Semantic Web (SW)

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Organizational Semantic Web based Portals

Organizational Semantic Web based Portals

Requirements change over time leading to extensions to the information model. The semantic web helps in two ways. Firstly, the user interface and submission tools can be generated from the declarative ontology. Secondly, the semi-structured data representation of RDF permits new data properties and types to be incrementally added without invalidating existing data, in such a way that both original and extended formats can be used interchangeably. This suggests an alternative approach to information portal design. Instead a long top-down design cycle, we start from a seed ontology and information structure that we extend incrementally.
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Semantic Web and Ontology: Effective Approaches to Build Intelligent Web

Semantic Web and Ontology: Effective Approaches to Build Intelligent Web

The Semantic Web [3] is distributed and heterogeneous. It has brought the evolution of the Web to a higher level. Tim Berners-Lee, who invented the World Wide Web, has worked on the Semantic Web stating “Semantic Web is not a separate Web but an extension of the current one, where the information is given in a well-defined meaning, enhancing to create collaborative environment for computers and people to work in cooperation.”[2].Hence, Semantic Web aims to make information available on the Web understandable by humans and computers focusing to enhance its usability as a medium of collaboration and to ensure its contents can be understood by machines. Web Services use HTTP to display the content of a page whereas Semantic Web focuses to generate machine readability by semantically representing the data or information in resources.
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On Coreference and the Semantic Web

On Coreference and the Semantic Web

Coreference within the Semantic Web is a growing, yet unappreciated problem, at least until recently. It has been suggested that it is a matter that will resolve as the Semantic Web evolves, with careful social engineering and planning. However, having performed a detailed study into the nature of this problem, investigating its occurrence not just within the Semantic Web but in other fields as well, we consider that the problem cannot be avoided. When looking at its appearance in related fields such as data warehousing and Artificial Intelligence, it becomes immediately obvious that the nature of the Semantic Web causes coreference to be systemic and prevents any existing solutions from being transferred. As larger knowledge bases and initiatives appear more frequently, coreference will become a significant barrier to progress and the need for an efficient system for managing references will increase, rather than subside. It is our conclusion that the most effective means for combating the issue is to make coreference-awareness an architectural feature of future semantic applications.
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The Advent of Semantic Web in Tourism Information Systems

The Advent of Semantic Web in Tourism Information Systems

The goal of the Semantic Web initiative is to provide an open infrastructure for intelligent software agents and web services. This infrastructure is based on formal domain models (ontologies) that are linked to each other on the Web. The domain model of an ontology can be taken as a unifying structure for giving information in a common representation and semantics. An ontology comprises the classes of entities, relations between entities and the axioms which apply to the entities of that domain (Mizoguchi, 2004). Through the use of metadata organized in numerous interrelated ontologies, tourism information can be tagged with descriptors that facilitate its retrieval, analysis, processing and reconfiguration. In addition, ontologies can offer a promising infrastructure to cope with heterogeneous representations of tourism web resources. Data heterogeneity can be solved, if semantic reconciliation with respect to the domain ontology is provided between the different tourism information systems. For the tourism industry, the development of ontologies is fundamental to allow machine-supported tourism-related data interpretation and integration. A brief presentation of tourism ontologies follows.
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Crowsdsourcing semantic web

Crowsdsourcing semantic web

This research did not start as a semantic web project. It started by looking at ways to make maps smarter so that they could be used to perform more sophisticated tasks. The problem was that traditional digital maps such as GoogleMaps or OpenStreetMap are built on quantitative data, with all the entities on the map largely defined by their quantitative properties. Map objects were defined as being points, lines or polygons; each rooted to a coordinate system and the distance between objects measured in metres and miles. The problem with this quantitative foundation is that it fails to represent how people view the world. A new way of representing locations on maps was needed, one which could communicate the qualitative properties of that location. So instead of something being ten metres away, it could be described as being nearby. So that an architect can describe a building in the terminology that makes sense to him/her, while a restaurant critic could describe the same building according to the properties of the restaurant it houses. As a result of investigating the idea of building qualitative maps, a solution emerged — ontologies. Ontologies take many forms and can relate to knowledge domains at both higher and lower levels. For the purposes of this thesis, an ontology provides a description of the knowledge needed to describe a particular subject domain. Ontologies usually consist of concepts, which determine what type of objects are being described, and object properties which establish how concepts can be linked. They may also contain the instances of the concepts along with data properties that can be used to link concepts to static data such as strings, integers or even coordinates. Additionally, ontologies embody a logical system that can be understood and used by computers to infer new knowledge and make new connections. If maps were based on ontologies, the kind of qualitative links that are needed to build smarter maps, and more adaptable for specialist groups of users, could be created.
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Privacy for Semantic Web Mining using Advanced DSA – Spatial LBS Case Study in mining

