introduction to odps and first pattern...
TRANSCRIPT
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Pascal HitzlerData Semantics Laboratory (DaSe Lab)
Data Science and Security Cluster (DSSC) Wright State University
http://www.pascal-hitzler.de
Introduction to ODPs and first pattern examples
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See IOS Press boothPascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnathi, Valentina Presutti (eds.), Ontology Engineering with Ontology Design Patterns: Foundations and Applications.Studies on the Semantic Web. IOS Press/AKA Verlag, 2016.
25% off flyer at http://ontologydesignpatterns.org/wiki/Odp:News/17Supplementary material for the chess example at http://dase.cs.wright.edu/content/pattern-driven-linked-data-publishing-primer
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This Tutorial (all parts)
• Pascal Hitzler (60 mins): Introduction and first examples
• Monika Solanki (30 mins): Example “modeling vaccine traceability”
coffee• Pascal Hitzler (60 mins):
Example “GeoLink Modular Ontology”• Agnieszka Lawrynowicz (30 mins):
Example “Reporting Event ODP”lunch• Karl Hammar with all others (3h):
Hands-on, the WebProtege XDP plug-in
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What this is about
• A tutorial about ontology modeling best practices.
• Coming from the “Ontology Design Patterns” community.
• Recommended by us for all types of ontology modeling, including as graph schema for linked data and knowledge graphs.
• We are approaching a point where our experiences can consolidate into crisp recommendations, but we’re not quite there yet. I.e. there’s still work (and research) to be done.
• Join us if you’re interested: Google Group called “Ontology Design Patterns”
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Very first example
Generic AgentRole pattern
Generic NameStub pattern
Joined:
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Very first example
Joined AgentRole and NameStub patterns:
Used as a template for a concrete modeling problem:
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Very first example
Recording the alignment (e.g. in a separate file):
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The Role Patterns
rangedomaininverserangedomaininverseexistentialscoped rangerange cardinalityrange cardinalityexistentialsdisjointness
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The Agent Role Pattern
Axioms: all previous plus the following.
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OWLAx
Ontology Axiomatization Support (OWLAx)• Protégé Plug-In• Md. Kamruzzaman Sarker, Adila A. Krisnadhi, Pascal Hitzler, OWLAx: A Protege Plugin to
Support Ontology Axiomatization through Diagramming. Proceedings Posters and Demos Track at ISWC 2016.
• Insert class diagram using graphical UI• System asks you whether to include corresponding axioms
(taken from a pool of most common axioms for the diagram)• You can of course also manually add further axioms.
http://dase.cs.wright.edu/content/ontology-axiomatization-support
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Trajectory pattern
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Trajectory pattern
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Trajectory pattern
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Spatiotemporal Extent
Yingjie Hu, Krzysztof Janowicz, David Carral, Simon Scheider, Werner Kuhn, Gary Berg-Cross, Pascal Hitzler, Mike Dean, Dave Kolas, A Geo-Ontology Design Pattern for Semantic Trajectories. In: Thora Tenbrink, John G. Stell, Antony Galton, Zena Wood (Eds.): Spatial Information Theory -11th International Conference, COSIT 2013, Scarborough, UK, September 2-6, 2013. Proceedings. Lecture Notes in Computer Science Vol. 8116, Springer, 2013, pp. 438-456.
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Axioms
Those inherited from the trajectory pattern, plus
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Spatiotemporal Extent
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Spatiotemp. Extent
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Spatiotemporal Extent
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Spatiotemporal event
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Spatiotemporal event
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Quantities and Units
Borrowed from the QUDT ontology
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Provenance
Borrowed from PROV-O
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Thanks!
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References
Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnathi, Valentina Presutti (eds.), Ontology Engineering with Ontology Design Patterns: Foundations and Applications. Studies on the Semantic Web. IOS Press/AKA Verlag, 2016/2017.
Hitzler, Krötzsch, Rudolph, Foundations of Semantic Web Technologies, CRC/Chapman & Hall, 2010
Adila Krisnadhi, Ontology Pattern-Based Data Integration. Dissertation, Department of Computer Science and Engineering, Wright State University, 2015.
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References
Adila A. Krisnadhi, Pascal Hitzler, Krzysztof Janowicz, On capabilities and limitations of OWL regarding typecasting and ontology design pattern views. In: V. Tamma, M. Dragoni, R. Goncalves, A. Lawrynowicz (eds.), Ontology Engineering. 12th International Experiences and Directions Workshop on OWL, OWLED 2015, co-located with ISWC 2015, Bethlehem, PA, USA, October 9-10, 2015, Revised Selected Papers. Lecture Notes in Computer Science 9557, Springer, Heidelberg, 2016, pp. 105-116.
Pascal Hitzler, Markus Krötzsch, Bijan Parsia, Peter F. Patel-Schneider, Sebastian Rudolph, OWL 2 Web Ontology Language: Primer (Second Edition). W3C Recommendation, 11 December 2012.
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References
Yingjie Hu, Krzysztof Janowicz, David Carral, Simon Scheider, Werner Kuhn, Gary Berg-Cross, Pascal Hitzler, Mike Dean, Dave Kolas, A Geo-Ontology Design Pattern for Semantic Trajectories. In: Thora Tenbrink, John G. Stell, Antony Galton, Zena Wood (Eds.): Spatial Information Theory - 11th International Conference, COSIT 2013, Scarborough, UK, September 2-6, 2013. Proceedings. Lecture Notes in Computer Science Vol. 8116, Springer, 2013, pp. 438-456.
Adila A. Krisnadhi, Pascal Hitzler, A Core Pattern for Events. In: Proceedings WOP 2016 at ISWC 2016.
Adila A. Krisnadhi, Pascal Hitzler, The Stub Metapattern. In: Proceedings WOP 2016 at ISWC 2016.
Adila A. Krisnadhi, Pascal Hitzler, Krzysztof Janowicz, A Spatiotemporal Extent Pattern based on Semantic Trajectories. In: Proceedins WOP 2016 at ISWC 2016.
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ReferencesMd Kamruzzaman Sarker, Adila A. Krisnadhi, David Carral, Pascal Hitzler, Rule-based OWL Modeling with ROWLTab Protege Plugin. In: Proceedings ESWC 2017. To appear.
