Enrichment of Sensor Descriptions and Measurements Using Semantic Technologies. Student: Alexandra Moraru Mentor: Prof. Dr.

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1 Enrichment of Sensor Descriptions and Measurements Using Semantic Technologies Student: Alexandra Moraru Mentor: Prof. Dr. Dunja Mladenić

2 Environmental Monitoring automation Traffic Monitoring integration Interoperability Building Monitoring Health Monitoring Industrial Process Monitoring Images source: M. Botts, G. Percivall, C.Reed, J. Davidson, OGC SWE: Overview And High Level Architecture 2

3 Motivation Understanding and managing of sensor data We want a way of doing sensing that can make the data available to any application that needs that specific data [1] Challenges: associate meaning to sensor data computer understandable representation many communities participate in sensor deployments Semantic Technologies identified as key enabling technologies for sensor networks (W3C) semantic enrichment can be considered as a first step [1]John Cox, Turning the world into a sensor network, Network World, August 11,

4 Contributions Semantic enrichment of sensor descriptions and measurements Definition of a framework Instantiation of framework components Examples of applications 4

5 Part I Framework Definition Introduction Problem Description Framework Components 5

6 Introduction (1/2) Sensor material or device which changes its properties according to a physical stimulus can be attached to more complex devices sensor nodes computing and communication capabilities embedded into physical objects wired and wireless networks 6

7 Introduction (2/2) Internet of Things world-wide network of heterogeneous smart objects sensors, actuators, RFIDs, MEMS based on standard communication protocols focused on establishing connectivity Web of Things integrating smart objects into the Web a.k.a Sensor Web, Physical Web based on standards like HTML, XML, RSS focused on application layer 7

8 Problem Description (1/2) Integration of sensor data from different systems Provide machine understandable representation of data Describe the meaning of data and the context in which it was collected Environment characteristics Sensor properties 8

9 Problem Description (2/2) Apply semantic technologies to sensor web Enriching the sensor data enrichment of data generally refers to adding information semantic enrichment refers to associating semantic tags Publishing annotated sensor data enables the development of new applications through standardized web services, application specific methods requires prior knowledge of the infrastructure used following Linked Open Data (LOD) principles 9

10 Conceptual Framework Framework for semantic enrichment of sensor data automatizing the process enriching sensor descriptions and measurements Ontology Extension Ontology Collection Descriptions Enrichment Query End-Point Semantic Browsers Measurements preprocess and enrichment Semantic Repository of Sensor Data Inference Engines Sensor Descriptions and Measurements Enrichment Components Data Consumers 10

11 Framework Components (1/3) Ontology Collection Ontology Extension Sensor Descriptions and Measurements Sensor description refer to the metadata defining sensor characteristics Sensor measurements numerical values quantifying the changes of sensor properties Sensor Descriptions and Measurements Ontology Collection set of ontologies necessary for describing sensor characteristics and providing context for sensor measurements. 11

12 Framework Components (2/3) Enrichment Components sensor descriptions are enriched with semantic concepts sensor measurements are processed to generate new features which are then enriched by semantics. Ontology Collection Sensor Descriptions and Measurements Ontology Extension Descriptions Enrichment Measurements preprocess and enrichment Enrichment Components Steps of the enrichment process Analysis of the sensor descriptions and measurements Selection of ontologies Extension of the selected ontologies with concepts specific to the domain of application Implementation of enrichment components 12

13 Framework Components (3/3) Semantic Repository of Sensor Data contains the enriched sensor descriptions and measurements. Data Consumers query end-points semantic browsers Inference engines Ontology Extension Ontology Collection Descriptions Enrichment Query End-Point Semantic Browsers Measurements preprocess and enrichment Semantic Repository of Sensor Data Inference Engines Sensor Descriptions and Measurements Enrichment Components Data Consumers 13

14 Part II Instantiation of Framework Components Sources of Sensor Data Non-standardized Standardized Ontology Collection OWL ontologies Cyc ontology Architecture Enrichment Components Semantic Repository of Sensor Data 14

15 Sources of Sensor Data (1/2) Non-standardized dataset data collected from a sensor network for monitoring environmental conditions temperature, humidity, luminance and pressure centralized MySQL database server, both the meta-data and sensor measurements. 15

16 Sources of Sensor Data (2/2) Standardized dataset contains description and measurements of sensors in the area of ocean tides and currents air temperature, water temperature, water level, currents, wind, air pressure, salinity sensor description (SensorML) sensor measurements (O&M) 751 sensor nodes, 1379 sensors measuring 14 types of properties downloaded and processed offline 16

17 Ontology Collection (1/3) OWL ontologies W3C Semantic Sensor Network ontology (infrastructure) 17

18 Ontology Collection (2/3) Additional (external) OWL ontologies Basic GeoWGS84 Vocabulary, provides namespaces for representing coordinates Geonames, provides geographical names in RDF representation findnearbyplacename web service W3C time ontology defining time intervals for sensor measurements 18

19 Ontology Collection (3/3) Research Cyc 19

20 Architecture OWL Ontologies SSN Ontology Jena Framework SPARQL Endpoint SESAME MySql Database JDBC RDF descriptions of sensors Pubby Data Publishing SensorML descriptios JAXB API ResearchCyc O&M measurements OntoGen OntoGenUI 20

