A semantic framework for ontology usage analysis

The Semantic Web envisions a Web where information is accessible and processable by computers as well as humans. Ontologies are the cornerstones for realizing this vision of the Semantic Web by capturing domain knowledge by defining the terms and the relationship between these terms to provide a formal representation of the domain with machine-understandable semantics. Ontologies are used for semantic annotation, data interoperability and knowledge assimilation and dissemination.In the literature, different approaches have been proposed to build and evolve ontologies, but in addition to these, one more important concept needs to be considered in the ontology lifecycle, that is, its usage. Measuring the “usage” of ontologies will help us to effectively and efficiently make use of semantically annotated structured data published on the Web (formalized knowledge published on the Web), improve the state of ontology adoption and reusability, provide a usage-based feedback loop to the ontology maintenance process for a pragmatic conceptual model update, and source information accurately and automatically which can then be utilized in the other different areas of the ontology lifecycle. Ontology Usage Analysis is the area which evaluates, measures and analyses the use of ontologies on the Web. However, in spite of its importance, no formal approach is present in the literature which focuses on measuring the use of ontologies on the Web. This is in contrast to the approaches proposed in the literature on the other concepts of the ontology lifecycle, such as ontology development, ontology evaluation and ontology evolution. So, to address this gap, this thesis is an effort in such a direction to assess, analyse and represent the use of ontologies on the Web.In order to address the problem and realize the abovementioned benefits, an Ontology Usage Analysis Framework (OUSAF) is presented. The OUSAF Framework implements a methodological approach which is comprised of identification, investigation, representation and utilization phases. These phases provide a complete solution for usage analysis by allowing users to identify the key ontologies, and investigate, represent and utilize usage analysis results. Various computation components with several methods, techniques, and metrics for each phase are presented and evaluated using the Semantic Web data crawled from the Web. For the dissemination of ontology-usage-related information accessible to machines and humans, The U Ontology is presented to formalize the conceptual model of the ontology usage domain. The evaluation of the framework, solution components, methods, and a formalized conceptual model is presented, indicating the usefulness of the overall proposed solution

Domain ontology usage analysis framework

The Semantic Web (also known as Web of Data) is growing fast and becoming a decentralized knowledge platform for publishing and sharing information. The web ontologies promote the establishment of a shared understanding between data providers and data consumers, allowing for automated information processing and effective and efficient information retrieval. The majority of existing research efforts is focused around ontology engineering, ontology evaluation and ontology evolution. This work goes a step further and evaluates theontology usage. In this paper, we present an Ontology Usage Analysis Framework (OUSAF) and a set of metrics used to measure the ontology usage. The implementation of the proposed framework is illustrated using the example of GoodRelations ontology (GRO). GRO has been well adopted by the semantic ecommerce community, and the OUSAF approach has been used to analyse GRO usage in the dataset comprised of RDF data collected from the web

Survey of Service Description Languages and Their Issues in Cloud Computing

Along with the growing popularity of cloud computing technology, the amount of available cloud services and their usage frequency are increasing. In order to provide a mechanism for the efficient enforcement of service-relevant operations in cloud environment, such as service discovery, service provision, and service management, a completed and precise service specification model is highly required. In this paper, we conducted a survey on existing service description languages applied in three different domains - general services, Web/SOA services, and cloud services. We discussed and compared the past literature from seven major aspects, which are: (1) domain, (2) coverage, (3) purpose, (4) representation, (5) semantic expressivity, (6) intended users, and (7) features. Additionally, two core dimensions semantic expressivity and coverage are employed to categorize and analyse the key service description languages by using Magic Quadrant methodology. These two dimensions are regarded as the most essential factors for the evaluation of a service description model. Based on this analysis, we concluded that Unified Service Description Language (USDL) is the language with the widest coverage from business, technical and operational aspects, while OWL-S is the one that has the highest semantic expressivity. At last, critical research issues on cloud service description languages are identified and analysed. The solution of these issues requires more research efforts on the standardization of cloud service specification, which will eventually enhance the development of cloud industry

