1. Semantics in oneM2M MAS-2016-0204 Group Name: MAS
Source: Joerg Swetina (NEC)
Meeting Date: (joint with W3C WoT IG)
Agenda Item:

2. Overview Semantic information and its use in oneM2M
The oneM2M Base ontology
Relation with other semantic systems

3. Semantic information and its use in oneM2M
Semantics [si-man-tiks] (from Ancient Greek: σημαντικός sēmantikos, "significant") is the philosophical and scientific study of meaning
Ontology [on-tol-uh-jee] (from Greek oν, on (gen. oντος, ontos), i.e. "being; that which is") is a formal specification of shared concepts
Originally (rel-1) oneM2M was almost completely agnostic of the meaning of the M2M data it transported
Only size of a datum and its media type were visible in the system interpretation of the data is left to the communicating entities
In release-2 oneM2M is able to carry metadata on the meaning of M2M data and entities (devices..)
oneM2M still in general does not try to interpret the – application specific – M2M data but enables applications to do so.
Release 3 will further enhance semantic capabilities

4. Levels of meaningfulness
Problems to solve:
Is a [the same] meaning (e.g. °C) assigned to the data?
Additional context (e.g. temperature of kitchen) to the data?
Area of SEMANTIC interoperability
Area of SYNTACTIC interoperability
Problems to solve:
Does the receiver get the same data the sender has sent?
Do communicating entities use the same data format?

5. oneM2M usage of semantics
In the area of SYNTACTIC interoperability
Formally - as ontology- describe other M2M systems (devicetypes, interfaces, datamodels..) for the purpose of interworking
Semi-automatic creation of interworking entities for oneM2M
Mapping of external datamodels to oneM2M resources
In the area of SEMANTIC interoperability
Annotate M2M data with semantics, describing e.g.
Name of the data (this could contain namespace / ontology).
Relation to other M2M data.
Abstraction from specific technologies
E.g. an ON/OFF command of an abstract light-switch could be mapped to turning a light-switch of a specific technology (ZigBee, KNX, OCF …) on/off. (in rel-2 abstract commands cannot yet be invoked. This is left for rel-3)
Data brokering (advertising available data / finding relevant data)
E.g. find humidity sensors along a river
Support of Big Data Analytics
Can be supplemented with additional context information

6. How it works: Annotate oneM2M data with
A reference an ontology (= formal description of semantic information) that explains the meaning of the data
A description of the data itself and its relation to other data
… annotations can be done for several oneM2M resource types
... a resource for oneM2M data

7. Semantic annotation allows to:
Support abstraction
With a common abstraction for devices of different manufacturers
E.g. a KNX device is replaced by ZigBee device “on the fly”
Execute semantic queries:
Find devices and data based on their semantic description using SPARQL queries
E.g. find all devices in a certain area that measure a room temperature
Create (semantic) mash-ups:
New (virtual) entities by finding and combining the data from existing entities that are useful for the purpose

8. The oneM2M Base Ontology
A Thing in oneM2M is an entity that can be identified in the oneM2M System. A Thing may have properties and can have relations to other Things
A Device is a special kind of Thing that is able to interact electronically with its environment
A Functionality represents the function necessary to accomplish the task for which a Device is designed.
A Command represents an action that can be performed to support the Functionality
A Service is a representation of a Functionality to a network that makes the Functionality discoverable, registerable, remotely controllable in the network
Input- and OutputDataPoints are Variables of a Service for RESTful Devices. An Operation (is the means of a Service to communicate in a procedure-type manner over the network.
Input- OutputDataPoints and Operations are representation of a Command to a network
Input- and OutputDataPoints are Variables of a Service for RESTful Devices. An Operation (is the means of a Service to communicate in a procedure-type manner over the network.
Input- OutputDataPoints and Operations are representation of a Command to a network
The left side describes machine/technology dependent concepts (Service, Input- OutputDataPoints, Operations)
The right side describes human-understandable concepts (Functionality, Commands) of a Device

9. Using the Base Ontology
The oneM2M Base ontology
is extremely simple and flexible
is usually not used ‘as is’ but only provides a blue-print (using sub-classing of concepts) for other ontologies that are describing real-world systems
can be instantiated in oneM2M in a standardized way as oneM2M resources
=> thus the Base Ontology enables mapping of these real-world systems (device types, data-models, procedures) to oneM2M

