Using Ontologies to make Smart Cities Smarter

1. Using Ontologies to make Smart Cities Smarter Rosario Uceda-Sosa, Biplav Srivastava and Bob Schloss IBM Research {rosariou@us, rschloss@us, sbiplav@in} @.ibm.com June 2012

2. Application Developer/ Consultant Data Model Semantic Data Model [ETL] A Semantic Data Model for Smart Cities A semantic data model (an ontology) of a city, if it is complete and authoritative, (1) simplifies the development of applications that require integrated access to city data sources and (2) enables solution reuse as we move from one city to the next. Independently of using ETL for data consolidation, a semantic data model (3) can extend the metadata with new categories (SanitationServices, CrimesAgainstProperty) without modifying the application or the data sources. 2. Reuse 3. Metadata Extensions Data sources • An ontology can make a city interconnected and smart, but it needs to assume that • Cities have their own data sources, not necessarily connected, and may not want to consolidate them. • Cities have non-standard organizations, departments and competencies.

3. … but, what is an ontology, anyway? What do you think?

4. … but, what is an ontology, anyway? In Computer Science, “An ontology is a formal explicit description of concepts in a domain of discourse (classes (sometimes called concepts)), properties of each concept describing various features and attributes of the concept (slots (sometimes called roles or properties)), and restrictions on slots (facets (sometimes called role restrictions)). An ontology together with a set of individual instances of classes constitutes a knowledge base. In reality, there is a fine line where the ontology ends and the knowledge base begins.” [Noy, 2000] Not to be confused with ontologies (and/or taxonomies) in Philosophy or Life Sciences In a Smart City domain, we’re concerned with modeling the city data (city activity data, city departments, assets, KPIs), not the city itself (the full set of spatial and temporal relations between people and objects in the city) Ontologies help us to structure and reason about city events, entities and services. Ontology = Class + Relations + Constraints Knowledge Base = Ontology + instances + (Standard) Inference and rules

5. Not all ontologies are created equal In practice, ontologies are used -together with inferencing engines and rules-, for a variety of purposes. If we think of them as schemas, there are different ways Normative schema Integrative Schema, depend on instances SCRIBE belongs to the fourth category: It has no constraints and was designed to support the programming of tools that allow domain experts to deal with entities natural to them (even if the recorded data is actually distributed).

6. What makes a good ontology for data integration? A good ontology is a useful ontology, an ontology that both humans and systems can process.

7. The SCRIBE Model of Cities

8. Scribe design decisions A good ontology is a useful ontology, an ontology that both humans and systems can process.

9. AssetManagement BuildingAndParcel Transportation Water Weather SCRIBE data model SCRIBE is a non-normative, authoritative, modular, extensible semantic model for Smarter Cities. It consists of a Core Model that includes common classes (events and messages, stakeholders, departments, services, city landmarks and resources, KPIs, etc.), extensions by domain and customizations by city. • Simple language • Classes + Inheritance + Relations + Inferencing • Based on standards (OWL-QL, SPARQL) • Mappable to UML • Metadata annotations and Tagging • Simple language • Classes + Inheritance + Relations + Inferencing • Based on standards (OWL-QL, SPARQL) • Mappable to UML • Metadata annotations and Tagging • Simple language • Classes + Inheritance + Relations + Inferencing • Based on standards (OWL-QL, SPARQL) • Mappable to UML • Metadata annotations and Tagging Common building blocks SCRIBE Core Model Extension City Customization Organization/Operation profile • Authoritative • Aligned with standards (CAP, NIEM, MISA/MRM, UCore) • Validate with customer scenarios • Validated with open city data • Authoritative • Aligned with standards (CAP, NIEM, MISA/MRM, UCore) • Validate with customer scenarios • Validated with open city data • Authoritative • Aligned with standards (CAP, NIEM, MISA/MRM, UCore) • Validate with customer scenarios • Validated with open city data Features

10. WorkItem (RoadWorkWI) Event Message Protocol (InfrastructureWorkP) WorkItem (RoadWorkWI) WorkItem (RoadWorkWI) Message Event Protocol (InfrastructureWorkP) WorkItem (RoadWorkWI) WorkItem (RoadWorkWI) Message (Advisory) Event (Storm, RoadWork) Protocol (InfrastructureWorkP) The key concepts of the SCRIBE Ontology 1. Describes messages, events and services as they flow through the system Before/after triggers Before/after triggers Before/after triggers Asset (pipe, valve) 2. Represents types of city services (not the city organization itself) so the administrative structure of a city can be assembled from SCRIBE building blocks CityServiceArea CityServiceArea Owns Agency (WhitePlainsTraffic)

11. City and Government Standards and SCRIBE While most of the standards relevant to Smarter Cities are message exchange models (CAP, UCore, NIEM) or business planning (MISA/MRM) , SCRIBE integrates the (1) message-based models with (2) asset management and (3) services and their KPIs in an extensible model.

