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End users get large information through the web. The search engine which allows increasing the information retrieval accuracy exploited a key content of semantic web resources of concepts and relations. Tremendous information gets poured in to the web pages for every query raised by the end user. Most of the pages are query related and fulfill the intention of the end users. There are some pages with useless information also. The web architecture represented by the semantic web provides the layered architecture which possibly overcomes this limitation. Search engines which usually allow increasing the information retrieval accuracy by exploiting a key content of semantic web resources, that is the relations that are existing in the query. There is a huge repository which makes very difficult for the end users to explore to have an easy catch up of the required concept many modifications are being done. This paper we deal with the page rank algorithm to be used in conjunction with the semantic web search engines that usually extracted from the user queries and on annotated resources. Page Relevance Score is measured as the probability that a retrieved resource actually contains those relations existence was assumed by the user at the time of query definition.

Semantic Web database is an RDF database due to increased use of Semantic Web in real life applications; one can find heavy growth in RDF database. As there is a tremendous increase in RDF data, performance and scalability issues are of main concern. This chapter discusses improving and scaling up query performance for increasingly growing Semantic Web. It discusses current Semantic Web data storage techniques, which have been found to scale poorly and have poor query performance. It discusses the partitioning techniques vertical and horizontal partitioning to improve query performance. To further improve the query performance, along with these partitioning techniques, various compression techniques can also be used. Relational data offers faster execution of queries as compared to RDF data. To demonstrate these ideas, semantic data is converted to relational data and then query performance improvement techniques are applied. The scaling up of Semantic Web data is also discussed.

although the tendency towards a Semantic Web is increasing, the current Web is keen on more semantic data. There are several web application domains which need to present their data in the form of graphs to get information and knowledge to allow semantic web searching to deal with them. These domains can be for example geographic data, data in the area of bioinformatics, social networks or even corporate search engines. Most of these domains already store their data in relational database management systems which are not capable of being queried directly by semantic web applications. In this paper we will propose a semi-automated method to map the data from relational databases to RDF structures. This is true for both a single source database as well as different source databases. The results show a complexity of order n2 where n is the number of records processed, when it comes to the performance in creating the output from mapping the relational database to RDF files.

PurposeThe purpose of this paper is to explore useful Semantic Web technologies and ontology-based applications for digital libraries. It also investigates the perceptions of university librarians and academicians in Pakistan about Semantic Web technologies and their use in digital libraries.Design/methodology/approachAn exploratory research design based on Delphi research strategy was conducted to answer the research questions. Interviews were conducted with a purposive sample of 50 key informants including university librarians and academicians to explore their perceptions about Semantic Web technologies and their use in digital libraries. Thematic analysis of interview data was conducted to obtain results.FindingsThe results of this paper showed that DuraCloud, Semantic information mashup, OntoEdit and resource description framework (RDF) are the various Semantic Web applications which are useful for digital libraries to develop semantic relationships among digital contents and increase their accessibility in the web environment. Findings revealed that Semantic Web provides precise results and meets user information needs in an effective way. Results also showed that next-generation digital libraries use context-awareness technology, intelligent agent software and detecting sensors to analyze user information needs and provide dynamic information services. This paper recommended that librarians should embrace the use of emerging web technologies in libraries and offer library services through the medium of the web.Practical implicationsThis paper envisaged the future of digital library services and Semantic Web applications that can be used to re-structure metadata of digital library. This paper has practical implications for librarians to consider the useful applications of Semantic Web for digital library and enhance the interoperability of metadata among heterogeneous information systems. Practically, results obtained from this paper are highly useful for library schools and LIS teachers to up-date their curriculum by incorporating new contents related to web languages and Semantic Web applications for digital libraries.Originality/valueThis paper identifies various Semantic Web applications which are useful for developing Semantic Digital Libraries.

Semantic web works on producing machine readable data and aims to deal with large amount of data. The most important tool to access the data which exist in web is the search engine. Traditional search engines are insufficient in the face of the amount of data that consists in the existing web pages. Semantic search engines are extensions to traditional engines and overcome the difficulties faced by them. This paper summarizes semantic web, concept of traditional and semantic search engines and infrastructure. Also semantic search approaches are detailed. A summary of the literature is provided by touching on the trends. In this respect, type of applications and the areas worked for are considered. Based on the data for two different years, trend on these points are analyzed and impacts of changes are discussed. It shows that evaluation on the semantic web continues and new applications and areas are also emerging. Multimedia retrieval is a newly scope of semantic. Hence, multimedia retrieval approaches are discussed. Text and multimedia retrieval is analyzed within semantic search.

