A Comprehensive Sensor Taxonomy and Semantic Knowledge Representation

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1 A Comprehensive Sensor Taxonomy and Semantic Knowledge Representation Energy Meter use Case Ranjan Dasgupta Innovation Lab Tata Consultancy Services Kolkata, India Sounak Dey Innovation Lab Tata Consultancy Services Kolkata, India Abstract The increasing use of sensors and their observations in applications like environmental monitoring, security and surveillance, health care, infrastructure, meteorology and others not only generate huge amount of sensor data but also increase complexity of integration of heterogeneous sensor devices, their data formats and procedures of measurements. Therefore ways to manage sensors, sensing devices and systems and thereby handling generation of large volume of sensor data is becoming very important. Formal definition of sensor data encodings and web services to store and access them given by Sensor Web Enablement (SWE) initiative of Open Geospatial Consortium (OGC) provide syntactic interoperability but collecting, reasoning, querying on sensors and their observations require sensor semantic compatibility. It allows users to work with domain concepts, their relations and restrictions, which is an abstraction above the technical nitty-gritty of diverse sensor data format and their integration. The paper describes various sensor concepts and their relationships extending IEEE SUMO upper level ontology and OntoSensor, including SensorML and classifies sensor information into five major sensor knowledge representation (1) hierarchy (2) data (3) function (4) data exchange and (5) domain specific along with code snippets of semantic services generated by mapping between conceptual relationships with structural relationships described in object oriented languages like C++ or Java. Index Terms Knowledge Representation, Semantics, Interoperability, OntoSensor, SensorML integration and interpretation of deluge of sensory data intensifies the existing problem of having too much data without any knowledge. Therefore in order to create situational awareness sensor data must be annotated with semantic metadata to provide essential contextual information. In order to create meaningful abstraction, the raw sensor data at first transformed into a set of observations e.g. if a smart meter measures energy or power consumption of an apartment or building, the meter reading at first should be represented as a digital value with a unit measurement e.g. X kilowatt or kilowatt hour. The second step is to transform this raw data into an observation; e.g. user consumption which is defined as a 2-tuple of power and energy (power, energy). At third step a simple rule based inference can be drawn about very-high, high, economic and low consumption footprint. Similarly raw sensor data can also be used to extract meaningful knowledge by combining with other information e.g. time of use (TOU). It is obvious that obtaining prior information about high energy consumption at weekends and low at working days due to usage and non-usage of HVAC (heating, ventilation, and air conditioning) systems is a better way of predicting and balancing power demand in smart grids. The data transformation process can be illustrated better using I. INTRODUCTION In recent years extending the current Internet with interconnected physical objects and devices referred as Things and their virtual representation is becoming a growing trend. Sensors and sensor devices are major physical elements in a cyber-physical system (CPS) [1]. Sensors of widely varied types and capabilities are increasingly being developed and deployed due to the advancement of sensor technology. Involvement of millions of sensors around CPS results in avalanche of sensor data. Data collected by different sensors and sensor devices is usually multimodal and their quality is mostly location and time dependent. The diversity, volatility and ubiquity make processing, integrating, and interpreting real world data a challenging task. The lack of Fig. 1. Concept Hierarchy (Sensor Data Transformation Process) the well-known concept hierarchy as shown in Figure 1. We adopt the meaning of layers to the context of CPS and its semantics. The lower layer always refers to large amount of /13/$ IEEE 795

2 raw data of various forms produced by sensors and sensor devices whereas layer above it is responsible to create structured and machine-readable information from them to enhance interoperability. However, what are required often by humans and high-level applications and services are not the information, but a high-level abstractions and perceptions that can provide meanings and insights of the underlying data unambiguously. Thereafter the high-level abstractions and perceptions can be transformed into actionable intelligence also known as wisdom with domain and background knowledge to exploit the full potential of CPS and create endto-end solutions. proprietary way of data exchange that can be considered for their aptness to be used in short haul and long haul communication between meter node and data collector in case II. IMPORTANCE OF SENSOR