12. Smarter City Standards and SCRIBE (1) A message is an event (with publisher/subscribers or requestors/responders) AND it has as a subject an (external/processing) event. In principle, a message could refer to another message. ServiceArea (Public Safety, etc.) Organization (CityOrganization) Role (Person, Organization) Entity (Person, Organization, - item) causes isStakeholder -> hasRole -> Stakeholder Event Is-a (1) ExternalEvent WorkItem subject Message Asset Maximo-Based Overlap, superset, etc. Planner Tom Travis Transportation Dept CAP-Based Overlap, superset, etc. Stakeholder1 RoadRepair WorkOrder Intersection: Main And Hamilton NIEM-Based Overlap, superset, etc.

13. The SCRIBE Metadata

14. Inferencing and object properties • There are three types of ‘horizontal’ relations: • HasAttribute (inv. attributeOf) for properties and attributes (name, identifier, etc.) • HasAggregateMember (inv aggregateMemberOf) for parts or members (hasChild, a process has process steps as members) • AssociatedTo (its own inverse) for everything else •  We can do inferencing on extensions to SCRIBE

15. EndUser Application Developer/ Consultant SCRIBE tooling SCRIBE is written using standard RDF/OWL editors and software (Jena) Model Tooling Edit, extend model Customize Model Integrate with Data Query/Navigate Model and Data Standard OWL/XML (TopBraid, Protégé, Pellet, SPARQL, etc. ) MIDO, DB2RQL, R2DQ, etc. Form-based queries? Record-based navigation? Implementation Content Semantic model of events, city assets, geography and resources, city organization and services, KPIs, processes, SCRIBE Core Model Simple subset of OWL, directly mappable to UML City Customization City Data Catalog SCRIBE is also a. A modeling process B. Tools to make the model usable. The first tool we’ve worked on, MIDO (Mapping Instance Data to Ontologies), allows the mapping of existing data to the SCRIBE model and is part of the process of customizing SCRIBE to a new city. MIDO Database Schema

16. Customizing Scribe in different cities Scribe is NOT closed. We know that cities have different organizations, different service levels and different KPIs. The Scribe model is designed to provide the building blocks (service types, city departments, KPI taxonomies, CAP messages) that can be customized to define the overall operations of a city Standards (CAP, NIEM, MISA/MRM, etc.) Scenarios/Data (cities open data) Scribe CORE MIDO Maps city data to Scribe. Populates model with instance data Chicago Dublin Washington D.C. Services Departments Assets KPIs Services Departments Assets KPIs Services Departments Assets KPIs

17. Scenario

18. 311 events in Washington D.C. • Suppose a Smarter City application that manages city operations wants to display citizen complaints (311 calls) on a map, filtered by a few user-defined constraints (times, locations, type of call, etc.) • A fraction of the 311 incident table (from DC Open Data) is below. Among the data we have: • Identifier • Type of service (code + description) • Time (ServiceOrderDate, ServiceResolution date, etc.) • Place (Lon/Lat, Ward, PSA, District, etc.) • The agency that should handle the request • Various qualifiers (enum types): priority, resolution, etc.) 311 Requests (2010)

19. How to map 311 events to an existing model The application may access directly the 311 table by querying incidents according to given criteria: “SELECT SERVICEREQUESTID SERVICETYPECODE LATITUDE LONGITUDE WARD DISTRICT PSA DATEREPORTED FROM DC911 WHERE SomeConstraintHere” OR The application may define an intermediate (data model) layer that: A Event Defines a ServiceRequest object that knows how to retrieve all the data from one or more tables. Defines two objects, ServiceRequest, where all the common data to all service requests is, and DC311SvceReq, which captures the info specific to DC. B ID ServiceRequest Type C DateOrdered Lon/Lat IS-A … Notice that in (C), inheritance can be applied to locations (wards, districts, addresses, Lon/Lat points are ways to describe a location) Also, we could push the model further and have all kinds of abstractions, say, an event class that captures ID, Time, Location and Type. DC311SvceReq Ward 311 Requests (2010)

20. Mapping 911 (crime) incidents • Now suppose that the application wants to add the visualization of crime incidents. The corresponding open data table is shown below. Notice that it looks similar to DC311… but not quite: • ID’s have different format • Time is ReportDateTime, and has a time of day, not just a date • Offenses do not have codes • There’s no referring agency • From the point of view of the application: • We can create another query for the DC911 table and consolidate the information at the application level (requires recompiling) • We can add types and data to the object model, but this bloats the objects. • We can use the inheritance hierarchy to refactor the information in the model. IF the model is well thought out, the changes are minimal… But we’ll need inferencing, infrastructure to keep the graphs… We’ll be replicating RDF/OWL A B C Crime Incidents (2010)

