A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The process of adding semantic metadata to web resources is called Semantic Annotation. There are many obstacles against the Semantic Annotation, such as multilinguality, scalability, and issues which are related to diversity and inconsistency in content of different web pages. Due to the wide range of domains and the dynamic environments that the Semantic Annotation systems must be performed on, the problem of automating annotation process is one of the significant challenges in this domain. To overcome this problem, different machine learning approaches such as supervised learning, unsupervised learning and more recent ones like, semi-supervised learning and active learning have been utilized. In this paper we present an inclusive layered classification of Semantic Annotation challenges and discuss the most important issues in this field. Also, we review and analyze machine learning applications for solving semantic annotation problems. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a framework that can map machine learning techniques into the Semantic Annotation challenges and requirements

Dynamic Discovery of Type Classes and Relations in Semantic Web Data

The continuing development of Semantic Web technologies and the increasing user adoption in the recent years have accelerated the progress incorporating explicit semantics with data on the Web. With the rapidly growing RDF (Resource Description Framework) data on the Semantic Web, processing large semantic graph data have become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method

Investigating the use of semantic technologies in spatial mapping applications

Semantic Web Technologies are ideally suited to build context-aware information retrieval applications. However, the geospatial aspect of context awareness presents unique challenges such as the semantic modelling of geographical references for efficient handling of spatial queries, the reconciliation of the heterogeneity at the semantic and geo-representation levels, maintaining the quality of service and scalability of communicating, and the efficient rendering of the spatial queries' results. In this paper, we describe the modelling decisions taken to solve these challenges by analysing our implementation of an intelligent planning and recommendation tool that provides location-aware advice for a specific application domain. This paper contributes to the methodology of integrating heterogeneous geo-referenced data into semantic knowledgebases, and also proposes mechanisms for efficient spatial interrogation of the semantic knowledgebase and optimising the rendering of the dynamically retrieved context-relevant information on a web frontend

Semantic Web Business Applications- A Scalability Model for the New Digital Economy

Semantic web technologies are considered to be the next wave for web technologies related with rich internet web applications, content management, and document and information management. The most promising semantic web applications for business domain are considered to be the semantic web business portals which integrate diverse business information. Because semantic web applications are working with ontologies or data vocabularies there is a need to permanently assure the links between publicly available vocabularies on the web disposed at different addresses and diverse information which comes from different web sources. This means for semantic web business applications a scalability problem. The present paper discusses the architecture of semantic web business application useful for assuring the scalability. We discuss the scalability problem in terms of data access and information retrieval. We conduct a series of experiments in order to test the scalability problems. Finally a so called scalability model is proposed. The main contributions of the present paper consist in presenting the main problems that a semantic web business application presents in terms of scalability. We also contribute to the semantic web business applications field by presenting a framework to measure scalability

Semantic Web Business Applications- A Scalability Model for the New Digital Economy

Semantic web technologies are considered to be the next wave for web technologies related with rich internet web applications, content management, and document and information management. The most promising semantic web applications for business domain are considered to be the semantic web business portals which integrate diverse business information. Because semantic web applications are working with ontologies or data vocabularies there is a need to permanently assure the links between publicly available vocabularies on the web disposed at different addresses and diverse information which comes from different web sources. This means for semantic web business applications a scalability problem. The present paper discusses the architecture of semantic web business application useful for assuring the scalability. We discuss the scalability problem in terms of data access and information retrieval. We conduct a series of experiments in order to test the scalability problems. Finally a so called scalability model is proposed. The main contributions of the present paper consist in presenting the main problems that a semantic web business application presents in terms of scalability. We also contribute to the semantic web business applications field by presenting a framework to measure scalability

Scalable RDF Data Compression using X10

The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of Semantic Web applications that perform computations over large volumes of information. A typical method for alleviating the impact of this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding for this purpose is particularly prevalent in Semantic Web database systems. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we describe an encoding implementation based on the asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art MapReduce algorithm, we demonstrate a speedup of 2.6-7.4x and excellent scalability. These results illustrate the strong potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of larger scale Semantic Web applications

The six challenges of the Semantic Web

The Semantic Web has attracted a diverse, but significant, community of researchers, institutes and companies, all sharing the belief that one day the Semantic Web will have as big an impact on life as currently the WWW/Internet has. We share that vision, based on the ever-increasing need to reduce information overload, and to increase task delegation to software agents. However, there is still a long way to go before the Semantic Web dream comes true. In this paper, we identify some of the major challenges the community faces in the coming years, and we outline solution directions. The major challenges concern: (i) the availability of content, (ii) ontology availability, development and evolution, (iii) scalability, (iv) multilinguality, (v) visualization to reduce information overload, and (vi) stability of Semantic Web languages. We will also say some words on the economic impact of the Semantic Web