DATA SEMANTICS In CPS, sensors and sensor devices are highly distributed, heterogeneous and resource constrained. But they need to be interconnected such that communication can take place among them in different scenarios. This implies that providing interoperability among sensor data representation, data storage and data exchange are the most fundamental requirements in a CPS. Different scenarios that demonstrate why we need semantics [2] to enhance interoperability of sensors and its data are discussed as follows siting the smart energy meter use case. A. Sensor Data Abstraction Sensor data abstraction in CPS is concerned with the ways the physical data is represented and managed. In case of Smart Metering Figure 2 shows that different energy meters from Fig. 2. Interoperability Challenges (Data Representation) different manufacturers show wide variation in their energy data representation which incurs substantial integration cost. But with semantic descriptions, energy data or more generally sensor data can be characterized on different abstraction level. Lower level creates structured energy data with semantics from meter readings of various forms to enhance interoperability whereas layer above it provides high-level abstractions and perceptions which are suitable to transform into actionable knowledge and intelligence and contextual information essential for situational awareness in different application domains. B. Sensor Data Access and Exchange Capability Data access in cyber physical systems can be achieved at device or network levels with the help of low-level programming and operating systems. Obviously heterogeneity of the devices and communication modes makes data access a difficult task. Figure 3 shows plethora of protocols and its Fig. 3. Interoperability Challenges (Data Transportation) of Smart Metering [3]. C. Sensor Data Integration Ubiquitous devices or human beings are usually the origin of sensor data that always refers either attributes of a phenomenon or an entity in the real world. In order to create different abstractions of the environment as well as contextual and situational awareness, the data can either be combined or can be integrated to the data processing chain in an existing application. In either case, seamless integration of one type of heterogeneous data with other is of utmost important and it is the semantic data models that can support such integration by enabling interoperability between different sources. However, analysis and mapping between different semantic descriptions is still required to facilitate the sensor data integration with other existing domain knowledge. D. Sensor Data Semantics for Interoperability The meaning of sensor data semantic interoperability is that the different stakeholders can access and interpret the sensor data unambiguously. Provide unambiguous sensor data descriptions in such a way that they can be easily processed and interpreted by machines and software agents and eventually become a key enabler of automated data exchange in CPS. Semantic annotation of the data i.e. data associated with domain knowledge can provide machine-interpretable descriptions on (1) what data represents (2) where it originates from (3) how it can be related to its surroundings (4) who is providing it and (5) what are the technical and non-technical attributes. E. Semantically Annotated and Linked Sensor Data In CPS, a sensor is referred to as a device or entity which can provide data and a sensor service as a software entity that exposes its functionality. Search and discovery are the most important functionalities that are required in CPS in order to locate sensors and sensor services using (1) sensor and device discovery and (2) service discovery that can provide data related to the entity of interest in the physical world. Semantically annotated sensor data are essential elements to support search and discovery methods. The idea behind linked /13/$ IEEE 796

3 data [4] is to interlink sensors and sensor devices and other types of basic and derived virtual sensors which are available externally on the web using semantic links. F. Reasoning and Interpretation of Sensor Data Semantic Semantic reasoning is an important instrument in CPS domain mainly for sensor data abstraction and knowledge extraction. III. SENSOR SEMANTIC SERVICES The increasing use of sensors accompanied by greater volume of data always increases heterogeneity of devices, data formats, measurement-procedures and data interpretations. Any Internet and Web applications are facing all the challenges described above while combining, managing and querying such sensory data. Semantic technology allows user applications to work with domain concepts and restrictions and thereby operating at abstraction levels above the technical details of formatof sensor data and integration issues for diverse sets of data formats. Create sensor semantic services that operate on semantic streams where sensor streaming data will always be associated with semantic information defined in a domainspecific knowledge representation in <subject, predicate, and object> or in SPO form. A sensor conceptual model