Presentation on 25.6.13

1. An approach to improve semantics in
Smart Spaces using reactive fuzzy rules
Natalia Díaz Rodríguez & Johan Lilius
Turku Centre for Computer Science (TUCS),
Åbo Akademi University, Turku, Finland
M.P. Cuéllar & Miguel Delgado Calvo-Flores
University of Granada, Spain
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IFSAWorld Congress - NAFIPS 2013, Edmonton, Canada 25.6.13

2. Introduction
§ Smart Spaces (UbiComp):
interoperability, working
on behalf of the user,
handle unanticipated
situations
§ Human behaviour
modelling: crucial task in
AmI environments
§ Problem: Sensor data as
crisp events.
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3. Introduction
§ Context-awareness for
machine processing and
reasoning.
§ Semantic Technologies and
Ontologies:
Independent knowledge
sharing minimizing
redundancy.
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4. 4
Related Work: fuzzy reasoners

5. Related Work: architectures
§ Different semantic storage solutions, some fuzzy
extensions of SPARQL (e.g. F-SPARQL).
§ What is missing? An integrated single solution
supporting:
– W3C standards (RDF, OWL, SPARQL).
– RDF stores with support for efficient and real-time
pub/sub event notifications.
– Uncertain, imprecise expressions.
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6. Our proposal: A hybrid framework for context-
aware Smart Space application development
2 main components: Crisp KB and Fuzzy KB, connected by a rule
engine which handles each type of event subscriptions.
a. CRISP element: SPARQL Subscription-based RDF store with
Pub/Sub architecture: Smart-M3
b. FUZZY element: Expressive Fuzzy DL reasoner, allows
mapping to a fuzzy control system: fuzzyDL
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7. 7
Fuzzy KB and rules in fuzzyDL

8. Fuzzy KB and rules in fuzzyDL
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9. Hybrid architecture proposal with fuzzy
and crisp KBs
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10. Equivalent queries to be executed in both crisp
M3 and fuzzyDL KBs
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Mapping of rule antecedent “IF Natalia hasStatus AwayForWeekend” to SPARQL and
fuzzyDL queries.

11. Mapping of subscription pattern types to fuzzyDL
queries
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12. Conclusions
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§ Hybrid Fuzzy-Crisp architecture whose
– Quantitative (SPARQL, event Pub/Sub) and Qualitative
(semantics + fuzzy reasoning expressive power) views allow a
versatile framework for developing Smart Space applications.
– strategy allows loosening of semantics or efficiency (depending
on application needs): avoiding continuous querying for changes
or fuzzy discretization-based solutions.
– Knowledge representation & queries are + flexible
§ fuzzyDL still does not support some required semantic constraints
§ 2 KBs = Redundancy (twofold updates) -> advantage for
optimizing execution time of different queries/ datasets

13. Future Directions
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§ Rule serializers (for crisp and fuzzy architectures)
§ Scalability + performance experiments in real-life applications
(reasoning, querying/updating and subscription responseVS ontology size &
fuzzy/crisp/hybrid rules)
§ Optimization: Semantic generalization of hybrid antecedents
1. When strict semantics are to be preserved (we sacrifice
performance):
– IF (WeatherSituationTurku, isCurrently, VeryStormy) –>
IF (WeatherSituationTurku, isCurrently, ?).
2. Transforming patterns into crisp-only (sacrifices semantic
precision):
– IF(Natalia, isVeryNearTo, JohansOffice) –> IF(Natalia, isNearTo,
JohansOffice).

14. Future Directions
§ Pub/sub fuzzy reasoner (->Fuzzy SPARQL?)
§ Alternatives to data redundancy keeping
consistency?
§ Overall:
Standardization of fuzzy SPARQL extension for fuzzy
reasoning will achieve more usable, flexible,
personalized and adaptive Smart Spaces
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15. Thank you for your attention!
Natalia Díaz Rodríguez
ndiaz@abo.fi
Embedded Systems Lab. Department of Information Technologies
Åbo Akademi University, Turku, Finland
TUCS (Turku Centre for Computer Science)
Department of Computer Science and Artificial Intelligence
University of Granada, Spain
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