Md. Kamruzzaman Sarker, David Carral, Adila A. Krisnadhi, Pascal Hitzler, Modeling OWL with Rules: The ROWL Protege Plugin In: Takahiro Kawamura, Heiko Paulheim (eds.), Proceedings of the ISWC 2016 Posters & Demonstrations Track co-located with 15th International Semantic Web Conference (ISWC 2016), Kobe, Japan, October 19, 2016. CEUR Workshop Proceedings 1690, CEUR-WS.org 2016.
Md. Kamruzzaman Sarker, Adila A. Krisnadhi, Pascal Hitzler, OWLAx: A Protege Plugin to Support Ontology Axiomatization through Diagramming, In: Takahiro Kawamura, Heiko Paulheim (eds.), Proceedings of the ISWC 2016 Posters & Demonstrations Track co-located with 15th International Semantic Web Conference (ISWC 2016), Kobe, Japan, October 19, 2016. CEUR Workshop Proceedings 1690, CEUR-WS.org 2016.
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Pascal HitzlerData Semantics Laboratory (DaSe Lab)
Data Science and Security Cluster (DSSC) Wright State University
http://www.pascal-hitzler.de
Some patterns and their use in the chess ontology
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Worked Example: Chess
• Establish a searchable repository for chess data.
• Starting point are PGN files.• Should be extendable with other information from
– Chess websites– Wikipedia– Geographic data– News– Etc.
• Use an ontology for information integration.
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GeoVoCamps modeling approach
• Collaborative modeling, group ideally has– More than one domain experts.– People familiar with the base data.– People understanding possible target use cases.– An ontology engineer familiar with the modeling approach.– Somebody who understands formal semantics of OWL.
• Domain experts are queried as to the main notions for the application domain. – E.g. for chess, these would include
• Chess game; move; opening; tournament; players; commentary
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GeoVoCamps modeling approach
• From available data and from application use cases, devisecompetency questions, i.e. questions which should be convertible into queries, which in turn should be answerable using the data.
1. Who played against Kasparov in the round 1994 Lineares tournament? Did (s)he play as a white or black player?
2. What is the first move taken by the black player in the Sicilian Defense opening?3. Find all games in which Bobby Fischer, playing black, lost in the poisoned pawn
variation of the Sicilian Defence opening.4. Are there any recorded games using the Grünfeld Defence from before the 20th
century?5. What did Kasparov say about his opponent’s first two moves in his commentary
about his game against Topalov in the 1999 Tournament in Wijk ann Zee?6. Who was the first non-Russian world champion after Fischer?7. Did Bobby Fischer ever play against a grandmaster in Germany?8. List all world championship games won by forfeit.
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GeoVoCamps modeling approach
• Then prioritize which notions to model first. In the chess case, e.g.
chess gamemove/half-moveplayersopeningtournamentscommentary
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GeoVoCamps modeling approach
• Understand the nature of the things you are modeling.
Chess game ... An EventHalf-move … A Subevent of a chess gamePlayer … The Role of an AgentOpening … this is probably complextournaments … Eventscommentary … this is again more complex
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Player as AgentRole
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ChessGame as Event
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Half-moves
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Opening, game result, etc.
We call these “stubs”.
I.e. we’re aware that more fine-grained modeling will be needed for some use cases.
But currently there’s no reason to do it (not in use case, no data), so we only provide “hooks” for future development of the ontology.
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Putting things together
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Adding commentaries
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Adequacy check
• Triplify sample data using the ontology.Does it work?
• Check if competency questions can be answered.
• Add axioms as appropriate (the graph is only for intuition, the OWL axioms are the actual ontology).
• (there are more post-hoc details to be taken care of, but let’s leave it at that)
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Axioms
Axioms in this case are mostly straightforward:
• Inherited from Event or AgentRole• Scoped domain/range restrictions, possibly with some
cardinalities• Basic existentials• Non-cyclicity of half-move sequence
What about adding, e.g., the following?
If one of the roles of axiomatization is to improve human understanding of the ontology, then such axioms are helpful!
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Shortcuts and Views
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 16
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 17
Shortcuts
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 18
Translating the rules
However note that the introduction of additional role chains may cause violations of regularity restrictions.
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 19
ROWLTab
Modeling OWL with Rules (ROWLTab)• Protégé Plug-In• Md. Kamruzzaman Sarker, David Carral, Adila A. Krisnadhi, Pascal Hitzler, Modeling OWL with Rules:
The ROWL Protege Plugin. Proceedings Posters and Demos Track at ISWC 2016.• Md Kamruzzaman Sarker, Adila A. Krisnadhi, David Carral, Pascal Hitzler, Rule-based OWL Modeling
with ROWLTab Protege Plugin. In: Proceedings ESWC 2017.
• Enter rules using interface very similar to SWRLTab.• But rules are converted into OWL axioms (whenever possible)
instead of DL-safe rules.
E.g., becomesand thus carries the correct semantics. http://dase.cs.wright.edu/content/modeling-owl-rules
We evaluated that ROWL leads to quicker modeling with fewer errors.
http://dase.cs.wright.edu/content/rowlAnd see full paper here at ESWC2017
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 20
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 21
Simplified View
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 22
Mapping from Views
We used rules (axioms) toexpress the mapping from theontology to the view.
The reverse direction ismuch more tricky.
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 23
Mapping from views
Existential rules may be suitable in principle.
However automated reasoning with the potentially rather complex rule heads requires investigations, in particular if it is to be integrated with ontology reasoning.
A specific case are right-hand-side role chains:
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 24
Thanks!
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 25
References
Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnathi, Valentina Presutti (eds.), Ontology Engineering with Ontology Design Patterns: Foundations and Applications. Studies on the Semantic Web. IOS Press/AKA Verlag, 2016/2017.
Hitzler, Krötzsch, Rudolph, Foundations of Semantic Web Technologies, CRC/Chapman & Hall, 2010
Adila Krisnadhi, Ontology Pattern-Based Data Integration. Dissertation, Department of Computer Science and Engineering, Wright State University, 2015.
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 26
References
Víctor Rodríguez-Doncel, Adila A. Krisnadhi, Pascal Hitzler, Michelle Cheatham, Nazifa Karima, Reihaneh Amini, Pattern-Based Linked Data Publication: The Linked Chess Dataset Case. In: Olaf Hartig, Juan Sequeda, Aidan Hogan (eds.), Proceedings of the 6th International Workshop on Consuming Linked Data co-located with 14th International Semantic Web Conference (ISWC 2105), Bethlehem, Pennsylvania, US, October 12th, 2015. CEUR Workshop Proceedings 1426, CEUR-WS.org, 2015.