Privacy for Semantic Web Mining using Advanced DSA – Spatial LBS Case Study in mining

A review of Semantic Web use in the EU shows that its related technologies are finding their way into real- world applications. Rather than being a fashionable research issue, the semantic web is slowly but surely becoming a reality. Semantic Web is an extension of the current Web in which data and information on the Web are defined and linked in a way that it can be used by computers not only for display purposes, but for automation, integration, and reuse of data across various applications. The Semantic Web allows computers to make more sense of the information on the Web with the result of facilitating better cooperation between computers and people. The semantic web is a recent initiative to take the World Wide Web much further and develop it into a knowledge representation and computing. The aim of semantic web is not only to support to access information on the web but also to support its usage. The semantic web encourages and facilitates the formulation of shared vocabularies and shared knowledge in the form of ontology’s. The construction and design of ontology’s for functions of web services is an area that is currently topic of active research: Requests to a service and the reaction of the server can be collected and learning methods, Identification of user behavioral patterns, Teaching aids etc.
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Enhanced matching engine for improving the performance of semantic web service discovery

Enhanced matching engine for improving the performance of semantic web service discovery

Chapter 2 provides background information about the concepts involved in the scope of this study as well as the common aspects of Web service discovery architectures. In addition, the important frameworks for Semantic Web service are studied and compared. A taxonomy is provided to classify Web service discovery systems from various perspectives. This chapter also focuses on the literature of Semantic Web service discovery and categorizes the existing approaches. In addition, a set of characteristics is presented to classify the approaches to Semantic Web service discovery in more detail. Finally, some of the problems of the current Semantic Web service discovery approaches that affect their performance and QRT are recognized and explained.
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WEB USAGE MINING USING SEMANTIC WEB APPROACH: A STUDY, SURVEY AND ANALYSIS

WEB USAGE MINING USING SEMANTIC WEB APPROACH: A STUDY, SURVEY AND ANALYSIS

The ultimate aim of this research paper is to show the web usage mining using semantic web approach. Web data mining is an essential approach in data mining that allows user or machine to retrieves and analyzes the information from web in reflex manner. In fact it is an art of getting the desired result in terms of relevant information from World Wide Web. Web usage mining is one of the essential branch of web data mining. Semantic web helps in integration the data from different distributed environments or web resources. Web resources is organized in the form of metadata known as Resource Description Framework (RDF). RDF is Extended Markup Language (XML) based data model, where data is depicted in the form of triplets. In this paper we are including the study of KNIME tool to show web usage mining. KNIME (read as “naim”) stand for Konstanz Information Miner, is developed by KNIME.com and written in Java. It is an open source data analytics, reporting and integration platform. We are also including Apache Jena Fuseki, Open virtuoso (For DBpedia) tools to show web usage mining using Semantic web approach. To facilitate RDF query, we will use SPARQL. It is very efficient language to execute RDF data.
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The Semantic Web Revisited

The Semantic Web Revisited

The article included many scenarios in which intelli- gent agents and bots undertook tasks on behalf of their human or corporate owners. Of course, shopbots and auc- tion bots abound on the Web, but these are essentially handcrafted for particular tasks; they have little ability to interact with heterogeneous data and information types. Because we haven’t yet delivered large-scale, agent-based mediation, some commentators argue that the Semantic Web has failed to deliver. We argue that agents can only flourish when standards are well established and that the Web standards for expressing shared meaning have pro- gressed steadily over the past five years. Furthermore, we see the use of ontologies in the e-science community pre- saging ultimate success for the Semantic Web—just as the use of HTTP within the CERN particle physics commu- nity led to the revolutionary success of the original Web.
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A REVIEW ON SEMANTIC WEB

