ContoExam: an ontology on context-aware examinations

GeoSpatial Semantics, 2009

An ontology of observation and measurement is proposed, which models the relevant information processes independently of sensor technology. It is kept at a sufficiently general level to be widely applicable as well as compatible with a broad range of existing and evolving sensor and measurement standards. Its primary purpose is to serve as an extensible backbone for standards in the emerging semantic sensor web. It also provides a foundation for semantic reference systems by grounding the semantics of observations, as generators of data. In its current state, it does not yet deal with resolution and uncertainty, nor does it specify the notion of a semantic datum formally, but it establishes the ontological basis for these as well as other extensions.

Journal of Database Management, 2000

This paper introduces a measurement ontology for applications to semantic Web applications, specifically for emerging domains such as microarray analysis. The semantic Web is the nextgeneration Web of structured data that are automatically shared by software agents, which apply definitions and constraints organized in ontologies to correctly process data from disparate sources. One facet needed to develop semantic Web ontologies of emerging domains is creating ontologies of concepts that are common to these domains. These general, "common-sense" ontologies can be used as building blocks to develop more domain-specific ontologies. However most measurement ontologies concentrate on representing units of measurement and quantities, and not on other measurement concepts such as sampling, mean values, and evaluations of quality based on measurements. In this paper, we elaborate on a measurement ontology that represents all these concepts. We present the generality of the ontology, and describe how it is developed, used for analysis and validated.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES

Recently, the use of sensor-based systems in many areas has led to an exponential increase in the raw sensor data. However, the lack of neither syntactic nor semantic integrity between these sensor data limited their sharing, reusability, and interpretation. These inabilities can cause some problems. For example, different wireless sensor networks may not work together due to the subtle variations in their sensing methods, operating systems, syntax, and data structure. In recent years, to cope with these inabilities, the semantic sensor web approach, which enables us to enrich the meaning of sensor data, has been seen as the critical technology in solving these problems by some researchers. The primary purpose of this study is to create a laboratory environment parameters sensor ontology (LEPSO) that provides a standard data model for heterogeneous sensor data from different platforms by expanding semantic sensor networks (SSN). A case study was conducted using the real-time data collected from Bolu Abant İzzet Baysal University, Scientific Industrial Technological Application and Research Center in order to demonstrate that the proposed LEPSO can be used in similar sensor-based applications. A series of semantic queries have been performed on the collected sensor data to evaluate the proposed sensor ontology. The results showed that sensor data, which are heterogeneous by nature, provide benefit results in sensor-based monitoring systems when enriched with semantic web technologies and ontologies. Besides, this study proves that the proposed semantic sensor ontology, which used the semantic sensor network framework, has the capability to provide a common infrastructure for many sensor-based applications. The proposed ontology has the potential to become a more comprehensive ontology by adding different platforms, different sensors, different environments such as school, factory. In the next study, it is aimed to expand the scope of this semantic sensor network, which is formed by including this ontology in the intensive care unit of a hospital.

2009

The increasing availability of sensor data through a variety of sensor-driven devices raises the need to exploit the data observed by sensors with the help of formally specified knowledge representations, such as the ones provided by the Semantic Web. In order to facilitate such a Semantic Sensor Web, the challenge is to bridge between symbolic knowledge representations and the measured data collected by sensors.

Journal of Web Semantics

The Sensor, Observation, Sample, and Actuator (SOSA) ontology provides a formal but lightweight general-purpose specification for modeling the interaction between the entities involved in the acts of observation, actuation, and sampling. SOSA is the result of rethinking the W3C-XG Semantic Sensor Network (SSN) ontology based on changes in scope and target audience, technical developments, and lessons learned over the past years. SOSA also acts as a replacement of SSN's Stimulus Sensor Observation (SSO) core. It has been developed by the first joint working group of the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) on Spatial Data on the Web. In this work, we motivate the need for SOSA, provide an overview of the main classes and properties, and briefly discuss its integration with the new release of the SSN ontology as well as various other alignments to specifications such as OGC's Observations and Measurements (O&M), Dolce-Ultralite (DUL), and other prominent ontologies. We will also touch upon common modeling problems and application areas related to publishing and searching observation, sampling, and actuation data on the Web. The SOSA ontology and standard can be accessed at https://www.w3.org/TR/vocab-ssn/.