10. Example: SAREF
“The Smart Appliances REFerence (SAREF) ontology is a shared model of consensus that facilitates the matching of existing assets (standards/protocols/datamodels/etc.) in the smart appliances domain” (available at => Because of the (subClass) mapping of SAREF to the oneM2M Base Ontology it is possible to map (natively implement or interwork with -) SAREF devices in oneM2M.
Class in SAREF
Mapping relationship
Class in Base Ontology
saref:Device
rdfs:subClassOf
oneM2M:Device
saref:BulidingObject
oneM2M:Thing
saref: BulidingSpace
saref :Command
oneM2M:Command
saref :Commodity
saref :Function
oneM2M:Funcationality
saref :Property
oneM2M:InputDataPoint OR oneM2M:OutputDataPoint
saref :Service
oneM2M:Service
saref :UnitOfMeasure
oneM2M:MetaData
saref :ActuatingFunction
oneM2M:ControllingFuncationality
saref :MeteringFunction
oneM2M:MesuringFuncationality
saref :SensingFunction
saref :State
saref:Profile,
saref:Task
oneM2M:Thingproperty
saref:DeviceCategory
saref:FunctionCategory
oneM2M:Aspect

11. A concrete SAREF example
The example taken from SAREF is a (very much simplified) washing machine:
The saref:washing machine has been manufactured by saref:manufacturer XYZ.
This type of washing machine has a saref:description "Very cool Washing Machine"
The saref:model of the type of washing machine is XYZ_Cool
The washing machine has an saref:actuating function: WashingFunction which has three saref:commands:
ON_Command
OFF_Command
Toggle_Command
The related saref:service of the washing machine that represents that actuating function is of class: SwitchOnService from SAREF. It has
an baseOnt:InputDataPoint: BinaryInput (to expose command ON_Command and OFF_Command) and
an baseOnt:Operation: ToggleBinary (to expose command Toggle_Command)
The washing machine has also a saref:function: MonitoringFunction that informs the user about the current state of the washing machine.
The saref:state of the washing machine: WashingMachineStatus can take the values „WASHING“ or „STOPPED“ or „ERROR“.
This state WashingMachineStatus is updated as a baseOnt:OutputDataPoint of a service MonitorService of the washing machine that monitors the washing machine’s behaviour.
Note: „InputDataPoint“, „OutputDataPoint“ and „Operation“ are not SAREF concepts but from the oneM2M Base Ontology

12. Relation: Base Ontology  SDT
In oneM2M the Smart Device Template (SDT) tool is available to describe device types of a system (e.g. Home Appliance Information Model - HAIM)
SDT complements the ontology description of device types as it allows to gather machine / technology dependent concepts of the device. The components of SDT can be mapped as sub-classes of the Base Ontology
Any system whose device types can be described with SDT can also be described by a – Base Ontology compatible - ontology
BO:Service
BO:Operation
BO:Operation Input
BO:Device
BO:Service
BO:Input DataPoint
BO:Output DataPoint
BO:Device
BO:Thing Property
BO:Operation

13. Relation to other semantic systems
In rel-2 oneM2M focused on using semantics (annotating resources, semantic queries), but not how to manage it (receive semantic information, manage ontologies …)
Other semantic systems may already have inherent mechanisms to gather semantic information
Directly from devices (e.g. WoT)
Through manual or automatic methods (e.g. FIWARE’s – IoT Broker)
oneM2M distinguishes between
Devices - that are capable of electronic communication and
Things - generally not capable of electronic communication
Things (rooms, streets, animals, climate …) are forming the context of M2M/IoT services (devices, service logic..)
Such Things have location, owners, seasonal temperature..
M2M/IoT services need to include context information
oneM2M seeks collaboration with other semantic systems

14. Proposal: study WoT  oneM2M
Similarities:
WoT:Thing  BO:Device
WoT: Thing Description (TD)  oneM2M: semanticDescriptor
WoT:Scripting API  oneM2M: mash-ups?
WoT:Security  oneM2M: security?
Can a BO compliant ontology be created that describes the WoT system (Servients..)
Can a [generic] Interworking be created for WoT? …

15. Thanks for your listening! Q & A