Publishing Linked Sensor Data

1. Publishing Linked Sensor Data Semantic Sensor Networks Workshop 2010 In conjunction with the 9th International Semantic Web Conference (ISWC 2010), 7-11 November 2010, Shanghai, China. Presenter: Kerry Taylor, CSIRO ICT Centre, Canberra, Australia Payam Barnaghi (Uni. of Surrey), Mirko Presser (Alexandra Institute), Klaus Moessner (Uni. of Surrey) Contact author: Payam Barnaghi (p.barnaghi@surrey.ac.uk) Centre for Communication Systems Research University of Surrey

2. Sensor networks and accessing physical world data • There are currently ongoing research on creating large-scale sensor/actuator networks; This will enable connecting millions of devices that capture physical world data in a global scale. • The sensors provide observation and measurement data from the physical. • The current data transmission on sensor networks mostly relies on binary or syntactic data models which lack of providing machine interpretable meanings to the data. • Binary representation or in some cases XML-based data • No general agreement • Requires an pre-agreement on both communication parties to be able to process and interpret the data • Limited reasoning • Limited interoperability • Data integration and fusion issues

3. Physical world data on the Web • The idea is providing sensor data on the same level as the Web data. • Semantic enrichment of data and integrating the real world data into the digital world; • Providing annotations and associating the descriptions to existing ontologies and domain knowledge • There are existing standards such as those provided by OGC, SSN-XG Sensor Ontology,…

4. W3C SSN-XG ontology SSN-XG annotations makes observations of this type What it measures units SSN-XG ontologies where it is 4

5. Sensor ontologies and semantic data • The ontologies and semantic models provide machine-interpretable descriptions • There is no direct association to the domain knowledge • What a sensor measures, where it is, etc. • Association of an observation and/or measurement data to a feature of interest. • Including the domain knowledge and relating the enriched description to the existing data in the digital world will support semantic integration. • Inference mechanism can process and analyse the emerging semantics.

6. Semantic interoperability and semantic integration Making sensor-generated information usable as a new and key source of knowledge will require their integration into the (existing) information space of Communities  Semantic Integration

7. 1010 1010 1010 1010 1010 1010 Semantic integration- example “I am TWITTER” “I am a parcel for Tom, dropped once” “I am a Post van, not going to Tom” Middleware Middleware Middleware

8. Semantic integration- example Mash-up of Real World Knowledge Middleware Semantic Semantic Semantic Middleware Middleware Description Discovery Integration Distributed processing

9. Semantic integration Semantics allows to create reusable knowledge that helps to understand who is talking to whom who is doing what and what the information means This enables the integration of information as knowledge. On a large scale this machine interpretable data is a key enabler and a necessity for the Real World Internet. 9

10. Publishing linked sensor data • Using existing knowledge on the Web to annotate the sensor resources. • Associating sensor descriptions to the domain knowledge. • Defining links between sensor observation and measurement and features of interests using the existing knowledge and domain ontologies. • Making sensor descriptions as a part of Web data and accessible through standard interfaces.

11. Linked data principles • The principles in designing the linked data are defined as: • using URI’s as names for things; • using HTTP URI’s to enable people to look up those names; • provide useful RDF information related to URI’s that are looked up by machine or people; • including RDF statements that link to other URI’s to enable discovery of other related things of the web of data;

12. Linked Data- Connecting distributed data across the Web - There are more than 13.1 billion interlinked RDF triples. - more than 142 million RDF links (properties). 12

13. Sensor data and linked data * The middle layer is adapted from Amit Sheth et al., “Semantic Sensor Web”

14. Publishing linked sensor data • We use existing linked-data to annotate sensor data and to associate the description to the domain knowledge; • We also publish the sensor data a linked data resources.

15. Using linked data for annotation

16. Using linked data for annotation – location model • We have a two layers location description; • A detailed location ontology for local descriptions; • A location attribute (concept) obtained from linked data (e.g. DBPedia, GeoNames); • The local ontology provides detailed location description (e.g. rooms, buildings on our campus) and the linked data concepts provide high-level concepts (e.g. University of Surrey) and then we linked these two models;

17. Using linked data for annotation – location model Internal location ontology (local) Lined-data location (external)

18. Using and reasoning the publishing linked sensor data

19. Components and architecture

20. The World at your Fingertips The world is the knowledge base Despite data volume, heterogeneity, distribution, dynamics: Integration/access all that data like aset of interconnected resources in an information network! - Structured Querying - Integrated Views - Aggregation, Analyses  Reasoning upon the data 20

21. Thank you!