Googling for Health Information

Due to the problems (poor precision, poor recall etc) associated with current keyword based search engines, a number of semantic search architectures have been proposed in near past. These semantic approaches though effective in returning better and more relevant search results, suffer from a number of drawbacks when it comes to scaling their implementation to a full web search. We have identified a number of limitations associated with semantic search; like lack of semantics for the web documents, domain limited search, search limited to the semantic parts of the web, lack of a unified ontology knowledge base etc. Through our work, we put forward a search-architecture for today’s web (partly semantic and mostly non semantic information). It addresses the fundamental issues of ontology standardization by introducing the notion of semantic annotations for ontologies using a global ontology. The proposed model combines the content based (keyword) and context based (semantic) search approaches thus taking into account the present (mostly non semantic data) and the future (semantic information).

Most people are more or less related to the web by participating in a kind of social networking site. Semantic Web technology plays a crucial role in these sites as they contain an enormous amount of data about ‎persons, pages, events, places, corporations, etc. This research is a Semantic Web application designed to create a new ‎semantic social community called Socialpedia. It links the already existing social public information to the newly ‎public ones. This information is linked with different information on the web to construct a new immense ‎data container. The resulting data container can be processed using a variety of Semantic Web techniques to produce ‎machine-understandable content. This content shows the promise of using integrated data to improve Web search and ‎Web-scale data analysis, unlike conventional search engines or social ones. This community involves obtaining data ‎from traditional users known as contributors or participants, linking data from existing social networks, extracting ‎structured data in triples using predefined ontologies, and finally querying and inferring such data to obtain ‎meaningful pieces of information. Socailpedia supports all popular functionalities of social networking websites ‎besides the enhanced features of the Semantic Web, providing advanced semantic search that acts as a semantic ‎search engine.

Semantic data has semantic information about the relationship between information and resources of data collected in a smart city so that all different domains and data can be organically connected. Various services using semantic data such as public data integration of smart cities, semantic search, and linked open data are emerging, and services that open and freely use semantic data are also increasing. By using semantic data, it is possible to create a variety of services regardless of platform and resource characteristics. However, despite the many advantages of semantic data, it is not easy to use because it requires a high understanding of semantics such as SPARQL. Therefore, in this paper, we propose a semantic framework for users of semantic data so that new services can be created without a high understanding of semantics. The semantics framework includes a template-based annotator that supports automatically generating semantic data based on user input and a semantic REST API that allows you to utilize semantic data without understanding SPAROL.

A Semantic Web of Things (SWoT) brings together the Semantic Web and the Web of Things (WoT), associating semantically annotated information to web-enabled physical devices, services and their data, towards seamless data integration and better understanding of real-world information. A missing element in order to realize SWoT is a standardized, scalable and flexible way to globally discover in (near) real time web-connected embedded devices, as well as their semantic data. To address this gap, we propose WOT Semantic Search Engine (WOTS2E), which is a search engine for the SWoT, based on web crawling, being able to discover Linked Data endpoints and, through them, WoT-enabled devices and their services. In this paper, we describe the design, development and implementation of WOTS2E, as well as an evaluation procedure showing its operation and performance across the web.

As semantic web technologies are increasingly used to empower knowledge management systems (KMSs), there is a growing need for mechanisms and automated tools for checking content generated by semantic-web tools. The content in a KMS includes both the knowledge management (KM) schema and the data contained within. KM schemas can be viewed as ontologies and the data contained within can be viewed as instance data. Thus we can apply semantic web ontology and instance data processing techniques and tools in KM settings. There are many semantic web tools aimed at ontology evaluation, however there is little, if any, research focusing on instance data evaluation. Although instance data evaluation has many issues in common with ontology evaluation, there are some issues that are either more prominent in or unique to instance data evaluation. Instance data often accounts for orders of magnitude more data than ontology data in organization intranets, thus our work focuses on evaluation techniques that help users of KMSs to determine when certain instance data is ready for use. We present our work on semantic web instance data evaluation for KMSs. We define the instance data evaluation research problem and design a general evaluation process GEP. We identify three categories of issues that may occur in instance data: syntax errors, logical inconsistencies, and potential issues. For each category of issues, we provide illustrative examples, describe the symptoms, analyze the causes, and present our detection solution. We implement our design in TW OIE which is an online instance data evaluation service. We perform experiments that show that the TW OIE is more comprehensive than most existing online semantic web data evaluators.