Adila A. Krisnadhi, Pascal Hitzler, A Core Pattern for Events. In: Proceedings WOP 2016 at ISWC 2016.
Adila A. Krisnadhi, Pascal Hitzler, The Stub Metapattern. In: Proceedings WOP 2016 at ISWC 2016.
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 27
References
Adila Krisnadhi, Pascal Hitzler, Modeling With Ontology Design Patterns: Chess Games As a Worked Example. In: Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnathi, Valentina Presutti (eds.), Ontology Engineering with Ontology Design Patterns: Foundations and Applications. Studies on the Semantic Web. IOS Press/AKA Verlag, 2016/2017.
Adila Krisnadhi, Nazifa Karima, Pascal Hitzler, Reihaneh Amini, Victor Rodriguez-Doncel, Krzysztof Janowicz, Ontology Design Patterns for Linked Data Publishing. In: Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnathi, Valentina Presutti(eds.), Ontology Engineering with Ontology Design Patterns: Foundations and Applications. Studies on the Semantic Web. IOS Press/AKA Verlag, 2016/2017.
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28th May 2017, Portoroz
OntoPedigree: Design patterns forevent-based traceability in
provenance-aware supply chains
Monika Solankihttps://w3id.org/people/msolanki
@nimonikaUniversity of Oxford
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28th May 2017, Portoroz
Motivation
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Visibility* in supply chainsVisibility is the ability to know exactly wherethings are at any point in time or where theyhave been and why.
*http://www.gs1.org/docs/GS1_SupplyChainVisibility_WhitePaper.pdf
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Enabiling Visibility
Data/Knowledge SharingInformation and knowledge need to be interlinked, sharedand made available consistently along the supply chainnot least for regulatory reasons but also due to increasingconsumer demands of being able to track and tracecommodities.
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Pharmaceutical supply chains
Flow of goods and flow of information (Abstraction)
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Visibility in Pharmaceutical supply chains
Crucially Important!Counterfeiting has increasingly become one of the majorproblems prevalent in these chains. The WHO estimatesthat between five and eight percent of the worldwide tradein pharmaceuticals is counterfeit.
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Pharmaceutical supply chainsGS1 standards* for Visibility
GS1: a neutral, not-for-profit organization dedicated to thedesign and implementation of global standards andsolutions to improve the efficiency and visibility in supplychains.Core GS1 standards: EPCIS 1.1 & CBV 1.1GS1 US Secure Supply Chain Task Force: preliminaryimplementation guidelines* for applying GS1 Standards toU.S. Pharmaceutical supply chains for track and trace.
*http://www.gs1.org/healthcare/standards*www.gs1us.org/RxGuideline
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
EPC, EPCIS and CBV
The Electronic Product Code (EPC)*: provides productswith unique, serialised identities.Electronic Product Code Information Services (EPCISv1.1)*: provides a set of specifications for the syntacticcapture and informal semantic interpretation of EPC basedproduct information.CBV* supplements EPCIS by defining the structure ofvocabularies and specific values for the vocabularyelements.Events as abstractions for traceability.
*http://en.wikipedia.org/wiki/Electronic_Product_Code*http://www.gs1.org/epcis
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
SW & LD for Visibility in Supply chains
Research and ApplicationOntological representation of EPCIS eventsOBDA approach towards the transformation of EPCISRDBMSAutomated generation of provenance-basedtraceability artifacts from streaming EPCIS events.Identifying and classifying exceptions in eventsValidation of externally acquired traceability artifacts.
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
EPCIS(1.1) Events: An informal Intuition
One generic and four specific physical event types
For this talk,EPCISEvent: the generic EPCIS event.ObjectEvent: an event that occurred as a result of someaction on one or more entities denoted by EPCs.AggregationEvent: an event that happened to one or moreEPC-denoted entities that are physically aggregated.TransactionEvent: an event in which one or more entitiesdenoted by EPCs become associated or disassociatedwith one or more identified business transactions.
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Data model components
What(product(s)), Where(location), When(time), andWhy(business step and disposition) of events (productmovement) occurring in any supply chain.
EPCsTimeRead PointsBusiness LocationBusiness steps (commissioning, packing, shipping...)Disposition (in_transit, retail_sold, returned...)Action (ADD, OBSERVE, DELETE)
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
EEM*: The EPCIS Event Model
Focuses on a tight conformance with the EPCIS 1.1standard and Simplicity.Explicitly defines relationships with CBV entities throughCBVVocab*.EEM has been mapped* to PROV-O*.
*http://purl.org/eem#*www.w3.org/ns/prov-o*http://purl.org/cbv#
*http://fispace.aston.ac.uk/ontologies/eem_prov.html
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
EEM Modules
Temporaltime of occurrencetime of recordtimezone offset
BusinessActionDispositionBusiness stepTransaction
ProductEPCEPCClassAggregation IdentifiersInput/Output Quantity List
SpatialReadPoint LocationBusiness LocationSourceDestination
EPCIS EventObject EventAggregationEventTransactionEventTransformationEventQuantityEvent** Deprecated in EPCIS 1.1
Structure of EEM
Sensors
EPC Readers
GoodRelations,CollectionsDUL
Semantic Sensor Network Ontology
wgs84, vCard
DUL
CBVVocabPROV-O
EPCISExceptionHierarchy of EPCISException
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
The OBDA approach
M. Solanki et al. Modelling and Linking transformations in EPCIS governingsupply chain business processes. EC-Web 2014.
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Pedigrees
Most widely prevalent in the pharmaceutical industry.Pedigree (e-pedigree) is an audit trail that records the pathand ownership of a drug as it moves through the supplychain.Each stakeholder involved in the manufacture ordistribution of the drug adds information to the pedigree.“Event-based Linked Pedigrees”: pedigrees based on arelevant subset of the captured EPCIS events.
cf. COLD, DeRiVE @ ISWC 2013
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Pharmaceutical supply chains
Flow of linked pedigrees (Abstraction)
M. Solanki et al. EPCIS event-based traceability in pharmaceutical supplychains via automated generation of linked pedigrees. ISWC 2014.