A REVIEW ON SEMANTIC WEB

The World Wide Web is based mainly on documents written in Hyper Text Markup Language (HTML), a markup convention that is used for coding a body of text interspersed with multimedia objects such as image and interactive forms. The semantic web involves publishing the data in a language, Resource Descriptive Framework (RDF) specifically for data, so that it can be manipulated and combined just as can data files on a local computer. The HTML language describes documents and the links between them. RDF, by contrast, describes arbitrary things such as people, meetings, and airplane parts.
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E-resource management and the Semantic Web : applications of RDF for e-resource discovery

E-resource management and the Semantic Web : applications of RDF for e-resource discovery

example, if information retrieval systems can better understand the meaning of items within an e-resource collection then it will be easier to design systems that provide greater retrieval precision during users’ information-seeking tasks. Increased precision could be achieved by better understanding user context, disambiguating conceptually similar items, performing some of the functions controlled vocabularies might; but improvements in recall could also be achieved by augmenting the results with conceptually related resources, perhaps spanning a variety of media. Although the deployment of the Semantic Web within LIS is our focus, such semantic technologies assume greater potential and complexity when applied to everyday tasks, such as booking a medical appointment 8 or ordering wine for a social event 9 . In such
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An Overview of the Semantic Web

An Overview of the Semantic Web

The concept of Semantic Web was introduced by Tim Berners-Lee, the developer of HTML, Hyper Text Transfer Protocol (HTTP), Uniform Resource Identifiers (URI) and World Wide Web (WWW). His visualization of Semantic Web is that in future we will have intelligent software agents that will analyze a particular given situation and present us with the best possible alternatives. In other words, the connectivity that is found today only on PCs through the Web, will become a part of our daily life.

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ROLE OF ONTOLOGY IN SEMANTIC WEB MINING

ROLE OF ONTOLOGY IN SEMANTIC WEB MINING

901 | P a g e A program that wants to compare or combine information across the two databases has to know that these two terms are being used to mean the same thing. Ideally, the program must have a way to discover such common meanings for whatever databases it encounters. A solution to this problem is provided by the third basic component of the Semantic Web, collections of information called ontology. In philosophy, ontology is a theory about the nature of existence, of what types of things exists; ontology as a discipline studies such theories. Artificial-intelligence and Web researchers have co-opted the term ontology is a document or file that formally defines the relations among terms. The most typical kind of ontology for the Web has taxonomy and a set of inference rules. The taxonomy defines classes of objects and relations among them. For example, an address may be defined as a type of location, and city codes may be defined to apply only to locations, and so on. Classes, subclasses and relations among entities are a very powerful tool for Web use. We can express a large number of relations among entities by assigning properties to classes and allowing subclasses to inherit such properties. If city codes must be of type city and cities generally have Web sites, we can discuss the Web site associated with a city code even if no database links a city code directly to a Web site. Inference rules in ontology supply further power. Ontology may express the rule "If a city code is associated with a state code, and an address uses that city code, then that address has the associated state code." A program could then readily deduce, for instance, that a Cornell University [11].
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Online Feedback System for Educational Institutions for Better Evaluation of Faculty's Performance Using Semantic Web (SW) Technology

Online Feedback System for Educational Institutions for Better Evaluation of Faculty's Performance Using Semantic Web (SW) Technology

The layered architecture of the semantic web is shown in Fig.1.Semantic web applications are recently being developed in fields and disciplines including education.The Semantic Web is the expansion of the WWW that enables people to distribute content beyond the restrictions of applications and websites[1]. For the development of human mind power, both formal and informaleducation is important. In whichever format, education is being provided, the ability for the humans to learn effectively is of primaryimportance[2]

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The Semantic Web as a Semantic Soup

The Semantic Web as a Semantic Soup

The Semantic Web is currently best known for adding metadata to web pages to allow computers to 'understand' what they contain. This idea has been applied to people by the Friend of a Friend project which builds up a network of who people know through their descriptions placed on web pages in RDF. It is here proposed to use RDF to describe a person and to have their RDF document follow them around the Internet. The proposed technique, dubbed Semantic Cookies, will be implemented by storing a user's RDF in a cookie on their own computer through the browser. This paper considers the concept of Semantic Cookies and investigates how far existing technology can be pushed to accommodate the idea.
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Metadata and the Semantic Web