relates, infers and creates components from one form to another in the information structure e.g. a read energy function, measure consumption, which provides energy data, user-consumption data regardless of how the complexities of syntactic and communication interoperability has abstracted. The primary goal of these components is to extract new semantic information from existing sensor data streams and thereby called as semantic services or simply services. Semantic services can be realized by Unified Modeling Language (UML) component diagram e.g. <Application Components: Sensor Annotation Service> or <Application Components: Sensor Semantic Service> pragmatically. Data streams carry specific semantics as defined in a domainspecific representation, which we can call semantic streams. A sensor annotation service provides input data streams with additional properties that carry semantic information without affecting their fundamental properties. Thus, they are easy to reuse. But sensor semantic services perform semantic transformations rather than a mere data transformation. It implies that semantics of input and output data streams would be different. They must be explicitly expressed in the sensor knowledge representation. IV. SENSOR KNOWLEDGE REPRESENTATION The idea of using information system based on ontology is not entirely new. But sensor knowledge representation is different. The notion of concepts not only provides thematic information about physical phenomenon but also captures semantic description of entities (directly and/or remotely associated with sensor and its measurement) and their interrelations. Semantically annotated sensor data can be constructed by adding meaning of various entities in machine understandable ontology. It will lead to generation of semantic sensor service that will be able to get the sensor data irrespective of heterogeneity of sensors of a given type. Sensors and their observations are at the core of empirical science. Sensors transform stimuli from the physical world into observations and thereby allow to reason about the observed properties of particular features of interest. The feature type and the feature attributes have a reference to concepts in ontology. Within this ontology we are now able to map the global domain concepts to the domain-specific concepts, which are directly associated with sensor data. Defining the semantics of a feature type, i.e. using application ontology to capture the meaning of the elements and attributes used in the data model, enables reasoning on a more detailed level. Even if there is no exact match between a user's query and the semantically annotated web service, the annotations help to infer unknown parameter based on the concepts and properties described in ontology. Once discovered, the annotation can be used to identify the exact attributes containing the desired information. The sensor knowledge representation illustrated in Figure 4 comprises of seven components (1) Suggested Upper Merged Ontology (SUMO) (2) OntoSensor (3) Sensor Hierarchy Representation (SHR) (4) Sensor Data Representation (SDR) (5) Sensor Function Representation (SFR) (6) Sensor Data Exchange Representation (SDER) and (7) Domain-Specific Representation (DSR). Importantly SHR, SDR, SFR, and SDER are all domain independent sub-concepts that are created Fig. 4. Inherited Structure of Sensor Knowledge Representation by referring and extending IEEE SUMO upper level ontology and OntoSensor whereas DSR enables to integrate domain specific ontologies into the main ontology in order to design a modular sensor knowledge representation within a distributed and heterogeneous sensing environment. While referring any pictorial representations consider all broken lines & arrows are concepts belong to SUMO and OntoSensor whereas solid lines and arrows indicate new concepts which refer, use or extend them. A. Suggested Upper Merged Ontology The IEEE SUMO ontology is an upper level ontology. It defines general-purpose terms and provides a foundation for middle-level and domain ontologies. Its main objective is to provide data interoperability, information retrieval and automated inference. IEEE SUMO is a single, comprehensive, /13/$ IEEE 797

4 and cohesive structure by merging publicly available ontological contents and systematically represents the highest level concepts and relations between them. Its root node is a Thing subsuming Physical which includes everything that has time and position in space and Abstract which includes everything else. Under the concept of Physical it has two disjoint concepts of Device and Process whereas Abstract subsumes Set, Proposition, Quantity, and Attribute concepts. IEEE SUMO is a common standard ontology that helps to develop more and more robust and stable new knowledge systems with easier and faster integration of other contents. B. ONTOSENSOR OntoSensor is a sensor knowledge repository which is compatible with semantic web infrastructure. It constructs from Web Ontology Language (OWL) and extends IEEE SUMO upper-level ontology. Its concept hierarchy uses and extends concepts from IEEE SUMO and ISO in some of its relations. At one side