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Requirements
Certification and digital signaturesProduct informationLocation informationConsignment informationTransaction informationPartner pedigree information
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Competency questions
Who is the creator of the pedigree ?What is the supply chain creation status of a givenpedigree?Which are the business transactions recorded against aparticular consignment?What are the events associated with pedigrees createdbetween dates X and Y?Which products have been shipped together?Which other pedigrees are included in the receivedpedigree?
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
OntoPedigree: A CO design pattern
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Linked Pedigree: Axiomatisation
Class: ped:PedigreeSubClassOf:(hasPedigreeStatus exactly 1 ped:PedigreeStatus)and (hasSerialNumber exactly 1 rdfs:Literal)and (pedigreeCreationTime exactly 1 xsd:DateTime)and (prov:wasAttributedTo exactly 1 ped:PedigreeCreator)and (ped:hasConsignmentInfo some eem:SetOfEPCISEvents)and (ped:hasTransactionInfo exactly 1 eem:SetOfEPCISEvents)and (ped:hasProductInfo min 1),(prov:wasGeneratedBy only ped:PedigreeCreationService),(ped:hasReceivedPedigree only eem:Pedigree),prov:Entity
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Automated generation of Linked Pedigrees
Streams of EPCIS events, where each EPCIS event is anamed graphAlgorithm to extract EPCIS events from streams usingINSTANS, an incremental SPARQL query engineCounterfeit EPC detection as a side-effect of generatinglinked pedigrees
M. Solanki et al. EPCIS event-based traceability in pharmaceutical supplychains via automated generation of linked pedigrees. ISWC 2014.
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
EvaluationEPCIS Event volumes
Data Sources: Sample EPCIS relational data, Greyliterature, interviews, surveys, EPCIS expertsAssumption: an average rate of production as 6 days perweek and 10 hours per day,Commissioning events generated based on the number ofitems ranging from 24,000 to 102,000 per day orapproximately 40 to 170 per minute.Aggregation and shipping events generated consideringaggregated items ranging from 100 to 500 (increments of100) per case and number of cases per pallet ranging from20 to 100 (increments of 20).Tumbling window sizes of 3, 5, 7 and 10 hours respectively.
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Evaluation
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Evaluation: Architecture and Implementation
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
EPCIS Exceptions
Typical examples(e1) Pedigree serial number discrepancy(e2) product inference problem - the inability to infer aboutproducts contained in an outer container withoutdisaggregation using pedigree information(e3) quantity inference problem - the inability to derive thetotal quantity of items packed in an outer container withoutdisaggregation using pedigree information(e4) missing or incorrect containment hierarchy betweenitems and their containers - source of counterfeits.(e5) incomplete pedigree data(e6) pedigree data with broken chains, i.e., missingintermediate stakeholder pedigree information.
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Validation requirements
Incomplete pedigree: Mandatory information missingPedigree data has broken chain: Pedigrees from otherpartners are missingPedigree based, receiving and shipping eventcorrelation: EPCs in receiving events do not match theEPCs in the shipping events.Temporal validity of shipping and receiving eventsMissing parent-child aggregation
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Specifying validation rules
http://spinrdf.org/sp.htmlhttp://www.topquadrant.com/spin/tutorial/
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Incomplete pedigree
CONSTRUCT{_:b0 a eem:PedigreeIncompleteException;
spin:violationRoot ?this;eem:eventOccurredAt "timeLiteral"xsd:datetime;eem:associatedBusinessStep cbv:receiving;....other triples about the exceptionrdfs:label ‘‘Incomplete pedigree exception’’.
}
M. Solanki et al. Detecting EPCIS Exceptions in linked traceability streamsacross supply chain business processes. SEMANTiCS 2014.
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Incomplete pedigree
WHERE{...}FILTER NOT EXISTS{ ped:hasPedigreeStatus ?PedigreeStatus;
ped:hasSerialNumber ?serialNumber;ped:pedigreeCreationTime ?pedTime;prov:wasAttributedTo ?pedigreeCreator;ped:hasConsignmentInfo ?setOfConsEvents;ped:hasTransactionInfo SetOfShipEvents;ped:hasProductInfo productInfo.}
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Pedigree data has broken chain
CONSTRUCT{_:b0 a eem:BrokenPedigreeChainException;..same as the CONSTRUCT above..rdfs:label ‘‘Broken pedigree chain exception’’
}WHERE{ ?this a ped:Pedigree;
ped:hasPedigreeStatus ped:IntermediatePedigree;ped:hasReceivedPedigree+ ?pedigree.
FILTER NOT EXISTS {ped:hasPedigreeStatus ped:IntermediatePedigree;ped:hasReceivedPedigree+ ?pedigree.}
}
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Receiving and shipping event correlation
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Implementation
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Summary
Semantic Web standards, ontologies and linked data canbe utilised to record and represent real time supply chainknowledgeComplex Event Processing over continuous streams ofsemantically interlinked EPCIS event datasets enableautomated generation of linked pedigrees, detection ofexceptions and validation of integrity constraints.Rule based frameworks can be integrated with distributedrealtime computation systems such as Apache Storm toprocess real time streams of supply chain data.The proposed approach is domain independent and canbe widely applied to most scenarios of traceability as longas there is conformance to EPCIS 1.1 in the supply chain.
[email protected], @nimonika Event-based traceability in supply chain datasets
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28th May 2017, Portoroz
Further informationM. Solanki and C. Brewster. OntoPedigree: A content ontology design pattern for traceability knowledgerepresentation in supply chains. Semantic Web Journal, 2015
M. Solanki and C. Brewster. Enhancing visibility in EPCIS governing Agri-food Supply Chains via LinkedPedigrees. To appear, International Journal on Semantic Web and Information Systems Volume 10, Issue 3,2015
M. Solanki and C. Brewster. EPCIS event-based traceability in pharmaceutical supply chains via automatedgeneration of linked pedigrees. ISWC 2014. Springer-Verlag.
M. Solanki and C. Brewster. A Knowledge Driven Approach towards the Validation of Externally AcquiredTraceability Datasets in Supply Chain Business Processes. EKAW 2014. Springer-Verlag.
M. Solanki and C. Brewster. Modelling and Linking transformations in EPCIS governing supply chainbusiness processes. EC-Web 2014. Springer-LNBIP.