Metadata and the Semantic Web

Solution: Annotation Metadata and Data Pedigree CMCS provides subject area metadata tags to identify data Species name, Chemical Abstracts Service number, formula, common name, vibration[r]

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A Surya on Web Service Data Classification Discovery using Semantic Similarity

A Surya on Web Service Data Classification Discovery using Semantic Similarity

putting data together, of new ways of querying data. Exploratory OLAP is to discover, acquire, integrate, and analytically query new external data. The Semantic Web (SW) has been conceived as a means to build semantic spaces over web published contents so that web information can be effectively retrieved and processed by both humans and machines for a great variety of tasks. To support the data integration, facilities must be provided to resolve conflicts in the data, to combine data from many different formats and sources, and to structure data in a multidimensional format. Again, SW technologies such as reasoning provide a powerful foundation for this. Advantages
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Bringing Communities to the Semantic Web and the Semantic Web to Communities

Bringing Communities to the Semantic Web and the Semantic Web to Communities

The alternative is the manual addition of metadata. Peo- ple will put time into something for two main reasons be- cause it is something they care about or because they stand to gain from it. Gains, perceived or real, can take many forms but needs to be proportional to the amount of work required. Data entry is universally recognized as being both time consuming and boring and yet given the right incentive people will do it voluntarily. Examples of this can be seen throughout the Internet the World Wide Web took off when the average user was able to easily create websites about the things that interested them. Amateur authors, and fan au- thors in particular, are hobbyist orientated, writing for the love of it and their love of the characters. The online com- munities that support them are based on concept of sharing that love. This translates to the sharing of electronic doc- uments, with accompanying metadata, and the sharing of information. Given the opportunity these types of commu- nities want to, and are, creating and accuracy checking the type of data that will power semantic applications.
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Ontology Based Searching Techniques in Web Environment

Ontology Based Searching Techniques in Web Environment

Ontology infers the relationship of elements dependent on its conduct and gives a typical vocabulary to furnish a common conceptualization of space with formal and express detail. Semantic Web upgrades the present Web to the cutting edge in which machines are improved to comprehend the importance of information as opposed to perusing the information, defeating the present issues of Web. Semantic Web Mining coordinates Semantic Web and Data Mining, which are the two approaching territories of research. Semantic Web utilizes Ontology as its key element to characterize the semantics of elements. This paper introduces the design and key layers of Semantic Web, Structure and Operations of Ontology and the working of RDF, Ontology and Semantic Web Rule Language with a commonsense Scenario.
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A Score based Web Page Ranking Algorithm

A Score based Web Page Ranking Algorithm

Han et al. [1] tried to distinguish the web content mining from two different points. One is Retrieval view and another one is database view. Laplas [2] gave the brief description about different types of web mining and research area of web mining. Chakrabarti et al. [3] described crawler, which explore its crawl boundary to discover the links that are probably to be most pertinent for the crawler. Kobayashi and Takeda [4] discussed the development of new strategies targeted to give solution of some problems such as noise, slack recovery speed and broken links associated with web- based information recovery. Mayfield et al. [5] explored the indexing using both words and N-grams by using a HAIRCUT (Hopkins Automated Information Retrieving for Combing Unstructured Text) system. Kim and Kwon [6] proposed a method of information retrieval using the context information by adopting different types of page ranking algorithms, which are context tags algorithms. Cho et al. [7] described the efficient approach to improve web crawlers by identifying replicated document collections. Brin and Page [8] gave an in-depth description of web search engines of large scale as well as described the PageRank algorithm. The algorithm states that the topicality of a page increases with the number of hyperlinks to it from other topical pages. Beigi et al. [9] introduced MetaSEEkA, a meta-search engine which is based on content, used for discovering images on the Web based on their visual information. MetaSEEkA was designed to penetratingly select and interface with diverse on-line image search engines by ordering their performance of user queries for different classes. Nguyen [10] presented a new web usage mining process for finding sequential patterns in web usage data which can be used for predicting the possible next move in browsing sessions for web personalization. Cooley et al. [11] described WEBMINER, a system for Web usage mining. Ramulu et al. [12] described the over view of semantic mining. Chakravarthy [13] proposed a research on how semantic web technologies can be used to mine the web, for information extraction and examined how new unsupervised processes can aid in extracting precise and useful information from semantic data.
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