 All the rules contained in the FLS (all the rules forming the
alignment between Onto1 and Onto2)
KB2 266 All subsumption rules along with all the rules that have
elements from Onto1 in their head
KB3 178 All rules from KB2 minus some of the rules that have
elements from Onto1 in their head (we aimed at reducing the
data inferred)
KB4 variable All the rules contained in the Activated Rule Set (ARS)
conceived by the RS.
25
o Experiment Environment
– Each repository’s ABox contains 1,146,294 triples
– Server: Intel Xeon CPU E5-2430 at 2.2GHz with 2 cores out of 6,
8GB of DDR3 RAM memory (Java Heap = 6GB)
– Client: Intel Core CPU I7-4790 at 3.6GHz with 4 cores, 8GB of
DDR3 RAM memory at 1600MHz (Java Heap = 1GB)