M. Solanki and C. Brewster. Detecting EPCIS Exceptions in linked traceability streams across supply chainbusiness processes. SEMANTiCS 2014. ACM-ICPS.
M. Solanki and C. Brewster. Consuming Linked data in Supply Chains: Enabling data visibility via LinkedPedigrees. COLD2013 at ISWC, volume Vol-1034. CEUR-WS.org proceedings, 2013.
M. Solanki and C. Brewster. Representing Supply Chain Events on the Web of Data. DeRiVE at ISWC.CEUR-WS.org proceedings, 2013.
http://windermere.aston.ac.uk/~monika/ontologies.html
http://windermere.aston.ac.uk/~monika/publication.html
[email protected], @nimonika Event-based traceability in supply chain datasets
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Pascal HitzlerData Semantics Laboratory (DaSe Lab)
Data Science and Security Cluster (DSSC) Wright State University
http://www.pascal-hitzler.de
Modular Ontology Design and Use Case: The GeoLink Example
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 2
This Tutorial (all parts)
• Pascal Hitzler (60 mins): Introduction and first examples
• Monika Solanki (30 mins): Example “modeling vaccine traceability”
coffee• Pascal Hitzler (60 mins):
Example “GeoLink Modular Ontology”• Agnieszka Lawrynowicz (30 mins):
Example “Reporting Event ODP”lunch• Karl Hammar with all others (3h):
Hands-on, the WebProtege XDP plug-in
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 3
EarthCube GeoLink Scenario
The NSF EarthCube Program: Developing a Community-Driven Data and Knowledge Environment for the Geosciences
“concepts and approaches to create integrated data management infrastructures across the Geosciences.”
“EarthCube aims to create a well-connected and facile environment to share data and knowledge in an open, transparent, and inclusive manner, thus accelerating our ability to understand and predict the Earth system.”
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 4
EarthCube GeoLink Scenario
GeoLink: An EarthCube “Building Block” project (2014-2017)
How to realize data search across many large-scale geoscience data repositories, such that
– The approach is extendable to new repositories.– The scope can extend across all of the Geosciences.– The search capabilities can be made more fine-grained in the
future if desired.
Central idea: Use a modular, extendable ontology for the integration of metadata.
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 5
GeoLink
An interactive demonstration of the integrated GeoLink data is available at
http://demo.geolink.org
At http://www.geolink.org/ there are links to the complete schema, a SPARQL Endpoint, publications, etc.
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 6
The GeoLink Framework
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 7
The GeoLink Modular Ontology
High-level overview of the GeoLink Modular Ontology (GMO). Each box stands for a module, which has been modeled in its own right.
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Identifier pattern
Identifier xsd:anyURI
xsd:string
hasIdentifierScheme
hasIdentifierValue
hasIdentifierScheme
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 9
OWLAx
Ontology Axiomatization Support (OWLAx)• Protégé Plug-In• Md. Kamruzzaman Sarker, Adila A. Krisnadhi, Pascal Hitzler, OWLAx: A Protege Plugin to
Support Ontology Axiomatization through Diagramming. Proceedings Posters and Demos Track at ISWC 2016.
• Insert class diagram using graphical UI• System asks you whether to include corresponding axioms
(taken from a pool of most common axioms for the diagram)• You can of course also manually add further axioms.
http://dase.cs.wright.edu/content/ontology-axiomatization-support
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 10
Axioms – Systematically
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 11
OWLAx Protégé plug-in
In: Proc. ISWC 2016 poster & demoshttp://dase.cs.wright.edu/content/ontology-axiomatization-support
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Information Object pattern
on InformationObject id:Identifier
owl:Thing xsd:stringxsd:anyURI
realizedBy hasPrimaryIdentifier,hasAlternativeIdentifier
describedByalsoKnownAs,
hasCanonicalName,hasDescription
hasWebpage
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Place
Placeio:InformationObject Geometryio:describedBy hasSpatialFootprint
geo:GeometryGeometry owl:equivalentClass
Alignment to external ontologies or vocabularies, rather than direct reuse:
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 14
Good axioms
Specificity matters: Problems with domain/range.
Recommendations often heard (but are problematic):• Indicate domain and range for your properties.• Reuse as many existing vocabularies as you can.
But there are problems with this:Ontology 1: Ontology 2:
domain(foaf:name) = Human domain(foaf:name) = Organization
Logical consequence after merge: Human ≡ Organization
Human xsd:stringfoaf:nameOrganization xsd:stringfoaf:name
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 15
Recommendations
• Make rich axiomatizations• Avoid re-use of external vocabularies
(rather provide an additional file with mappings for those who want to use it)
• Avoid naïve domain and range axioms.
Alternative to naïve domain/range: scoped domain and range.
scoped rangescoped domain
both rules can be expressed in OWL.
A Bp
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Place shortcuts
Placeio:InformationObject Geometry
xsd:string
xsd:anyURI
io:describedBy hasSpatialFootprint
io:alsoKnownAs, io:hasCanonicalName,
io:hasDescription
hasPlaceName,hasPlaceAliasName,hasTextDescription
io:hasWebpage hasWebpage
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Funding Award pattern
FundingAward
CurrencyAwardAmount
Instantar:AgentRole
io:InformationObject
AgencyProgramManagerRole
CoPrincipalInvestigatorRole PrincipalInvestigatorRole
SponsorRole
xsd:decimalhasAwardAmount
hasCurrency
startsOnDate,endsOnDatear:providesAgentRole
io:describedBy
rdfs:subClassOf
rdfs:subClassOf ar:providesAgentRolerdfs:subClassOf
ar:providesAgentRolerdfs:subClassOf
hasCurrencyValue
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Funding Award shortcuts
FundingAward
Currency
AwardAmount
Instantar:AgentRole
io:InformationObject
AgencyProgramManagerRole
CoPrincipalInvestigatorRole
PrincipalInvestigatorRole
SponsorRole
xsd:decimal
ar:Agent
p:Person
id:Identifier
o:Organization
xsd:string
hasAwardAmount
hasCurrency
startsOnDate,endsOnDatear:providesAgentRole
io:describedByrdfs:subClassOf
hasCurrencyValue
hasAwardValueInUSD
ar:performedBy
hasPrincipalInvestigator,hasCoPrincipalInvestigator,hasAgencyProgramManager
rdfs:subClassOf
io:hasPrimaryIdentifier
hasSponsor
id:hasIdentifierValue
hasAwardID
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Program
Program Instantar:AgentRole
io:InformationObjectCoPrincipalInvestigatorRole PrincipalInvestigatorRole
DataManagerRole
startsOnDate,endsOnDatear:providesAgentRole
io:describedByrdfs:subClassOf rdfs:subClassOf
rdfs:subClassOf
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Program shortcuts
Program
Instant
ar:AgentRole
io:InformationObject
CoPrincipalInvestigatorRole
PrincipalInvestigatorRoleDataManagerRole
xsd:string
ar:Agent
p:Person
startsOnDate,endsOnDate
ar:providesAgentRole
io:describedBy
rdfs:subClassOf
rdfs:subClassOfrdfs:subClassOf
io:hasCanonicalName
hasProgramName
ar:performedBy
hasDataManager,hasPrincipalInvestigator,
hasCoPrincipalInvestigator
rdfs:subClassOf
Note: shortcut does not go to data value.