21. The right data integration point. A semantic model approach • … And there are net benefits to a model-driven, semantic approach: • Applications can be coded ‘in the abstract’. E.g., Display all current events independently of whether they are 311 or 911. • Applications can refine the metadata without having to touch the code or the underlying data. E.g. Display all sanitation requests • Applications can be shielded from the details of the databases, like in the case of implicit joins. E.g. Display the names of the dispatchers associated with active requests. • The SCRIBE model captures enough information about events to allow a small customization to work.

22. Step 1. Customizing SCRIBE for Washington D.C. SCRIBE captures the basics of events, service types, dates, etc. but we don’t expect the model to be comprehensive. For example, we didn’t model all the types of services that the 311 table had. To customize SCRIBE, we created a new file for DC, importing the core model. • We may want to customize SCRIBE for a variety of reasons • SuperCans is a DC-specific program and it will likely remain in the DC specific classes. • CollectingIllegalDumping or SeasonalCollection were not contemplated in the core, and they may be marked for promotion at a later date (using the modelPromotion annotation) • Adding a new data property to a core class, like a DC-specific identifier • Note that constraints and rules in the DC model do not need to be reflected in the mapping to SCRIBE.

23. Step 2. Mapping instance data to the model Next, we map the data in the columns to either a data property (transferring the data into that data property, like in the case of SIMPLEREQUESTID) OR a class (to match an enumerated type, which in the case of SCRIBE is represented as a taxonomy of classes.) ServiceRequest ServiceRequestID associatedTo ServiceType hasDescriptor ServiceTypeDescriptor codeData This mapping is done through a mapping model and tool called MIDO, whose details are not covered here. However, we can assume that the columns in the two tables have been mapped to the SCRIBE model AND the instance data can be accessed through the SCRIBE model.

24. Step 3. Query through the model. Query abstract classes The data from DC Service Requests and Crime Incidents can now be queried together as events, not just as service requests or criminal incidents. Query: All Events in DC, with type, District and Ward … Notice that some of the data is missing in the original table… That’s still ok

25. Step 3. Query through the model. Annotation Metadata As shown previously. The inferencing in the ontology can be leveraged in a query. Query: Public Sanitation Service Requests

26. Step 3. Query through the model. Implicit join Everything in a semantic model is connected. The service request can be linked to the name of the dispatcher of the department. Query: Select events associated to dept of Public Works and his dispatcher

27. Lessons Learned

28. Scribe design decisions A good ontology is a useful ontology, an ontology that both humans and systems can process.

29. For more informationhttp://researcher.ibm.com/view_project.php?id=2505 OR email rosariou@us.ibm.com

30. References A direct map of relational data to RDF, W3C working draft 14 March, 2011, http://www.w3.org/TR/2011/WD-rdb-direct-mapping-20110324/ R2RML: RDB to RDF Mapping Language, W3C Working Draft 24 March 2011, http://www.w3.org/TR/r2rml/ The D2RQ Platform v0.7 - Treating Non-RDF Relational Databases as Virtual RDF Graphs, 2009-08-10, http://www4.wiwiss.fu-berlin.de/bizer/d2rq/spec/ Hannes Bohring and Soren Auer, Mapping XML to Ontologies,citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.59.8897 T. nf Rodrigues, P. Rosa, J. Cardoso, Mapping XML to existing OWL ontologies, citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.59.292 DB2OWL, A tool for automatic Database-To-Ontology mapping, http://citeseerx.ist.psu.edu/viewdoc/ summary?doi=10.1.1.97.5970 Municipal Information Systems Association/Municipal Reference Model (MISA/MRM), http://www.misa.on.ca/en/ National Information Exchange Model, http://www.niem.gov/ D. Gonzales, C. Ohlandt, E. Landree, C. Wong, R. Bitar and J. Hollywood. The Universal Core Information Exchange Framework, Assessing its Implications for Acquisition Programs, RAND report, 2011, http://www.rand.org/content/dam/rand/pubs/technical_reports/2011/RAND_TR885.sum.pdf D. Allemang, J. Hendler, Semantic Web for the Working Ontologist, Effective Modeling in RDF and OWL, Morgan Kaufman, 2008. Noy, McGuinness, Ontology Development 101: A Guide to Creating Your First Ontology. http://www.ksl.stanford.edu/people/dlm/papers/ontology-tutorial-noy-mcguinness-abstract.html