the Sensor and Platform concepts of OntoSensor extend the Measurement Device and Transportation Device concepts whereas at the other side the Event concept of OntoSensor extends the Process concept of IEEE SUMO as described in [5]. OntoSensor [6] which extends the concepts from IEEE SUMO upper-level ontology can be viewed as a middle-level ontology whose concepts can be used by more specialized ontologies to model domain specific sensors e.g. Energy Meter as energy sensor. C. Sensor Hierarchy Representation The Sensor Hierarchy Representation (SHR) describes actuators and other physical sensor devices in the form of a knowledge model containing a hierarchy of transducer classes. As there are different user communities, there can be variety of sensor classification method resulting into variety of taxonomy Measuring Device concept from IEEE SUMO upper level ontology. The former concept now subsumes Electrical, Electro Mechanical, Induction concepts that extend OntoSensor hierarchy. Energy meter in our current purview is a specific induction type electro-mechanical sensor belongs to the same hierarchy. Fig. 6. SHR Ontology Figure 7 shows SHR implementation using Protégé ontology editor accompanied by energy meter sensor instantiation whereas Figure 8 and 9 shows its equivalent class diagram and a code snippet of its C++ implementation. Bold lines or words represent newly added concepts whereas dotted lines or words represent inherited concepts from OntoSensor. OntoSensor ontology is modified and extended using a standard well known ontology editor tool called Protégé 4.3 [9]. Fig. 7. SHR Implementation (Protégé) Fig. 5. SHR Taxonomy of sensor types resulting into variety of sensor hierarchy ontology. Several kinds of classification dimensions are possible e.g. (1) measurand (2) sensor material (3) application (4) cost (5) accuracy (6) output signal (7) modulating principle (8) transduction principle etc. But considering only measurand as the current dimension of interest, a snapshot of the taxonomical structure of SHR is shown in Figure 5. An excerpt of sensor hierarchy ontology using OntoSensor is also shown in Figure 6. The root concept in this hierarchy is the Sensor concept from OntoSensor which extends the Fig. 8. SHR Class Diagram /13/$ IEEE 798

5 Fig. 9. SHR C++ Code Snippet D. Sensor Data Representation The Sensor Data Representation (SDR) describes the dynamic and observational properties of sensors data that goes beyond just describing individual transducers. The ontological model describes the context of sensor with respect to spatial, temporal and thematic observations. A snapshot of a taxonom- Fig. 11. SDR Ontology subsumes Current Tuple, Voltage Tuple, Power Tuple concepts as Energy Meter Tuple. Specification a subconcept of Physical Properties also belongs to the same hierarchy under the purview of energy meter. Figure 12 and 13 Fig. 10. SDR Taxonomy -ical structure of SDR is shown in Figure 10. An excerpt of sensor data ontology using OntoSensor is also shown in Figure 11. The root concepts are (1) Parameter (2) Physical Properties and (3) Tuple from OntoSensor which extend IEEE SUMO upper level ontology. Parameter subsumes Energy, Power, Phase Angle concepts as Basic Quantity and Consumption concepts as Inferred Quantity. It signifies that SDR defines derived virtual sensors by utilizing the underlying notion of basic sensors to provide abstract measurements and operations e.g. energy and power meter readings may collectively monitor user consumptions. Parameter also subsumes Avg, Min, Max, RMS concepts as Electrical Quantity Parameter. Similarly Tuple subsumes Current Tuple, Voltage Tuple, Power Tuple Fig. 12. SDR Implementation (Protégé) /13/$ IEEE 799

6 OntoSensor which extends IEEE SUMO upper level ontology. Action subsumes Sensor Action, Transducer Action concepts that extend OntoSensor hierarchy and defines a functional ontology. Energy Meter Function is a new Fig. 13. SDR (Tuple) Implementation (Protégé) Fig. 16. SFR Taxonomy Fig. 14. SDR Class Diagram shows SDR implementation using Protégé ontology editor whereas Figure 14 and 15 shows its equivalent class di- Fig. 17. SFR Ontology Fig. 15. SDR C++ Code Snippet -agram and its C++ code fragments. SDR provides a universal structure with emerging semantics by defining conceptual models of various types of complex sensory data and their relationships. E. Sensor Function Representation The Sensor Function Representation (SFR) is a functional or action-oriented model for the sensors, actuators and other physical sensor devices. It contains a hierarchy of sensor action classes and describes its functions and capabilities. A snapshot of the taxonomical structure of SFR is shown in Figure 16. An excerpt of sensor function ontology using OntoSensor is shown in Figure 17. The root concept is the Action concept from Fig. 18. SFR Implementation (Protégé) Fig. 19. SFR Class Diagram /13/$ IEEE 800