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Organization pattern
Organization
Place
ar:AgentRole
io:InformationObjectAffiliationRole
haSubOrganization
hasSite
ar:providesAgentRole
io:describedByrdfs:subClassOf
ar:Agent
rdfs:subClassOf
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Person
Person PersonalInfoItem
ar:Agent Instant
rdfs:LiteralhasPersonalInfoItem
rdfs:subClassOf startsAtTime,endsAtTime
hasLiteralValue
owl:Thing
hasValue
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Person Name
Person PersonalInfoItem
ar:Agent Instant
rdfs:LiteralhasPersonalInfoItem
rdfs:subClassOf startsAtTime,endsAtTime
hasLiteralValue
owl:Thing
hasValue
p:Person p:PersonalInfoItem
PersonName
p:hasPersonalInfoItem
hasPersonName rdfs:subClassOf
xsd:stringfamilyOrSurname,firstOrGivenName,
fullName
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Platform pattern (stub)
Platform io:InformationObjectio:describedBy
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Property Value pattern
PropertyPropertyKind PropertyUnit
xsd:string rdfs:Literal
owl:Thing
hasPropertyKind hasPropertyUnit
hasPropertyName hasPropertyValue
hasProperty
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Vessel module
Vessel
pf:Platform
io:InformationObject
ar:AgentRole
pv:Property
VesselOwnerRole
VesselBeam
VesselDisplacement
VesselDraft
VesselLength
VesselDraft
VesselDisplacement
VesselBeam
VesselLength
rdfs:subClassOf
io:describedBy
ar:providesAgentRole
pv:hasProperty
rdfs:subClassOf
rdfs:subClassOf
rdfs:subClassOf
rdfs:subClassOf
rdfs:subClassOf
pv:hasPropertyKind
pv:hasPropertyKind
hasPropertyKind
hasPropertyKind
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Vessel module
Vessel
pf:Platform
io:InformationObject
ar:AgentRole
pv:Property
VesselOwnerRole
VesselBeam
VesselDisplacement
VesselDraft
VesselLength
VesselDraft
VesselDisplacement
VesselBeam
VesselLength
rdfs:subClassOf
io:describedBy
ar:providesAgentRole
pv:hasProperty
rdfs:subClassOf
rdfs:subClassOf
rdfs:subClassOf
rdfs:subClassOf
rdfs:subClassOf
pv:hasPropertyKind
pv:hasPropertyKind
hasPropertyKind
hasPropertyKind
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Vessel shortcuts
Vessel
pf:Platform
io:InformationObject
ar:AgentRole
pv:Property
VesselOwnerRole
VesselBeam
VesselDisplacement
VesselDraft
VesselLength
id:Identifier
ar:Agent
xsd:string
"ICES"
rdfs:Literal
rdfs:subClassOf
io:describedBy
ar:providesAgentRole
pv:hasProperty
rdfs:subClassOf
rdfs:subClassOf
io:hasPrimaryIdentifier
hasVesselOwnerar:performedBy
hasICESPlatformCode id:hasIdentifierValue
id:hasIdentifierScheme
hasVesselBeam,hasVesselDisplacement,
hasVesselDraft,hasVesselLength
pv:hasPropertyValue
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Cruise module
Cruise
e:Event
CruiseTrajectory
io:InformationObject
f:FundingAward
p:Program
ar:AgentRole
pl:Place
t:Trajectory
t:Fix
t:StartingFix
t:Position
t:MovingObjectv:Vessel
t:Segment
Port
d:Dataset
do:Document
ScientistRole
SchedulerRole
ChiefScientistRole
CoChiefScientistRole
EducatorRole
ObserverRole
OperatorRole
StudentRole
TechnicianRole
hasRelatedCruise
rdfs:subClassOf
hasTrajectory
io:describedBy
f:fundedBy
p:associatedWithProgram
ar:providesAgentRole
rdfs:subClassOf
t:hasFix
undertakenBy traveledBy
rdfs:subClassOf
t:hasSegment
t:traversedBy
atPort
rdfs:subClassOfpl:hasSpatialFootprint
d:associatedWithDataset
do:associatedWithDocument
rdfs
:sub
Cla
ssO
f
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Cruise module
Cruise
e:Event
CruiseTrajectory
io:InformationObject
f:FundingAward
p:Program
ar:AgentRole
pl:Place
t:Trajectory
t:Fix
t:StartingFix
t:Position
t:MovingObjectv:Vessel
t:Segment
Port
d:Dataset
do:Document
ScientistRole
SchedulerRole
ChiefScientistRole
CoChiefScientistRole
EducatorRole
ObserverRole
OperatorRole
StudentRole
TechnicianRole
hasRelatedCruise
rdfs:subClassOf
hasTrajectory
io:describedBy
f:fundedBy
p:associatedWithProgram
ar:providesAgentRole
rdfs:subClassOf
t:hasFix
undertakenBy traveledBy
rdfs:subClassOf
t:hasSegment
t:traversedBy
atPort
rdfs:subClassOfpl:hasSpatialFootprint
d:associatedWithDataset
do:associatedWithDocument
rdfs
:sub
Cla
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler
Cruise shortcuts
Cruise
e:Event
CruiseTrajectory
io:InformationObject
ar:AgentRole
pl:Place
t:Fix
t:Position
t:MovingObject
v:Vesselt:Segment
Port
t:TemporalEntity
t:StartingFixt:EndingFix
id:Identifier
xsd:string
ar:Agent
hasRelatedCruise
rdfs:subClassOf
hasTrajectory
io:describedBy
ar:providesAgentRolet:hasFix
t:hasPosition
undertakenBy
traveledBy
rdfs:subClassOf
t:hasSegment
t:traversedBy
atPort
hasPortCall,hasStartPortCall,hasEndPortCall,e:occursAtPlace
rdfs:subClassOf pl:hasSpatialFootprint
t:atTimee:occursAtTime
io:hasPrimaryIdentifier
id:hasIdentifierValue
hasCruiseID
ar:performedBy
hasScientist, hasChiefScientist,hasCoChiefScientist, hasScheduler,
hasEducator, hasObserver,hasOperator, hasStudent,
hasTechnician
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 32
What we need
• A critical amount of simple, general-purpose patterns– Well-documented– Not too generic, not too specialized– Interrelated (e.g., different versions with different granularity
of the same notion)• Languages for describing patterns.• Languages for describing modular ontologies based on
patterns.• Tools for working directly with patterns in ontology engineering
(see afternoon session – Karl Hammar’s work)
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 33
Thanks!
