RuleML2015: FOWLA, a federated architecture for ontologies

TarcisioMENDESDEFARIAS–t.mendesdefarias@active3D.net–Ph.D.Candidate
ResearchGroupChecksem–LaboratoryLE2I(UMRCNRS6306)–UniversityofBurgundy
FOWLA, A Federated Architecture for Ontologies
Tarcisio Mendes de Farias, Ana Roxin and Christophe Nicolle
t.mendesdefarias@active3D.net
The 9th International Web Rule Symposium
August 2-5, 2015
Freie Universität Berlin, Berlin, Germany

CONTEXT
o Data to process and share has exponentially
increased since the advent of the internet
o The web of data is pointed as a solution to publish
structured data on the Web
o Various ontologies and relevant vocabularies keep
emerging nowadays
2
Linking Open Data cloud diagram 2014, by Max Schmachtenberg, Christian Bizer, Anja
Jentzsch and Richard Cyganiak. http://lod-cloud.net/

PROBLEM
o Data integration in the context of enterprise
information systems and Semantic Web
o 3 layers of data interoperability
– Physical (e.g. network protocols )
– Syntactic (e.g. XML)
– Semantic (e.g. RDF, OWL)
o Needs of mechanisms for semantic interoperability
3

PROBLEM
o Semantic heterogeneity
– Schema vs Data
heterogeneity
o Full data integration is
only possible considering
both
– Schema
– Data
4
Source: cloudtweaks.com

GOALS AND PROPOSED SOLUTIONS
o Mitigating semantic heterogeneity
– Solution: interoperability at the schema (data model) level
o Tackling semantic data interoperability
– Solution:
• A loosely coupled federated architecture for OWL ontologies
• A rule-based integration of several autonomous ontologies
5

BACKGROUND
o Ontology Matching
– Tackling complex alignments (user involvement)
6
onto2:C21(?x1) ∧ onto2:C22(?x6) ∧ onto2:C23(?x3) ∧ … ∧ onto2:p28(?x7, ?x8) ∧ onto2:p26(?x5,
?x7) ∧ onto2:p27(?x6, ‘‘Category”) ∧ onto2:p28(?x3,‘‘ProductResource”)
→ onto1:p11(?x1, ?x8)
Source: www.webology.org/2006/v3n3/a28.html
o Ontology Alignment
– Alignment format (e.g. SWRL)

BACKGROUND
o Target and source ontologies
7
“A@ruleml.org”^^xsd:string
onto:email
rdf:type
Target Source

RELATED WORK
o Interoperability for different database schemas
– Non-federated (e.g. centralized database )
– Federated database architecture
8
[1] Heimbigner, D., and McLeod, D.. A Federated Architecture for Information Management. ACM Trans. Off. Znf. Syst. 3, 3 253-278 (1985).
“Collection of components to unite loosely coupled federation in order to
share and exchange information” using “an organization model based on
equal, autonomous databases, with sharing controlled by explicit interfaces.”
[1]

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).

FOWLA
o Federated architecture for OWL ontologies
“We define FOWLA as an architecture based on autonomous
ontologies with sharing described through a rule-based
format controlled by inference mechanisms.”
10

FOWLA – General architecture
11
Autonomous
ontologies
Ontology
alignments
(rule-based)
Inference
mechanisms

FOWLA – FD Component
o Separating alignments from the ontology definition
o Federal Logical Schema (FLS)
‒ Ensemble of logical DL-safe rules
‒ OWL + SWRL
‒ Impossible to create new concept instances
o Federal Concept Instantiation (FCI)
– Creating instances for encapsulated data
12

o Interoperability over two OWL ontologies
13
Onto1 TBox
Onto1 ABox
Onto2 TBox
Onto2 ABox

14
swrl1: onto1:Car (?x) → onto2:Motor_Car(?x)
swrl2: onto2:Motor_Car(?x) → onto1:Car(?x)
swrl3: onto1:Car(?x) ∧ onto1:hasColour( ?x, ?y) ∧ onto1:Colour(?y)
∧ onto1:hasName(?y, ?z) → onto2:hasBodyColour(?x, ?z)
Onto1 TBox
Onto1 and
Onto2 ABox
Onto2 TBox
FLS

15
swrl3: onto1:Car(?x) ∧ onto1:hasColour( ?x, ?y) ∧ onto1:Colour(?y) ∧ onto1:hasName(?y, ?z) → onto2:hasBodyColour(?x, ?z)
swrl4: onto2:Motor_Car(?x) ∧ onto2:hasBodyColour(?x,?z) ∧ onto1:Colour(?y) ∧
onto1:hasColour( ?x, ?y) → onto1:hasName(?y,?z)
FLS
Onto1 TBox
Onto1 and
Onto2 ABox
Onto2 TBox
FCI

