Web Semantics for Personalized Information Retrieval

This chapter explores the synergy between Semantic Web (SW) technologies and Web Personalization (WP) for demonstrating an intelligent interface for Personalized Information Retrieval (PIR) on web. Benefits of adding semantics to WP through ontologies and Software Agents (SA) has already been realized. These approaches are expected to prove useful in handling the information overload problem encountered in web search. A brief introduction to PIR process is given, followed by description of SW, ontologies and SA. A comprehensive review of existing web technologies for PIR has been presented. Although, a huge contribution by various researchers has been seen and analyzed but still there exist some gap areas where the benefits of these technologies are still to be realized in future personalized web search.

Technique for Transformation of Data From RDB to XML Then to RDF

This chapter will clearly show the need for better mapping techniques for Relational Database (RDB) all the way to Resource Description Framework (RDF). This includes coverage of each data model limitations and benefits for getting better results. Here, each form of data being transform has its own importance in the field of data science. As RDB is well known back end storage for information used to many kinds of applications; especially the web, desktop, remote, embedded, and network-based applications. Whereas, EXtensible Markup Language (XML) in the well-known standard for data for transferring among all computer related resources regardless of their type, shape, place, capability and capacity due to its form is in application understandable form. Finally, semantically enriched and simple of available in Semantic Web is RDF. This comes handy when with the use of linked data to get intelligent inference better and efficient. Multiple Algorithms are built to support this system experiments and proving its true nature of the study.

Semantic Web-Based Framework for Scientific Workflows in E-Science

In the future generation, computer science plays prominent role in the scientific research. The development in the field of computers will leads to the research benefits of scientific community for sharing data, service computing, building the frameworks and many more. E-Science is the active extending field in the world by the increase data and tools. The proposed work discusses the use of semantic web applications for identifying the components in the development of scientific workflows. The main objective of the proposed work is to develop the framework which assists the scientific community to test and deploy the scientific experiments with the help of ontologies, service repositories, web services and scientific workflows. The framework which aims to sustenance the scientific results and management of applications related to the specific domain. The overall goal of this research is to automate the use of semantic web services, generate the workflows, manage the search services, manage the ontologies by considering the web service composition.

Information Retrieval Models

Information retrieval is currently an active research field with the evolution of World wide web. The objective of this chapter is to provide an insight into the information retrieval definitions, process, models. Further how traditional information retrieval has evolved and adapted for search engines is also discussed. The information retrieval models have not only been used for search purpose it also supports cross lingual translation and retrieval tasks. This chapter also outlines the CLIR process in a brief manner. The tools which are usually used for experimental and research purpose is also discussed. This chapter is organized as Introduction to the concepts of information retrieval. Description of the information retrieval process, the information retrieval models, the role of external sources like ontologies in information retrieval systems. Finally the chapter provides an overview of CLIR and the tools used in developing IR systems is mentioned. Further the latest research directions in IR is explained.

Scope of Automation in Semantics-Driven Multimedia Information Retrieval From Web

Present digital information driven society is a part of Semantic Web, where focus is on returning relevant information to the users, in response of their searches. Research community had been doing great efforts to associate semantics with textual information since early 2000. However, there had been tremendous growth in capturing, sharing, storing and retrieving photographs and multimedia contents on the web in last one decade. This has drawn attention of research community for embedding semantics with multimedia contents while storing, so as to lead efficient retrieval of these contents later on. This chapter focuses on presenting need of associating semantics with images, initially various techniques of image retrieval are elaborated. Existing techniques of embedding semantics in images are analyzed, further scope of automation in associating semantics with images is explored considering software agent technology as instrument.