21 Enrichment Components (1/4) Enrichment of sensor data Input: sensor data + ontologies manually creating rules extract the information from the datasets attach the corresponding semantic concepts for the non-standardized dataset -> OWL ontologies for the standardized dataset -> both collections of ontologies, separately Output: RDF representation of the original dataset annotated with semantic concepts from the ontology collection 21

22 Enrichment Components (2/4) Non-standardized dataset Database 22

23 Enrichment Components (3/4) Standardized dataset platforms described in SensorML -> instances of Platform. the networks to which these platforms belong to -> instances of Deployment. the platforms components -> instances of SensingDevice. the observed properties of sensing devices -> instances of the subclasses extending Property related to the sensed domain by the using the relation ispropertyof and the subclasses extending FeatureOfInterest. the geographical locations of the platforms, given by latitude and longitude coordinates -> lat and long relations from the GeoWGS84 vocabulary. findnearbyplacename web service (fro GeoNames) -> finds the name of the closest populated place to the platform location Computation based enrichment of measurements 23

24 Enrichment Components (4/4) Standardized dataset Enrichment of measurements data mining tool for processing the sensor measurements and for extracting knowledge from the raw measurements enriched measurements annotated according to the collection of OWL ontologies exported in RDF format permits the user to take advantage of knowledge extracted from the raw measurements Features generated from the raw sensor measurements wind and sea conditions for sailors according to the Beaufort scale 26 nominal values, such as: Calm, Flat, Fresh Breeze, etc. migraines caused by atmospheric pressure according to pressure values published in medical studies risk of headache: NoHeadache, Headache and HighHeadache time of day intervals: early morning, late evening, etc. 24

25 Semantic Repository of Sensor Data Implementation of Semantic Repository of Sensor Data Sesame, framework for processing RDF data store, parse, query, perform inference on RDF data Java API (storing and updating the enriched sensor data) for each dataset a separate repository 25

26 Part III Applications Sensor search Data Publishing 26

27 Sensor Search (1/3) Finding specific sensors, from which one could be interested in gathering data Searching directly through sensor measurements for explicit values or different events. Formulating queries for retrieving the results we need SPARQL end-points 27

28 Sensor Search (2/3) Which are the sensors measuring temperature located in the Vič region of the city of Ljubljana? SELECT DISTINCT?s WHERE {?sn ssn:hassubsystem?s.?s ssn:observes < ssn:onplatform?p.?p foaf:based_near < 28

29 Sensor Search (3/3) Which are the locations with risk of headache on early morning? SELECT DISTINCT?sensor?location where{?platform ssn:attachedsystem?sensor.?platform geo:location?loc.?loc foaf:based_near?place.?place gn:name?location.?sensor ssn:madeobservation?obs.?obs ssn:observationresult?res.?obs time-entry:startsorduring tmint:earlymorning.?res ssn:hasvalue obsval:headache. } 29

30 Data Publishing (1/2) Publishing methods standardized web services OGC s SOS application specific: Pachube, Sensorpedia Linked Sensor Data Linked Data method of exposing, sharing, and connecting data via dereferenceable URIs on the Web. URI for the real-world object itself. URI for a related information resource that describes the real-world object and has an HTML representation. URI for a related information resource that describes the real-world object and has an RDF/XML representation. 30

31 Data Publishing (2/2) Pubby open source tool that provides Linked Data interfaces to SPARQL end-points rewrite URIs found in the RDF dataset into Pubby server s namespace simple HTML interface about each resource. 31

32 Conclusions Summary Definition of a framework for semantic enrichment of sensor data Instantiation of the framework components Discussion about possible applications Lessons Learned enriching standardized vs. non-standardized dataset domain ontologies and application ontologies representation of physical and virtual sensors Sensors as physical entities Sensors as virtual entities Sensors as either physical or virtual entitites 32

33 Future Work Development of new enrichment components Static and stream data Enrichment at sensor node level serving semantic descriptions from the nodes Represent time and space on higher semantic levels strdf/stsparql, GeoSPARQL, EP-SPARQL URI generation Integration with complex events 33

34 Bibliography Moraru, A.; Vučnik, M.; Porcius M.; Fortuna, C.; Mladenić, D. Exposing Real World Information for the Web of Things. In Proceedings of the 8 th International Workshop on Information Integration on the Web, in conjunction with 20 th International World Wide Web Conference (2011). Moraru, A.; Fotuna C.; Mladenić, D. A System for Publishing Sensor Data on the Semantic Web. In Proceeding of 33 rd International Conference on Information Technology Interfaces (June ). Dali, L; Moraru, A.; Mladenić, D. Using Personalized PageRank for Keyword Based Sensor Retrieval. In Proceedings of 4 th International Semantic Search Workshop, located at the 20 th International World Wide Web Conference, (March ). Moraru, A.; Pesko, M.; Porcius, M.; Fortuna, C.; Mladenić, D. Using Machine Learning on Sensor Data. Journal of Computing and Information Technology, 18, (2010). 34

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