Farha Ali

An Efficient Ontology Comparison Tool for Semantic Web Applications

With the growing access to heterogeneous and independent data repositories, determining the semantic difference of two ontologies is critical in information retrieval, information integration and semantic web applications. In this paper, we propose an ontology comparison tool based on a novel senses refinement algorithm, which builds a senses set to accurately represent the semantics of the input ontology. The senses refinement algorithm automatically extracts senses from the electronic lexical database WordNet (locally installed or online), removes unnecessary senses based on the relationship among the entity classes of the ontology, and specifies relations and constraints of the concepts in the refined senses set. The senses refinement converts the measurement of ontology difference into simple set operations based on set theory, thus ensures the efficiency and accuracy of the ontology comparison. Our experimental studies show that the proposed senses refinement algorithm outperforms the naive senses set construction algorithm in terms of efficiency and accuracy.

A Web service for efficient ontology comparison

In this paper, we develop a Web service for ontology comparison based on a novel senses refinement algorithm, which builds senses sets to represent the semantics of the input ontologies. The senses refinement algorithm converts the measurement of ontology difference into simple set operations based on set theory, thus ensures the efficiency and accuracy of the ontology comparison. We believe our Web service is the first available online measurement tool for ontology comparison.

Teaching The Internet of Things Concepts

Named one of the disruptive technologies of the current world, Internet of Things (IoT) is being adopted in many areas of every day life. With an expected growth of exponential measures, IoT brings the promise of generating huge revenues. Companies are taking notice and investing in IoT related products. With this growth rate, we can see an increasing demand for professionals trained in developing and maintaining IoT related projects. The escalated demand for the professionals in computing fields with the knowledge of IoT motivated us to teach IoT concepts as special topics courses to the Computer Information Systems (CIS) majors. This paper summarizes our efforts, experiences, and reflections while teaching two offerings of IoT course.

The Intelligent River©: Implementation of Sensor Web Enablement technologies across three tiers of system architecture: Fabric, middleware, and application

Population growth, energy demand, and climate change are placing an unprecedented strain on water resources, requiring a fundamental shift in how these resources are managed. More precisely, resource management programs must embrace a new paradigm, one with realtime environmental monitoring at its core. The Intelligent River© is an environmental and hydrological observation system engineered to support research and management of water resources at watershed scales. The system architecture is comprised of three primary tiers: (i) a field-deployed sensor fabric and uplink infrastructure, (ii) real-time data streaming middleware, and (iii) repository, presentation, and web services. Sensor Web Enablement (SWE) adoption decisions revolve around balancing efficiency concerns and implementation time with capability and standards compliance. In this context, our team has examined, applied, and evaluated SWE technologies to enable data archival, access, and discovery. We have found varying levels of success with SWE adoption across the three tiers. At the fabric layer, platform configurability and ease-of-integration have been important engineering drivers. SensorML arose as a natural candidate solution; however, its resource requirements are largely incompatible with our target hardware platforms. At the middleware layer, recent efforts have focused on the use of SensorML and a metadata catalog to perform metadata annotation. This solution appends SensorML elements onto incoming observations, supporting data processing and semantic resolution. During early development of middleware technologies, we linked sensor platforms with web services using the transactional profile of the Sensor Observation Service (SOS) to perform data insertion and retrieval queries. At the application level, SOS is used to support data discovery and access, and Sensor Alert Service (SAS) is used to provide near-real time notifications of sensor status and QA/QC failures. In this paper, we report on our experiences, both positive and negative, and outline potential solutions to some of the most important obstacles we have encountered.

A metadata encoding for memory-constrained devices

With the broad applicability of wireless sensor networks across fields, it is desirable to develop self-describing sensor nodes that can operate in a plug-n-play manner. In this paper, we present MoteML, a metadata encoding suitable for storage on memory-constrained devices, designed in support of this goal. MoteML is consistent with Sensor Web Enablements [23] Sensor Model Language (SensorML). More specifically, while MoteML does not conform to the SensorML schema, it can be translated into SensorML and vice-versa. This paper explores the available solutions for storing self-describing information on memory-constrained sensor nodes and presents the design of MoteML. MoteML is a text-based encoding that captures a subset of SensorML in a template-based structure. This text data is then compressed using available text compression techniques. The resulting file is small enough to be stored on a memory-constrained embedded device.

An efficient method to measure the semantic similarity of ontologies

With the recent availability of large number of bioinformatics data sources, query from such databases and rigorous annotation of experimental results often use semantic frameworks in the form of an ontology. With the growing access to heterogeneous and independent data repositories, determining the semantic similarity or difference of two ontologies is critical in information retrieval, information integration and semantic web services. In this paper, a sense refinement algorithm is proposed to construct a refined sense set (RSS) for an ontology so that the senses (synonym words) in this refined sense set represent the semantic meanings of the terms used by this ontology. In addition, a semantic set that combines the refined sense set of ontology with the relationship edges connecting the terms in this ontology is proposed to represent the semantics of this ontology. With the semantic sets, measuring the semantic similarity or difference of two ontologies is simplified as comparing the commonality or difference of two sets. The experimental studies show that the proposed method of measuring the semantic similarity or difference of two ontologies is efficient and accurate.

The smart surface network

We present the Smart Surface Network (SSN), a hardware and software platform designed for dense sensing. Sensor nodes connected to the SSN communicate using a serial bus integrated within a mountable physical surface. The hardware architecture and bus access and communication mechanisms are implemented in a self-stabilizing manner, providing robust handling of unannounced arrivals and departures of network devices. An associated API supports a peer-to-peer communication paradigm, providing access to the physical, data link, and application layers of the bus. In this paper, we describe the SSN hardware architecture and present the bus access and peer discovery algorithms. We also discuss the design of the API and describe experimental results characterizing the fairness of the bus algorithm, the efficiency of the peer discovery algorithm, and the performance of the SSN system under varying load conditions.