Monitoring the status of cultural heritage for its preservation is a complex activity that can be performed by human experts with the support of technology. This complexity is due to the need of considering several related aspects: parts of buildings, materials, damages, environmental conditions, risk factors, etc. Querying this complex knowledge base could benefit from a very expressive ontology and an efficient technology support for reducing response times especially when implicit knowledge needs to be inferred in Web applications. We adopt the Cidoc4HeriwarD ontology as a benchmark to analyse two different technologies for storing and querying semantic data: an open source one based on relational data bases and a commercial solution with a native triple store.

1. Querying a Complex Web-Based KB
for Cultural Heritage Preservation
Ester Giallonardo, Ciro Sorrentino, Eugenio Zimeo
Department of Engineering
University of Sannio
Benevento, Italy
ICKEA 2017 October 22, 2017

2. Outline
● HeriwarD: a Smart Cultural Heritage Management platform
● The OWL Cidoc4HeriwarD ontology
● The semantic repositories: Marmotta 3.3.0 and Stardog 4.2.4.
● The design of Cidoc4Heriward benchmark
● Benchmark results
● Conclusions
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3. HeriwarD objective:
continuous cultural
heritage monitoring
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4. HeriwarD: some use cases
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www.heriward.com

5. HeriwarD Ontology for actionable knowledge
The Cidoc4HeriwarD ontology
20 years ago: CIDOC-CRM first release.
From 2006 it is an international standard
(ISO 21127) for managing CH diversity.
Features: flexible, expressive and articulate
● extends CIDOC-CRM with SKOS
Thesauri, Concepts and Properties.
Semantic repositories
● can extend the available knowledge base
with additional relationships inferred from
new facts and rules.
● we need keep low the response time that
can increase significantly query processing
time, making it the application not
responsive and scarcely usable.
E22 Man-Made_Object
P2 has type
E55 Type: Triumphal Arch
ICKEA 2017

6. Marmotta 3.3.0 Stardog 4.2.4
License Apache 2 Commercial and Community (functional and non funcional limitations)
Language / Connectors Java Java
Triple store Hybrid Native
Query Language SPARQL 1.1 SPARQL 1.1
Security Role Based Access Control. Role Based Access Control
API REST-API compliant with the
SPARQL 1.1 Graph Store API
protocol. WS Api
Java, Clojure, .NET, Spring, Groovy, REST-API ompliant with the SPARQL 1.1 Graph Store
API protocol.
Reasoning model Forward chaining Backward chaining.
Reasoning Rule based (similar datalog) OWL 2, SWRL/RIF.
Geospatial queries GeoSparql (not definitively) GeoSparq l(in the commercial version through the JTS library[])
Number of Databases 1 Only 10 databases in the community
Marmotta vs Stardog

7. Approaches to a benchmark
- LUBM: OWL Lite constructs, retrieving only instances.
- BSBM and SP2BENCH: retrieving concepts and property too.
- MUM: Linked Data, OWL 2 QL.
- DBPSB: DBPedia, cache clearing, warmup execution, dataset
size scaling.
- SIB: Social Network, insert-delete-update times.
- Metrics: time, space, query features and error conditions.
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8. Cidoc4Heriward Benchmark
- No reasoning time considered before.
- Application driven benchmark with a data generator tool.
- A complex ontology (Cidoc4Heriward)
- Realistic datasets: D1 and D2(updated model).
- Equivalence, Transitive and Symmetric OWL Properties.
- SKOS Thesauri.
- Insert and Delete times.
- Huge inferred graphs.
-
D1 dataset (554,442 triples)
D2 dataset (4,368,097 triples)
ICKEA 2017

9. Datasets
D1: contains 5000 complex
cultural heritage objects created
according the Heriward insert
schemas based on the
Cidoc4HeriwarD ontology
D2: exploits the inferred
knowledge and makes transitive
or symmetric some defined
Cidoc4HeriwarD properties.
554,442triples
56 CIDOC-CRM concepts
116 Cidoc4HeriwarD concepts
88 CIDOC-CRM properties
45 Cidoc4HeriwarD properties
6 SKOS properties.
4,368,097triples
87,3%inferred knowledge
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10. Cidoc4HeriwarD Benchmark Design
ICKEA 2017 October 22, 2017
INSERT DATA{
$temp$:&MaterialModificationType& c4t:is_generally_degradation_of
$mat$:&Material& ;
c4t:is_degradation_started_by $int$:&Intervention& ;
c4t:is_degradation_stopped_by $int$:&Intervention& .}
● Cidoc4HeriwarD SPARQL-based SCHEMA INSERT
● SPARQL DELETE
● SPARQL SELECT query mix

11. Cidoc4HeriwarD Benchmark Query: example
Q18: for a specified agent, all the interventions required by all the
interventions need to stop a specific agent.
SELECT distinct ?t where {
?intervention c4h:stops_agent
agent:Overpaint_Agent.
?intervention
c4h:requires_intervention ?t.
}
owl:TransitiveProperty
In the SKOS Agents ThesaurusICKEA 2017
($p owl:inverseOf $q), ($1 $p $2) -> ($2 $q $1)
($p rdf:type owl:SymmetricProperty), ($1 $p $2) -> ($2 $p $1)
($p rdf:type owl:TransitiveProperty),($1 $p $2),($2 $p $3)
-> ($1 $p $3)
($r rdf:type $1), ($1 owl:equivalentClass $2)
-> ($r rdf:type $2)
($1 $p $2), ($p rdfs:subPropertyOf $q) -> ($1 $q $2)
($1 owl:equivalentClass $2) -> ($2 owl:equivalentClass $1)
($r rdf:type $1),($1 rdfs:subClassOf $2),($2
owl:equivalentClass $3) -> ($r rdf:type $3)
($p owl:inverseOf $q) -> ($q owl:inverseOf $p)
Marmotta Reasoning Rules
agent1
int1
int2
int0

12. Results: Avg Time Query Mix
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13. Results: Difference between 1st access and followings
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[ms]
[ms]

14. StrengthsConclusions
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Linear response time
Flexibility
Integration (OSGI)
Open Source
API Rest
Consistency
Reliability
Geosparql enabled
Power reasoning
API Rest
Federated SPARQL queries
Monotonic Read
Consistency
No Federated
SPARQL
Response time
strongly related to
complexity and
expressivity
Weekneeses

15. Thanks. Any questions?
Ester Giallonardo
{ ester.giallonardo, csorrentino, zimeo } @unisannio.it
Ciro Sorrentino Eugenio Zimeo
ICKEA 2017