7 Fig. 20. SFR C++ Code Snippet concept added to the same hierarchy. Read Function and Write Function are two major sub-concepts that model the functional role of an energy sensor. Figure 18 shows SFR implementation using Protégé ontology editor accompanied by energy meter function instantiation whereas Figure 19 and 20 shows its equivalent class diagram and a code snippet of its C++ implementation. F. Sensor Data Exchange Representation The Sensor Data Exchange Representation (SDER) is a model for communications and data exchange capabilities of sensors, actuators and other physical sensor devices. It is a knowledge representation of various sensor data exchange methods to a hierarchy of sensor capabilities. We know that any successful CPS will require an intelligent, secure and flexible bidirectional data communication between functional blocks of the system. Plethora of protocols and its proprietary way of data representation can be considered for their aptness to be used in short and long haul communication. A snapshot of the taxonomical structure of SDER is shown in Figure 21. An excerpt of sensor capability and its SDER using OntoSensor is shown in Figure 22. The root concept in this hierarchy is the Capabilities Description from OntoSensor which extends IEEE SUMO upper level ontology and subsumes Platform Capabilities and Sensor Capabilities. The latter is further extended with Data Exchange subconcept which defines a sensor data exchange ontology introducing short and long haul communications e.g. ZigBee, MBus, ModBus, Zwave, Wireless HURT, Wireless MBus, Wireless Modbus, GPRS, IPv4 and IPv6 into the hierarchy which are mostly supported by current sensors nodes. Fig.21. SDER Taxonomy Fig.22. SDER Ontology Fig. 23. SDER Implementation (Protégé) Electrical Meter Capability is a new concept related to energy sensor and added to the same hierarchy. Figure 23 shows a SDER implementation using Protégé ontology editor accompanied by energy meter sensor capability instantiation. G. Domain Specific Representation To extend the capabilities and behavior of the proposed sensor knowledge representation, the Domain Specific Representation (DSR) is meant for integrating domainspecific information with sensor ontology. It works like plug-in information that implements the knowledge representation of a particular domain of sensor data and establishes the connection with the fundamental sensor ontology. Among sub-ontologies it enables interoperability and knowledge sharing in the core ontology architecture. DSR is also logically organized on three semantic layers (1) Main Representation, (2) Extended Representation and (3) External Representation Main Representation (MR) includes central domain specific concepts. Extended Representation is logically built on MR. It defines context-aware concepts and have a meaning only in the context of main domain /13/$ IEEE 801

8 External Representation is a set of concepts which has a semantic meaning both inside and outside MR. It has a central role within the ontology because it allows the definition of semantic rules, relationships and dependencies between physical resource and external concepts. Using OntoSensor we also have defined a domain-specific representation of a Smart Meter as energy sensor. The major sub-concepts created in its Main Representation are (1) Energy Meter (2) Electrical Meter Reading (3) Electrical Meter Feature (4) Meter Action (5) Electric Meter Capability etc. Concepts such as User Consumption and Measure Consumption are Extended Representation whereas Manufacturing Details of an energy sensor is an External Representation according to our formal definition. All three conceptual energy sensor models are implemented using Protégé ontology editor accompanied by energy meter instantiations without any loss of generality of our sensor knowledge representation. V. IMPLEMENTATION VERIFICATION AND VALIDATION To implement the constructed taxonomy Protégé 3.4.0, a free tool available at Stanford University web site is used to build and edit the ontology. The language that is used to represent the sensor knowledge is RDF-XML. The key features described by RDF-XML are processed by FaCT++ and HermiT 1.3.8, built-in Protégé reasoners. We manually extend IEEE SUMO upper level ontology and OntoSensor classes by adding various sub-classes and establishing object properties among them in order to create sensor hierarchy, sensor data, sensor function, sensor data exchange and domain specific representations. After building ontologies the next step is verification and validation to check their logical correctness and consistency. Usually two major ontology tests are conducted: (1) subsumption test and (2) consistency check. The subsumption test validates class and sub-class hierarchy, their logical correctness whereas consistency check validates whether a classes have many instances or follow a singleton design pattern. A class will be treated as inconsistent if it cannot be instantiated. VI. DISCUSSION The paper defines how to classify and represent sensor information by newly describing various sensor concepts and their relationships extending IEEE SUMO upper level ontology and OntoSensor and including SensorML with an example of energy meter model. All conceptual relations between domain and range defined in the ontology has been attempted to map with structural relations e.g. generalization, association of