IEEE Symposium on Computational Intelligence in healthcare and e-health (IEEE CICARE 2013), 2013

"Much effort has been spent on the optimization of sensor networks, mainly concerning their performance and power efficiency. Furthermore, open communication protocols for the exchange of sensor data have been developed and widely adopted, making sensor data widely available for software applications. However, less attention has been given to the interoperability of sensor networks and sensor network applications at a semantic level. This hinders the reuse of sensor networks in different applications and the evolution of existing sensor networks and their applications. The main contribution of this paper is an ontology-based approach and architecture to address this problem. We developed an ontology that covers concepts regarding examinations as well as measurements, including the circumstances in which the examination and measurement have been performed. The underlying architecture secures a loose coupling at the semantic level to facilitate reuse and evolution. The ontology has the potential of supporting not only correct interpretation of sensor data, but also ensuring its appropriate use in accordance with the purpose of a given sensor network application. The ontology has been specialized and applied in a remote patient monitoring example, demonstrating the aforementioned potential in the e-health domain."

Expert Systems with Applications, 2014

In the past years, the large availability of sensed data highlighted the need of computer-aided systems that perform intelligent data analysis (IDA) over the obtained data streams. Temporal abstractions (TAs) are key to interpret the principle encoded within the data, but their usefulness depends on an efficient management of domain knowledge. In this article, an ontology-based framework for IDA is presented. It is based on a knowledge model composed by two existing ontologies (Semantic Sensor Network ontology (SSN), SWRL Temporal Ontology (SWRLTO)) and a new developed one: the Temporal Abstractions Ontology (TAO). SSN conceptualizes sensor measurements, thus enabling a full integration with semantic sensor web (SSW) technologies. SWRLTO provides temporal modeling and reasoning. TAO has been designed to capture the semantic of TAs. These ontologies have been aligned through DOLCE Ultra-Lite (DUL) upper ontology, boosting the integration with other domains. The resulting knowledge model has a modular design that facilitates the integration, exchange and reuse of its constitutive parts. The framework is sketched in a chemical plant case study. It is shown how complex temporal patterns that combine several variables and representation schemes can be used to infer process states and/or conditions.

2007

This article introduces a measurement ontology for applications to Semantic Web applications, specifically for emerging domains such as microarray analysis. The Semantic Web is the nextgeneration Web of structured data that are automatically shared by software agents, which apply definitions and constraints organized in ontologies to correctly process data from disparate sources. One facet needed to develop Semantic Web ontologies of emerging domains is creating ontologies of concepts that are common to these domains. These general "common-sense" ontologies can be used as building blocks to develop more domain-specific ontologies. However most measurement ontologies concentrate on representing units of measurement and quantities, and not on other measurement concepts such as sampling, mean values, and evaluations of quality based on measurements. In this article, we elaborate on a measurement ontology that represents all these concepts. We present the generality of the ontology, and describe how it is developed, used for analysis and validated.

The aim of this paper is the description of our structure. This structure is created as an extension of Sensor ML. The first part of the paper is description of logic, which is used to describe the ontology. We describe rules for description logic and we define formal definition of ontology. The second part of the paper is about description of our structure. There are description tables which are used in structure and their properties.

ABSTRACT The spreading of devices and applications that allow people to collect personal information opens new opportunities for user modeling (UM). In this new scenario UM together with personal informatics (PI) can offer a new way for self-monitoring that can provide the users with a sophisticated mirror of their behavior, attitudes and habits and their consequences on their life, on the environment and on contexts in which they live in. These new forms of self-reflection and self-knowledge can trigger and motivate the behavior change. In this paper we describe the first step in this direction, focusing on opportunities offered by semantic web ontologies for data integration and reasoning over data for recommendation purposes.