Presentation on theme: "Using Ontologies to make Smart Cities Smarter Rosario Uceda-Sosa, Biplav Srivastava and Bob Schloss IBM Research"— Presentation transcript:
Using Ontologies to make Smart Cities Smarter Rosario Uceda-Sosa, Biplav Srivastava and Bob Schloss IBM Research @.ibm.com June 2012
Application Developer/ Consultant A Semantic Data Model for Smart Cities An ontology can make a city interconnected and smart, but it needs to assume that 1.Cities have their own data sources, not necessarily connected, and may not want to consolidate them. 2.Cities have non-standard organizations, departments and competencies. 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. [ETL] Semantic Data Model 2. Reuse 3. Metadata Extensions Data Model Data sources
… but, what is an ontology, anyway? What do you think?
… but, what is an ontology, anyway? In a Smart City domain, were 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 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
Not all ontologies are created equal 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). 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 PurposeInstancesInferencingExamples As a deductive system Deductive System (axioms + deductive rules) Part of the knowledge base Defined by rules.Expert systems, Planning, Optimization. As a data blueprint Constrain a domainMust conform to the normative schema determined by the ontology Subsumption, class inferencing Biomedical and life sciences (FMA, Radlex) As a data classifier Classify open dataUnknown formatsSubsumption, class inferencing Tag ontologies (MOAT, Echarte, SCOT, NAO, etc.) As a data integrator Integrating pre-defined model to existing data sources Instances are mapped, no constraint enforcement. Subsumption, class, entity inferencing SCRIBE As data mapping vocabulary Mapping to/from existing data sources Mined instances determine the ontology/schema. Subsumption, class inferencing D2RQ (a tool) Normative schema Integrative Schema, depend on instances
What makes a good ontology for data integration? Human Usability Communicable. Naming, natural language support, etc. Concise. A simple way to describe the key entities of the model and yet able to infer many facts Consistent. Naming conventions and modeling patterns Authoritative to domain experts Documented, not just descriptions, but also provenance Managed and maintained by people throughout the model lifecycle. Reusable in similar domains, for similar instances. System Usability Scalable so large amounts of data can be parsed, stored and retrieved. Efficient query and inferencing Programmable solutions, both in open and closed data paradigms. Open infrastructure and tools A good ontology is a useful ontology, an ontology that both humans and systems can process.
The SCRIBE Model of Cities
Scribe design decisions A good ontology is a useful ontology, an ontology that both humans and systems can process. Human Usability Communicable. Naming, natural language support, etc.Natural language naming, user readable labels Concise. A simple way to describe the key entities of the model and yet able to infer many facts Anchor classes: events, services, assets, KPIs. Simple and expressive OWL sublanguage, relation taxonomies Consistent. Naming conventions and modeling patternsClear boundaries between classes and instances. Authoritative to domain expertsAlignment with standards Documented, not just descriptions, but also provenanceWealth of annotations Managed and maintained by people throughout the model lifecycle.Class stewards, involvement of domain experts and end users Reusable in similar domains, for similar instances.Mechanisms for modularization of extensions and customizations System Usability Scalable so large amounts of data can be parsed, stored and retrieved. Caching mechanisms for DB data (?) Efficient query and inferencingOntology-based inferencing (?) Programmable solutions, both in open and closed data paradigms.Data adapters and schema exploring (?) Open infrastructure and toolsJena, DB2DRQ, Ruby on Rails, etc.
Authoritative Aligned with standards (CAP, NIEM, MISA/MRM, UCore) Validate with customer scenarios Validated with open city data 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 SCRIBE Core Model City Customization Common building blocks Extension Weather Water Transportation BuildingAndParcel AssetManagement Simple language Classes + Inheritance + Relations + Inferencing Based on standards (OWL-QL, SPARQL) Mappable to UML Metadata annotations and Tagging Organization/Operation profile Features Authoritative Aligned with standards (CAP, NIEM, MISA/MRM, UCore) Validate with customer scenarios Validated with open city data Simple language Classes + Inheritance + Relations + Inferencing Based on standards (OWL-QL, SPARQL) Mappable to UML Metadata annotations and Tagging Authoritative Aligned with standards (CAP, NIEM, MISA/MRM, UCore) Validate with customer scenarios Validated with open city data
The key concepts of the SCRIBE Ontology 1. Describes messages, events and services as they flow through the system Before/after triggers Message (Advisory) Event (Storm, RoadWork) WorkItem (RoadWorkWI) WorkItem (RoadWorkWI) WorkItem (RoadWorkWI) WorkItem (RoadWorkWI) WorkItem (RoadWorkWI) Protocol (InfrastructureWorkP) Protocol (InfrastructureWorkP) Protocol (InfrastructureWorkP) Before/after triggers Before/after triggers 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 Agency (WhitePlainsTraffic) CityServiceArea Owns Asset (pipe, valve)
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. CAPUCoreNIEMMISA/MRM Core entities Alert, message certainty, security, urgency IncidentPeople, Places, Events and Things Program, service, outcome, target group, outcome. Advantages Simple to implement and read. Established standard Extension mechanisms defined. Supported by DoD, DHS, DoJ. Tools for search and subset extraction (SSTG) Established standard. Well defined extension process (IEPD) International, municipality based Issues Subject and related resources are underdefined Not mature enough, incomplete. Large (4000 concepts) and cumbersome (even with support tools) Not deep in any domain Represents administration, business planning of a city, not its operation. Cumbersome to extend. Representational Language XML XML with schema substitution for inheritance XML (rdfs?)