State-of-the-Art Information Retrieval Tools for Biological Resources

With the advancements in sequencing technologies, there is an exponential growth in the availability of the biological databases. Biological databases consist of information and knowledge collected from scientific experiments, published literature and statistical analysis of text, numerical, image and video data. These databases are widely spread across the globe and are being maintained by many organizations. A number of tools have been developed to retrieve the information from these databases. Most of these tools are available on web but are scattered. So, finding a relevant information is a very difficult, and tedious task for the researchers. Moreover, many of these databases use disparate storage formats but are linked to each other. So, an important issue concerning present biological resources is their availability and integration at single platform. This chapter provides an insight into existing biological resources with an aim to provide consolidated information at one place for ease of use and access by researchers, academicians and students.

Knowledge Discovery and Big Data Analytics

The era of big data has come with the ability to process massive datasets from heterogeneous sources in real-time. But the conventional analytics can't be able to manage such a large amount of varied data. The main issue that is being asked is how to design a high-performance computing platform to effectively carry out analytics on big data and how to develop a right mining scheme to get useful insights from voluminous big data. Hence this chapter elaborates these challenges with a brief introduction on traditional data analytics followed by mining algorithms that are suitable for emerging big data analytics. Subsequently, other issues and future scope are also presented to enhance capabilities of big data.

Knowledge Discovery From Massive Data Streams

T.S. Eliot once wrote some beautiful poetic lines including one “Where is the knowledge we have lost in information?”. Can't say that T.S. Eliot could have anticipated today's scenario which is emerging from his poetic lines. Data in present scenario is a profuse resource in many circumstances and is piling-up and many technical leaders are finding themselves drowning in data. Through this big stream of data there is a vast flood of information coming out and seemingly crossing manageable boundaries. As Information is a necessary channel for educing and constructing knowledge, one can assume the importance of generating new and comprehensive knowledge discovery tools and techniques for digging this overflowing sea of information to create explicit knowledge. This chapter describes traditional as well as modern research techniques towards knowledge discovery from massive data streams. These techniques have been effectively applied not exclusively to completely structured but also to semi-structured and unstructured data. At the same time Semantic Web technologies in today's perspective require many of them to deal with all sorts of raw data.

Role of Social Networking Sites in Enhancing Teaching Environment

In recent days, most of the academic institutions across the world understand the usefulness of social networks for teaching and learning. In general, information is being transferred across the world for multiple purposes in different aspects through social media networks. In academic environment to enhance the teaching and learning processes social media networks are used to greater extent. Researchers and academicians are making use of social media tools, specifically Facebook, Blogs, Google groups, SkyDrive and Twitter for teaching and research. Further, the academic performance of students has been tested statistically by teachers using Social Networking Sites (SNS). The study has been carried out to understand the role of SNS in teaching environment which reveals that students are accessing various social media tools for information sharing and personal interaction. Finally, it has been observed from the analysis that there is increasing demand for the role of SNS in future education perspective. In this chapter the role of SNS in teaching environment is carried out elaborated and presented.

Conversion of Higher into Lower Language Using Machine Translation

This chapter addresses an exclusive approach to expand a machine translation system beginning higher language to lower language. Since we all know that population of India is 1.27 billion moreover there are more than 30 language and 2000 dialects used for communication of Indian people. India has 18 official recognized languages similar to Assamese, Bengali, English, Gujarati, Hindi, Kannada, Kashmiri, Konkani, Malayalam, Manipuri, Marathi, Nepali, Oriya, Punjabi, Sanskrit, Tamil, Telugu, and Urdu. Hindi is taken as regional language and is used for all types of official work in central government offices. Commencing such a vast number of people 80% of people know Hindi. Though Hindi is also regional language of Jabalpur, MP, India, still a lot of people of Jabalpur are unable to speak in Hindi. So for production those people unswerving to know Hindi language we expand a machine translation system. For growth of such a machine translation system, used apertium platform as it is free/open source. Using apertium platform a lot of language pairs more specifically Indian language pairs have already been developed. In this chapter, develop a machine translation system for strongly related language pair i.e Hindi to Jabalpuriya language (Jabalpur, MP, India).