The quality of data is a critical factor for all kinds of decision-making and transaction processing. While there has been a lot of research on data quality in the past two decades, the topic has not yet received sufficient attention from the Semantic Web community. In this paper, we discuss (1) the data quality issues related to the growing amount of data available on the Semantic Web, (2) how data quality problems can be handled within the Semantic Web technology framework, namely using SPARQL on RDF representations, and (3) how Semantic Web reference data, e.g. from DBPedia, can be used to spot incorrect literal values and functional dependency violations. We show how this approach can be used for data quality management of public Semantic Web data and data stored in relational databases in closed settings alike. As part of our work, we developed generic SPARQL queries to identify (1) missing datatype properties or literal values, (2) illegal values, and (3) functional dependency violations. We argue that using Semantic Web datasets reduces the effort for data quality management substantially. As a use-case, we employ Geonames, a publicly available Semantic Web resource for geographical data, as a trusted reference for managing the quality of other data sources.

Websites increasingly embed semantic data for search engine optimization. The most common ontology for semantic data, schema.org, is supported by all major search engines and describes over 500 data types, including calendar events, recipes, products, and TV shows. As of today, users wishing to pass this data to their favorite applications, e.g., their calendars, cookbooks, price comparison applications or even smart devices such as TV receivers, rely on cumbersome and error-prone workarounds such as reentering the data or a series of copy and paste operations. In this paper, we present Semantic Data Mediator (SDM), an approach that allows the easy transfer of semantic data to a multitude of services, ranging from web services to applications installed on different devices. SDM extracts semantic data from the currently displayed web page on the client-side, offers suitable services to the user, and by the press of a button, forwards this data to the desired service while doing all the necessary data conversion and service interface adaptation in between. To realize this, we built a reusable repository of service descriptions, data converters, and service adapters, which can be extended by the crowd. Our approach for linking services to websites relies solely on semantic data and does not require any additional support by either website or service developers. We have fully implemented our approach and present a real-world case study demonstrating its feasibility and usefulness.

Explosive growth of the Web has made searching Web data a challenging task for information retrieval systems. Semantic search systems that go beyond the shallow keyword matching approaches and map words to their conceptual meaning representations offer better results to the users. On the other hand, a lot of representation formats have been specified to represent Web data into a semantic format. We propose a search engine for searching Web data represented in UNL (Universal Networking Language). UNL has numerous attractive features to support semantic search. One of the main features is that UNL does not depend on domain ontology. Our proposed search engine is based on semantic graph matching. It includes semantic expansion for graph nodes and relation matching based on relation meaning. The search results are ranked depending on the semantic similarity between the user query and the retrieved documents. We developed a prototype implementing the proposed semantic search engine, and our evaluations demonstrate its effectiveness across a wide-range of semantic search tasks.

The exponential growth of information on the web far exceeds the capacity of present day information retrieval systems and search engines, making information integration on the web difficult. In order to overcome this, semantic web technologies were proposed by the World Wide Web Consortium (W3C) to achieve a higher degree of automation and precision in information retrieval systems. Semantic web, with its promise to deliver machine understanding to the traditional web, has attracted a significant amount of research from academia as well as from industries. Semantic web is an extension of the current web in which data can be shared and reused across the internet. RDF and ontology are two essential components of the semantic web architecture which support a common framework for data storage and representation of data semantics, respectively. Ontologies being the backbone of semantic web applications, it is more relevant to study various approaches in their application, usage, and integration into web services. In this article, an effort has been made to review the research work being undertaken in the area of design and development of ontology supported information systems. This paper also briefly explains the emerging semantic web technologies and standards.