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. ExternalEvent Event Entity (Person, Organization, - item) NIEM-Based Overlap, superset, etc. CAP-Based Overlap, superset, etc. Role (Person, Organization) Stakeholder hasRole -> isStakeholder -> Message Is-a subject ServiceArea (Public Safety, etc.) (1) Organization (CityOrganization) causes RoadRepair WorkOrder Stakeholder1 Transportation Dept Planner Tom Travis Intersection: Main And Hamilton Maximo-Based Overlap, superset, etc. Asset WorkItem
The SCRIBE Metadata
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
SCRIBE tooling Database Schema Semantic model of events, city assets, geography and resources, city organization and services, KPIs, processes, EndUser Application Developer/ Consultant SCRIBE is also a. A modeling process B. Tools to make the model usable. The first tool weve 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. City Data Catalog Content Simple subset of OWL, directly mappable to UML Implementation Model Tooling SCRIBE is written using standard RDF/OWL editors and software (Jena) Edit, extend model Query/Navigate Model and Data Customize Model Integrate with Data Standard OWL/XML (TopBraid, Protégé, Pellet, SPARQL, etc. ) MIDO, DB2RQL, R2DQ, etc. Form-based queries? Record-based navigation? SCRIBE Core Model City Customization MIDO
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 Scribe CORE Standards (CAP, NIEM, MISA/MRM, etc.) Scenarios/Data (cities open data) Washington D.C. Chicago Dublin Services Departments Assets KPIs Services Departments Assets KPIs Services Departments Assets KPIs MIDO Maps city data to Scribe. Populates model with instance data
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)
How to map 311 events to an existing model 311 Requests (2010) 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: ServiceRequest DC311SvceReq ID Type DateOrdered Lon/Lat … Ward 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. IS-A Event A B C 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.
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: IDs have different format Time is ReportDateTime, and has a time of day, not just a date Offenses do not have codes Theres 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 well need inferencing, infrastructure to keep the graphs… Well be replicating RDF/OWL Crime Incidents (2010) Mapping 911 (crime) incidents A B C
… And there are net benefits to a model-driven, semantic approach: 1.Applications can be coded in the abstract. E.g., Display all current events independently of whether they are 311 or Applications can refine the metadata without having to touch the code or the underlying data. E.g. Display all sanitation requests 3.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. The right data integration point. A semantic model approach
Step 1. Customizing SCRIBE for Washington D.C. 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. SCRIBE captures the basics of events, service types, dates, etc. but we dont expect the model to be comprehensive. For example, we didnt 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.
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.) 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. ServiceRequest ServiceRequestID ServiceType associatedTo ServiceTypeDescriptor codeData hasDescriptor
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. … Notice that some of the data is missing in the original table… Thats still ok Query: All Events in DC, with type, District and Ward
As shown previously. The inferencing in the ontology can be leveraged in a query. Step 3. Query through the model. Annotation Metadata Query: Public Sanitation Service Requests
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
Scribe design decisions A good ontology is a useful ontology, an ontology that both humans and systems can process. Human Usability Communicable. Naming, natural language support, etc.Key to management and model validation Concise. A simple way to describe the key entities of the model and yet able to infer many facts Balance between simple language (RDF), conciseness and inferencing power is key to usability. Map to UML. Consistent. Naming conventions and modeling patternsUse of relation taxonomy to infer relations despite extensions. Authoritative to domain expertsMerging standards is not enough. Alignment with standards allows a consistent model. Documented, not just descriptions, but also provenanceLimited benefit to end users unless coupled with sample instances or data entry forms Managed and maintained by people throughout the model lifecycle.People not always available for the full lifecycle Reusable in similar domains, for similar instances.Mechanisms for promotion of changes to the core. System Usability Scalable so large amounts of data can be parsed, stored and retrieved. Not clear whether data should remain in RDB Efficient query and inferencingImpact analysis queries may require a few seconds. This is OK. Programmable solutions, both in open and close data paradigms.dA standard library of data adapters and mappings to SCRIBE are needed. Open infrastructure and toolsWe used Jena, DB2DRQ, Ruby on Rails, etc.
For more information OR
References A direct map of relational data to RDF, W3C working draft 14 March, 2011, R2RML: RDB to RDF Mapping Language, W3C Working Draft 24 March 2011, The D2RQ Platform v0.7 - Treating Non-RDF Relational Databases as Virtual RDF Graphs, , Hannes Bohring and Soren Auer, Mapping XML to Ontologies, citeseerx.ist.psu.edu/viewdoc/download?doi= T. nf Rodrigues, P. Rosa, J. Cardoso, Mapping XML to existing OWL ontologies, citeseerx.ist.psu.edu/viewdoc/download?doi= DB2OWL, A tool for automatic Database-To-Ontology mapping, summary?doi= Municipal Information Systems Association/Municipal Reference Model (MISA/MRM), National Information Exchange Model, 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, D. Allemang, J. Hendler, Semantic Web for the Working Ontologist, Effective Modeling in RDF and OWL, Morgan Kaufman, Noy, McGuinness, Ontology Development 101: A Guide to Creating Your First Ontology.