Open ebusiness ontology usage: investigating community implementation of goodrelations

The GoodRelations Ontology is experiencing the first stages of mainstream adoption, with its appeal to a range of enterprises as the eCommerce ontology of choice to promote its offerings and product catalogue. As adoption increases, so too does the need to critically review and analyze current implementation of the ontology to better assist future usage and uptake. To comprehensively understand the implementation approaches, usage patterns, instance data and model coverage, data was collected from 105 different web based sources that have published their business and product-related information using the GoodRelations Ontology. This paper analyses the ontology usage in terms of data instantiation, and conceptual coverage using a SPARQL queries to evaluate quality, usefulness and inference provisioning. Experimental results highlight that early publishers of structured eCommerce data benefit more due to structured data being more readily search engine indexable, but the lack of available product ontologies and product master datasheets is impeding the creation of a semantically interlinked eCommerce Web

Biotechnology: a powerful tool for the removal of cadmium from aquatic systems

The prime objective of the present research work was to evaluate the efficiency of bio-machine for the removal of Cadmium (Cd) from aquatic systems. Aspergillus niger fungus was used as bio-machine to remove Cd from aquatic systems. Twenty three different strains (IIB-1 to IIB-23) were isolated from industrial effluents and the Langmuir and Freundlich models were applied to the best Cadmium removal strain IIB-23 in order to obtain the adsorption parameters. Different parameters such as pH, temperatur e, contact time, initial metal concentratio, and biomass dosage on the biosorption of Cd were studied. The percent removal of Cd initially increased with an increase in pH ranging from 5.5-6.5 and then decreased by increasing pH from 7.0-7.5. An optimized pH used for Cd removal from aquatic systems was found to be 6.5. Additionally, an optimum amount of biomass was 1.33 g for the maximum removal of Cd from the aqueous solutions with initial metal concentration of 75 mg/L. The results obtained thus indicated that Langmuir model is the best suited for the removal of Cd from aquatic systems

Clinical Profile of Mortality and Treatment Profile of Survival in Patients with COVID-19 Pneumonia Admitted to Dubai Hospital

Background: Most COVID-19 studies conclude old age and coexisting illnesses as mortality determinants owing to different populations or methodologies, or omitting factors affecting outcomes. Methods: We analyzed COVID-19 patients’ data (N = 391) of Dubai Hospital between January 1, 2020 and June 30, 2020. Results: Only 19 patients (4.8%) were UAE nationals, while 372 (95.2%) were expatriates. Median age was 48 (interquartile range, 40–56) years; 22% were <40 years, and only 16.6% were female. Cough was the most common symptom (78.7%), fever was 77.4%, and gastrointestinal symptoms were least common (13.8%). Approximately 95% had elevated C-reactive protein (CRP) and D-dimers (79%), lymphocytopenia 47.3%, and thrombocytopenia 13.8%. Mortality was 30% for the total sample and 50% in ICU patients. ICU patients were older than non-ICU (age; 49.6 ± 10.9 vs. 46.7 ± 12.7 years, p = 0.04). Eighty-five percent of ICU patients required invasive mechanical ventilation, 78% vasopressors, 88% sedation, 84% muscle paralysis, while none require any of these in the medical group. Survivors had fewer patients with sedatives (p = 0.01). The median length of stay in the hospital was 19 days, ICU stays 14 days, and ventilator 11 days. The Mann-Whitney test showed that survivors spent more days in the ICU (median [IQR] 18 [6.5–29.5] vs. 11 [4–18], p value 0.003) and the hospital (32 [14.5–49.5] vs. 14 [7–21], p value 0.001) than nonsurvivors. Ferritin and D-dimers were higher in nonsurvivors, but CRP was lower in nonsurvivors (ferritin (ng/mL) median (IQR) 1,434 (661.5–2206.5) versus 1,362 (630–2,094), p value = 0.017, CRP (mg/L) 118.7 (53.4–184) versus 134.9 (66.5–203.2), p value 0.001 and D-dimer (µg/mL) 1.54 (0–3.13) versus 1.09 (0–2.51), p value = 0.001). Multiple logistic regression analysis determined age, fever on admission, use of oxygen, mechanical ventilation, and steroids as predictors of survival. Conclusions: COVID-19 patients were young males with pre-existing conditions. Ferritin, CRP, and D-dimers were higher in nonsurvivors. Treatment with chloroquine, antivirals, and anticoagulation was not different between survivors and nonsurvivors. Steroid use was a survival predictor