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 34
References
Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnathi, Valentina Presutti (eds.), Ontology Engineering with Ontology Design Patterns: Foundations and Applications. Studies on the Semantic Web. IOS Press/AKA Verlag, 2016/2017.
Hitzler, Krötzsch, Rudolph, Foundations of Semantic Web Technologies, CRC/Chapman & Hall, 2010
Adila A. Krisnadhi, Yingjie Hu, Krzysztof Janowicz, Pascal Hitzler, Robert Arko, Suzanne Carbotte, Cynthia Chandler, Michelle Cheatham, Douglas Fils, Tim Finin, Peng Ji, Matthew Jones, Nazifa Karima, Kerstin Lehnert, Audrey Mickle, Tom Narock, Margaret O'Brien, Lisa Raymond, Adam Shepherd, Mark Schildhauer, Peter Wiebe, The GeoLink Framework for Pattern-based Linked Data Integration. In: Proceedings of the Posters and Demos session at the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pensylvania, USA, October 2015.
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 35
References
Adila A. Krisnadhi, Yingjie Hu, Krzysztof Janowicz, Pascal Hitzler, Robert Arko, Suzanne Carbotte, Cynthia Chandler, Michelle Cheatham, Douglas Fils, Tim Finin, Peng Ji, Matthew Jones, NazifaKarima, Audrey Mickle, Tom Narock, Margaret O'Brien, Lisa Raymond, Adam Shepherd, Mark Schildhauer, Peter Wiebe, The GeoLink Modular Oceanography Ontology. In: Marcelo Arenas, Óscar Corcho, Elena Simperl, Markus Strohmaier, Mathieu d'Aquin, Kavitha Srinivas, Paul T. Groth, Michel Dumontier, Jeff Heflin, Krishnaprasad Thirunarayan, Steffen Staab (eds.), The Semantic Web - ISWC 2015 - 14th International Semantic Web Conference, Bethlehem, PA, USA, October 11-15, 2015, Proceedings, Part II. Lecture Notes in Computer Science 9367, Springer, Heidelberg, 2015, 301-309.
Adila Krisnadhi, Ontology Pattern-Based Data Integration. Dissertation, Department of Computer Science and Engineering, Wright State University, 2015.
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May 2017 – ESWC 2017, Portoroz, Slovenia – Pascal Hitzler 36
References
Yingjie Hu, Krzysztof Janowicz, David Carral, Simon Scheider, Werner Kuhn, Gary Berg-Cross, Pascal Hitzler, Mike Dean, Dave Kolas, A Geo-Ontology Design Pattern for Semantic Trajectories. In: Thora Tenbrink, John G. Stell, Antony Galton, Zena Wood (Eds.): Spatial Information Theory - 11th International Conference, COSIT 2013, Scarborough, UK, September 2-6, 2013. Proceedings. Lecture Notes in Computer Science Vol. 8116, Springer, 2013, pp. 438-456.
Eva Blomqvist, Pascal Hitzler, Krzysztof Janowicz, Adila Krisnadhi, Thomas Narock, Monika Solanki, Considerations regarding Ontology Design Patterns. Semantic Web 7 (1), 2016, 1-7.
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The Reporting Event ODP
Agnieszka Lawrynowicz1, Ewa Kowalczuk1
1
Faculty of Computing, Poznan University of Technology, Poznan, Poland
May 28, 2017Tutorial: Modular Ontology Modeling with Ontology Design Patterns at
ESWC2017
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Motivation
� emerging need for storing and investigating not onlyinformation about a particular event, but also the provenanceof the information and circumstances of its provision
� properties attributed to an event are not stored as facts, butas a narrative of a particular agent, which could di↵er fromthe narratives of other agents
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Do existing event ontologies address the subjectivity ofevent properties? 1/2
� CIDOC Conceptual Reference Model (CRM): does not allowto mark the level of property value probability or attribute aproperty assignment to a particular agent
� Linking Open Descriptions of Events ontology (LODE): allowsfor linking events to media objects presenting them and thusdenoting sources
� IPTC NewsML-G2 controlled vocabulary: allows to definewhether an event and its time interval are confirmed or not;IPTC rNews model introduces a propertyrnews:accountablePerson for linking to a person responsiblefor a particular news item (as a whole).
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Do existing event ontologies address the subjectivity ofevent properties? 2/2
� Simple Event Model (SEM): introduces a subclass ofsem:Constraint, called sem:View, allowing to mark someattributed property as a belief (point of view) of a particularsem:Authority. The property assignment is constant: there isno means of representing the fact that a view changed overtime
� BBC Storyline Ontology introduces a notion of nsl:Storyline,to denote the editorial perspective of an event or a group ofevents. It can be attributed to a specific owner. Storylineshave a larger span than a single event. They can includensl:StorylineSlots: real world events or inner storylines.
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Reporting Event ODP: Intent
To allow for modelling situations in which the knowledge about anevent cannot be treated as certain. It is particularly useful forcases in which two or more agents provide di↵erent, contradictoryinformation about the same event. Also for modelling situation inwhich a single agent provided contradictory information about thesame event at di↵erent points in time. The pattern allows forstating di↵erent circumstances of an act of the informationprovision.
