Jamshaid Ashraf

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 domainsgeneral 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.

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.

Making sense from Big RDF Data: OUSAF for measuring ontology usage

Recent growth and advancements in the Semantic Web have shifted the research focus from being knowledge‐centered to data‐centered. This has led to the increased use of ontologies to structurally represent the data, thereby generating huge amounts of RDF data, which we term Big RDF Data. Nevertheless, the literature lacks the tools to analyze Big RDF Data and make sense of it. Access to such tools would enable pragmatic inputs and insights for users in respect of such tasks as the usage and adoption of Ontologies, their uptake by different users in the community, and the identification of prevalent patterns. This analysis, which we term Ontology Usage, is important from the viewpoint of users who need informed inputs in the various stages of the ontology engineering lifecycle, such as ontology evolution, ontology population, and ontology deployment. In this paper, we propose the Ontology USage Analysis F̌ramework (OUSAF), which performs analysis of Ontology Usage on Big RDF Data and synthesizes the usage knowledge acquired. OUSAF provides a methodological approach to performing the various phases such as identifying, analyzing, representing, and utilizing the Ontology usage results from Big RDF Data. We describe in detail each of those phases and the metrics required to perform the analysis of each phase. The utilization of the OUSAF results obtained by users such as data publishers and ontology developers is demonstrated by a dataset collected in the e‐business domain. Copyright

Ontology usage analysis in the ontology lifecycle

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 through the defined terms and the relationship between them to provide a formal representation of the domain with machine-understandable semantics. Given the importance of ontologies, a significant amount of work in the literature has been done on knowledge representation on the Web which includes ontology development (ontology engineering), ontology evaluation, ontology population and ontology evolution. As a result, numerous domain ontologies have been developed however, not much attention has been given to the area of ontology usage analysis that shows how the developed ontologies are being used. Such a study is very important as we explain in this paper by using some motivational scenarios of a Semantic Web user in different roles. The discussion is followed by a summary of the state-of-the-art of the existing literature on ontology usage to highlight gaps in this area. We define Ontology Usage Analysis and discuss our proposed Ontology Usage Analysis Framework (OUSAF) to measure the usage of an ontology on the web from different perspectives. On a real-world collected dataset, the results obtained from OUSAF demonstrate the practical utilization of OUSAF in measuring ontology usage analysis.

Empirical analysis of domain ontology usage on the Web: eCommerce domain in focus

In the recent past, there has been an exponential growth in Resource Description Framework data on the web known as web of data. The emergence of the web of data is transforming the existing web from a document‐sharing medium to a decentralized knowledge platform for publishing and sharing information between humans and computers. To enable common understanding between different users, domain ontologies are being developed and deployed to annotate information on the web. This semantically annotated information is then accessed by machines to extract and aggregate information, on the basis of the underlying ontologies used. To effectively and efficiently access data on the web, insight into the usage of ontology is pivotal, because this assists users in experiencing the benefits offered by the Semantic Web. However, such an approach has not been proposed in the literature. In this paper, we present a pragmatic approach to the analysis of domain ontology usage on the web. We propose metrics to measure the use of domain ontology constructs on the web from different aspects. To comprehensively understand the usage patterns of conceptual knowledge, instance data, and ontology co‐usability, we considered GoodRelations ontology as the domain ontology and built a dataset by collecting structured data from 211 web‐based data sources that have published information using the domain ontology. The dataset is analyzed by using the proposed metrics and observations along with their usability and applicability to the different users of the Semantic Web. Copyright

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 the ontology 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 Good Relations 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.

Ontology Usage Network Analysis Framework

Recently, there is tremendous growth in the use of ontologies to publish semantically rich structured data on the Web. In order to understand the adoption and uptake of ontologies in real world setting, it is important to analyse the ontology usage. In this paper, we propose Ontology Usage Network Analysis Framework (OUN-AF) which models the ontology usage by different data publishers in the form of affiliation network. Metrics are defined to measure the ontology usage, their co-usability and the cohesive subgroups emerging from the dataset.

Integrating Financial Data Using Semantic Web for Improved Visibility

The Semantic Web technologies and Linked Data principles enable information integration and data interoperability 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.

A framework for ontology usage analysis

The Semantic Web (also known as the Web of Data) is growing rapidly and becoming a decentralized social and knowledge platform for publishing and sharing information. In the early days of the Semantic Web (1999-2006), research efforts of the community were centered around knowledge representation; thus, most of the research work was focused on building ontologies (ontology engineering), developing formal languages to represent them (ontology language), methodologies to evaluate and evolve ontologies (ontology evaluation and evolution (OE)), and logic for reasoning with them. As a result of this, even though ontologies were being developed but their instantiation was inadequate to provide the actual instance data needed for the evaluation and analysis of the developed ontologies. In order to overcome this issue, test data was often used to perform the above tasks [1]. However, in the recent past, the focus has shifted towards publishing data either with little or no use of ontologies [2]. This shift in focus is credited to the Linked Open Data (LOD) Project which has published billions of assertions on the Web using well known Linked Data principles. Because of this, the research focus has shifted from knowledge-centered to data-centered and is now settling down at the point where domain ontologies are being used to publish real-world data on the Web. This trend promotes consistent and coherent semantic interoperability between users, systems and applications. In this regard, several domain ontologies have been developed to describe the information pertaining to different domains such as Healthcare and Life Science (HCLS), governments, social spaces, libraries, entertainment, financial service and eCommerce.

Utilization Phase: Utilization of OUSAF

To demonstrate the utilization of the OUSAF, in this chapter, a methodological approach is adopted which provides a systematic flow of activities and the interaction between different components to analyse the utilization. This methodological approach is presented in Sect. 8.2. Section 8.3 presents details on the construction of the dataset that is used to demonstrate the utilization phase. In Sects. 8.4–8.6, the utilization of the different phases of the OUSAF is presented. Section 8.7 summarizes the achievements of the utilization phase. Section 8.8 concludes the chapter.