FOWLA – FC Component
o Performs the bulk of necessary inferences
o Contains the following sub-modules:
– Rule Selector (RS)
– Rule Engine associated to a DL reasoner
o Controls the interoperation among the considered
ontologies based on an ensemble of rules and DL
formalisms (e.g. OWL)
16

FOWLA – FC Component
o RS is responsible for improving backward-chaining
reasoning
– The number of rules highly impacts query execution time
– Integrates access policies
o Why backward-chaining (or hybrid) reasoner ?
– Avoiding considerable amounts of materialized data
– Modification → re-computation of all inferred data
17

FOWLA - Implementation
18

FOWLA – Pre-processing Phase
o Alignments converted to a rule format (e.g. SWRL)
o Query Module
– Identifies each alignment presenting schema
heterogeneity
– Missing properties are materialized along with new
instances for each one
19
swrl4: onto2:Motor_Car(?x) ∧ onto2:hasBodyColour(?x,?z) ∧
onto1:Colour(?y) ∧ onto1:hasColour( ?x, ?y) → onto1:hasName(?y,?z)

FOWLA - Query Execution Phase
20
o Selection of specific rules necessary to answer a
given query addressed over the federated ontologies
swrl3: onto1:Car(?x) ∧ onto1:hasColour( ?x, ?y) ∧ onto1:Colour(?y) ∧
onto1:hasName(?y, ?z) → onto2:hasBodyColour(?x, ?z)
swrl4: onto2:Motor_Car(?x) ∧ onto2:hasBodyColour(?x,?z) ∧ onto1:Colour(?y) ∧
onto1:hasColour( ?x, ?y) → onto1:hasName(?y,?z)
SELECT ?x ?y WHERE{ ?x rdf:type onto2:Motor_Car. ?x onto2:hasBodyColour ?y }
FLSARS

FOWLA BENEFITS
o Avoiding data redundancy
o Inferring new ontology alignments
o Modularizing the maintainability
o Querying with vocabulary terms issued from
different ontologies
o Improving query execution time
21

FOWLA BENEFITS
o Inferring new ontology alignments
22

FOWLA BENEFITS
o Modularizing the maintainability
– Modification in IS(A,D) – { IS(A,B) ∩ IS(A,D) }
23

EVALUATION
24
o We consider two aligned ontologies
– FLS composed of 474 SWRL rules
o Triple store: Stardog
– OWL reasoner associated to a SWRL engine
– It is based on backward-chaining reasoning
OWL entities Onto1 Onto2
Classes 30 802
Object properties 32 1292
Data properties 125 247
Inverse properties 7 115
Triples in the Tbox 2212 9978
DL expressivity ALCHIF(D) ALUIF(D)

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)

EVALUATION
Query name SPARQL Query
Q1 SELECT ?x ?y WHERE { ?x onto1:p11 ?y . }
Q2 SELECT ?x ?y WHERE { ?x a onto2:C21 . ?x onto1:p11 ?y . }
Q3
SELECT ?x ?u WHERE { ?x a onto1:C11 . ?y a onto2:C22 .
?x onto1:p12 ?y . ?y onto1:p11 ?x . }
26
Query KB
Mean execution
time (s)
Standard
deviation ()
#RuleSet #Results
Q1
KB1 - - 474 0
KB2 - - 266 0
KB3 9.25 12.21 178 1683
KB4 2.23 1.78 16 38318
Q2
KB1 - - 474 0
KB2 - - 266 0
KB3 32.99 0.75 178 74
KB4 0.16 0.04 2 74
Q3
KB1 - - 474 0
KB2 - - 266 0
KB3 71.62 0.95 178 0
KB4 0.88 0.43 5 9

CONCLUSION
o An approach for federating ontologies in order to
address the problem of semantic interoperability
o Advantages:
– Allows composing queries using terms from different
ontologies (be it source or target)
– Takes advantage of existing inference mechanisms for
deducing new knowledge
– Reduces execution time for queries addressed over rule-
based alignments
27

FUTURE WORKS
o Defining the strategies for ordering ontologies to be
aligned
o Integration of SWRL built-ins (e.g. swrlb) at the level
of the FLS
o Investigating the use of query languages other than
SPARQL for implementing our approach
28

RuleML2015: FOWLA, a federated architecture for ontologies

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