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Competency questions
� What characteristics (e.g. date, participants, cause) is anactual event said to have?
� Which agent made a statement about an actual event?
� On which sources these statements were based?
� What were the circumstances of providing information aboutan actual event?
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Reporting Event ODP
http://ontologydesignpatterns.org/wiki/Submissions:ReportingEvent
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Pattern formalization
ActualEvent � EventActualEvent � SocialObject � ObjectActualEventView � EventActualEventView � InformationObject � ObjectActualEventView � = 1 isAbout.ActualEventActualEventView � ∀isAbout.ActualEventActualEventView � = 1 reports−1.ReportingEventActualEventView � ∀hasContext.ActualEventViewContextActualEventViewContext � ∃hasContext−1.ActualEventViewReportingEvent � EventReportingEvent � SituationReportingEvent � ∃reports.ActualEventViewReportingEvent � ∀reports.ActualEventViewReportingEvent � ∃hasAuthor.EventReporterReportingEvent � ∀isBasedOn.SourceEventReporter � AgentEventReporter � ∃hasAuthor−1.ReportingEventSource � (Event �Object)reports � isSettingFor
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Intension Extension ODP 1/2
Intent: To represent the meaning of an information object: theconcepts it expresses, the things it is about.
Competency Questions:
� What is the meaning of an information object?
� What information objects express this meaning?
� What is this about?
� How can I call this?
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Intension Extension ODP 2/2
http://ontologydesignpatterns.org/wiki/Submissions:IntensionExtension
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Situation ODP 1/2
Intent: To represent contexts or situations, and the things that arecontextualized.
Competency Questions:
� What is the context or situation of something?
� What are the things present in this context or situation?
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Situation ODP 2/2
http://ontologydesignpatterns.org/wiki/Submissions:Situation
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Sample use of Reporting Event ODP: Historical Debate
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News Reporting Event ODP: Intent
Can be used for modelling situations in which we are not certainthat a particular actual event has the properties which weredescribed in a news message. We want to define the properties ofan actual event which were reported (time, place, actors,subevents, cause, e↵ect etc.), but not to treat them as universal,verified knowledge. The pattern also allows to define who isresponsible for a particular description of an event and how thisdescription is dealt with.
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Competency questions
� What aspects of an actual event were presented in the newsmessage?
� Who reported an actual event? Which news provider theyrepresented?
� When was a certain actual event reported for the first time?
� What actual events are presented in a certain medium/bymedia of a certain news provider?
� How was an actual event presented?
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News Reporting Event ODP
http://ontologydesignpatterns.org/wiki/Submissions:NewsReportingEvent
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Pattern formalization
NewsReportingEvent � ReportingEventNewsReportingEvent � TimeIndexedSituationNewsReportingEvent � ∃hasAuthor.NewsEventReporterNewsReportingEvent � ∃presentedAt.MediaNewsEventReporter � EventReporterNewsEventReporter � ∃hasAuthor−1.NewsReportingEventNewsProvider � SocialAgentNewsProvider � ∃owns.MediaMedia � = 1 owns−1.NewsProviderNewsPresentationContext � ∃hasPresentationContext−1.NewsReportingEventhasPresentationContext � isSettingFor
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Acting For ODP
Intent: To represent that some agent is acting in order to forwardthe action of a social (non-physical) agent.
Competency Questions:
� Who is working for which organization?
� Who is representing the company?
http://ontologydesignpatterns.org/wiki/Submissions:ActingFor
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Time Indexed Situation ODP 1/2
Intent: To represent time indexed situations.
Competency Questions:
� At what time did a certain situation occur?
� What situations occurred at a certain time?
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Time Indexed Situation ODP 2/2
http://ontologydesignpatterns.org/wiki/Submissions:TimeIndexedSituation
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Sample use of News Reporting Event ODP: Presentationof Social Unrests
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Thank you!
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Introduction to XD and XDP
2017-05-28
Karl Hammar
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eXtreme Design
• ”a family of methods and associated tools, based on the application, exploitation, and definition of Ontology Design Patterns (ODPs) for solving ontology development issues” – Presutti et al.
• Agile, iterative, pair development, testing emphasis• Requirements written as user storys formalised as Competency
Questions, Contextual Statements, Reasoning Requirements• Tight customer integration• Key steps: find ODP, instantiate ODP, integrate solution
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Project initiation and scoping
Identify CODP catalogues
Collect requirement
stories
Select story
Elicit requirements
Select set
Match and select ODPs
Instantiate and integrate ODPs
Test module
Release module
Integrate partial solutions,
evaluate, revise
Release new version
All req:s covered?
All stories covered?
No
NoYes
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XD for WebProtégé (XDP)
Fork of WebProtégé including tooling to support some XD steps:• Find ODPs• Instantiate ODPs (template-based or specialisation-based)• Integrate ODPs into solution (basic alignment)Also includes visualization, courtesy of code from the VisualDataWebproject and new UI tabs for advanced editingSome restrictions of WebProtégé:• No reasoning• ODP namespaces cloned, not imported
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Hands-On Session
• Using XDP and ODPs, construct an ontology covering a set ofrequirements and structuring a set of provided data, in the policingdomain.
• Inspired by a real-world project and real-world data.
• Goal: try out and learn about the method, the tooling, and look at
some ODPs.
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CAVEATS
• ODP Portal, tooling, etc. are mirrored from the Internet, to accountfor the lack of Internet connectivity. Some IRIs you see here do not exist in the real world, or lead to content that is not the same as what you see here.
• Further: ODP quality varies greatly in the real world: stale IRIs, bad documentation, bad illustrations, dependency on remotereferences, etc.
• Thus: four specifically suggested patterns have been tampered within order to simplify modelling. E.g., merged import closure, addedmissing documentation, added some common-sense assumptions.
• Finally: this is beta-quality research software. Expect some bugs.
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Get started
Poll: Who wants some Google Refine/OpenRefine introduction as well?
• WiFi SSID “ODP Tutorial”, password: “eswc2017”• Instructions: http://ontologydesignpatterns.org/instructions.txt• Data: http://ontologydesignpatterns.org/data.zip• XDP Instance: http://wp.xd-protege.com• ODP Portal: http://ontologydesignpatterns.org• WebVOWL Instance: http://vis.xd-protege.com
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