Integrating Financial Data Using Semantic Web for Improved Visibility

The Semantic Web technologies and Linked Data principles enable information integration and datainteroperability at syntactic and semantic levels. This will lead to having a large amount of integrated data with increased visibility leading to the discovery and synthesis of knowledge to address financial issues. In this paper, we illustrate the implementation of the Financial Linked Data using the Linked Data principle within the financial domain. The use of XBRL (XML-based business reporting language)by various companies to generate their financial data has enabled us to obtain a standardized and uniform body of financial data. We utilize this format to link business facts to corresponding reports and then publish these data using Linked Data principles. Furthermore, financial data can be interlinked with other relevant datasets by creating semantic relationships. We believe that such linkage between various datasets represents an important step in achieving data transparency which in turn establishes a basis for making profitable decisions in the future

Genetic algorithm with adaptive and dynamic penalty functions for the selection of cleaner production measures: A constrained optimization problem

This paper presents a new approach of genetic algorithm (GA) to solve the constrained optimization problem. In a constrained optimization problem, feasible and infeasible regions occupy the search space. The infeasible regions consist of the solutions that violate the constraint. Oftentimes classical genetic operators generate infeasible or invalid chromosomes. This situation takes a turn for the worse when infeasible chromosomes alone occupy the whole population. To address this problem, dynamic and adaptive penalty functions are proposed for the GA search process. This is a novel strategy because it will attempt to transform the constrained problem into an unconstrained problem by penalizing the GA fitness function dynamically and adaptively. New equations describing these functions are presented and tested. The effects of the proposed functions developed have been investigated and tested using different GA parameters such as mutation and crossover. Comparisons of the performance of the proposed adaptive and dynamic penalty functions with traditional static penalty functions are presented. The result from the experiments show that the proposed functions developed are more accurate, efficient, robust and easy to implement. The algorithms developed in this research can be applied to evaluate environmental impacts from process operations. © Springer-Verlag 2006

A framework for measuring ontology usage on the web

A decade-long conscious effort by the Semantic Web community has resulted in the formation of a decentralized knowledge platform which enables data interoperability at a syntactic and semantic level. For information interoperability, at a syntactic level, RDF provides the standard format for publishing data and RDFS gives structure to the information. For semantic-level interoperability, ontologies are used which allow information dissemination and assimilation among diverse applications and systems; where information is equally accessible and useful to humans and machines. The success of the linked open data project, recognition of explicit semantics (annotated through web ontologies) by search engines and the realized potential advantages of semantic data for publishers have resulted in tremendous growth in the use of web ontologies on the web. In order to promote the adoption of ontologies (to new users), reusability of adopted ontologies, effective and efficient utilization on ontological knowledge and evolving the ontological model, erudite insight on the usage of ontologies is imperative. While ontology evaluation attempts to evaluate a developed ontology to assess its fitness and quality, it does not provide any insight into how ontologies are being used and what is the state of prevalent knowledge patterns. Realizing the importance of measuring and analysing ontology usage to advance the adoption, reusability and exploitation of ontologies, we present a semantic framework for measuring and analysing ontology usage on the Web on empirical grounding.Our methodological approach is discussed to highlight the detail and role of each step. A framework is presented along with the set of metrics developed to measure ontology usage from different aspects such as ontology richness, usage and incentives to provide a holistic view on the state of ontology usage. The framework is then evaluated using an important use-case scenario to identify the prevalent knowledge patterns in order to rank the terminological knowledge for annotating the information