an object oriented language. The UML descriptions and C++ code snippet at different places of discussion illustrate how relationships have been established to provide semantic services. In a sensor knowledge representation, concept likes 'Reading Type' which is-a 'Electrical Quantity Parameter' has-a relationship with 'Power' and 'Energy'. The corresponding structural relationship that is realized in a UML class diagram is 'Reading Type' which is a subclass of 'Electrical Quantity Parameter' has aggregation relation 'has Reading Type' with subclasses 'Power' and 'Energy' of 'Electrical' superclass. Their operation on sensor data with semantic information defined in sensor ontology is to allow applications to run at an abstraction level from nitty-gritty of sensor technology and assist them in querying, reasoning and inferring from sensor observation data. In our seven layer sensor description [7], it is the top-most layer whose role is to provide such interoperable semantic sensor services. The paper classifies sensor information into five major sensor knowledge representations which provide thematic annotations to sensors and their observations and give various levels of abstraction against heterogeneous (1) sensor data type (2) sensing process and (3) data transfer mechanism between sensors and sensor devices with data collectors belong to sensing systems. Spatial and temporal annotations are also inherently incorporated as a result of extending IEEE SUMO upper-level ontology and OntoSensor where later includes SensorML specification that not only provides concepts pertaining to time and location model of sensors based on Geographic Markup Language (GML) but also refers ISO standards for geographic information and services [8]. VII. FUTURE WORKS Application of various set of simple rules in sensor knowledge representation using Semantic Web Rule Language (SWRL) can be a promising future scope of work. As an example, application of a simple SWRL based rule like energy(?kwh)^hasvalue(?kwh,value)^swrlb:lessthan(?value,?3300kwh) medium(?kwh) on energy defined in energy sensor knowledge representation can figure out a typical medium electricity user consumption in the form of a set of 3- tuple set of SPO as:{ Energy, hasvalue, #3000 }, { Unit of Measurement, is, kwh }, { User Consumption, is, Medium }. A built-in small scale reasoner can easily determine out of bound, suspected error sensor data on their observations using such primitive rules. Usage of Mathematics Markup Language (MathML) helps in evaluating various expressions and empirical results e.g. (1) transfer function of a device U = (0.699 x F) where U = wind speed & F = frequency in Hz (2) temperature conversion from 0 Centigrade to 0 Fahrenheit, C/5 = (F-32)/9. Eventually enrich the entire sensor knowledge representation by introducing more and more concepts and their relationships using formal definitions of various conceptual models defined by SWE in SensorML and Observation and Measurement (O&M) specifications by referring their UML class diagrams is of utmost important. SWE developed SensorML and O&M models with a software engineering point of view. But creation of knowledge representation resulting from a direct mapping of the models can be analyzed from an ontology engineering point of view in order to infer new and deep insights from the underlying conceptualizations of central notions defined and described in the respective SensorML and O&M specifications /13/$ IEEE 802

9 VIII. ACKNOWLEDGEMENT We would like to convey our thanks to Prof. Anupam Basu of Computer Science Dept. at IIT KGP and Dr. Arpan Pal, Research Head TCS Kolkata Innovation Lab for their help, guidance and encouragement during the work. REFERENCES [1] Dhiman Chattopadhyay and Ranjan Dasgupta, A Survey of Available Sensor Data Modeling Techniques, A National Conference on Computing and Communication Systems (NCCCS) [2] Oscar Corcho and Raul Garcia Castro, Five challenges for the Semantic Sensor Web, Semantic Web. IOS. Press. vol. 1, pp , [3] Dhiman Chattopadhyay, Ranjan Dasgupta and Arpan Pal Sensor Data Modeling for Smart Meters A Methodology to Compare Different Systems, International Conference on Computing, Networking and Communications (ICNC) [4] Harshal Patni, Cory Henson and Amit Sheth, Linked Sensor Data, International Symposium on Collaborative Technologies and Systems (CTS), May [5] Ian Niles and Adam Pease, Towards a standard upper ontology, Proceedings of the International Conference on Formal Ontology in Information Systems, 2001, pp [6] David J.Russomanno, Cartik R. Kothari and Omoju A, Building a Sensor Ontology: A Practical Approach Leveraging ISO and OGC Models, Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN USA [7] Dhiman Chattopadhyay and Ranjan Dasgupta, A Novel Comprehensive Sensor Model for Cyber Physical System Interoperability for heterogeneous sensor, Sixth International Conference on Sensing Technology (ICST) [8] David J.Russomanno, Cartik R. Kothari and Omoju Thomas, Sensor Ontologies: From Shallow to Deep Models, Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN USA. [9] Protégé The official web site accessed Sept 25, /13/$ IEEE 803