Semantic Web Mining

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A State of the Art Survey on Semantic Web Mining

A State of the Art Survey on Semantic Web Mining

The vendor, consequently lose valuable customers and because of the later intention to satisfy and grasp as much as possible of customers, they require a high level of accuracy and correctness of the provided web content. Most of the existing applications suffer from the limita- tion of retrieved information from content not in a text format such as flash animations, videos, and images causing the analysis process to be done on a small pro- portion of the whole data. The proposed solution is to use Semantic Web Mining to discover a novel relation from the components’ structures of a WebObjects, “a struc- tured group of words or a multimedia files present within a Webpage that has metadata for describing its content” [13], based on web user’s perspective and possibly lead- ing to enhance the website’s satisfaction level by em- powering the information provided and considering its preferred appealing format according to the preferences of web user. The presented research shows great inten- tion in the details and is well structured. However, the use of a very small size of participants, only 10 users, to test, evaluate, and validate the proposed algorithm could lead to incorrect and unreliable results. There is informa- tion missing about the methodology used to select the representative sample, and the experiment’s details such as where and how the experiment was conducted.
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ROLE OF ONTOLOGY IN SEMANTIC WEB MINING

ROLE OF ONTOLOGY IN SEMANTIC WEB MINING

The foreground of semantic web mining is from artificial intelligence. The Semantic Web vision given by Tim Berners-Lee et. al. [1], which is currently supported by the World Wide Web consortium, is quite determined and has to be gradually realised (and in particular outreached to industry) in the long term. Thus, this grand vision both represent and stand on an ongoing research framework, which has early roots in computer science, more precisely in formal logics, knowledge representation and reasoning, and databases. The vision of the Knowledge Web network experts on the evolution of some topics related to the Semantic Web is presented. Finally, the current research directions, which aim at supporting the scaling up of semantic technologies from closed intranets to the open internet, are discussed.
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Survey on recommendation system using semantic web mining

Survey on recommendation system using semantic web mining

With the rapid development of the World Wide Web, Recommender systems are become an important part of online sites, people can now obtain and transform knowledge easily through a variety of online publishing tools, such as weblogs and online forums. The web has thus become a valuable and abundant information source that has a significant effect on users’ lifestyles, especially their purchasing behavior [3]. Fast development of users of web has offered climb to e-business applications. The origin of recommenders can be traced back to methods like approximation theory, cognitive science, information retrieval and management science [4]. There are many benefits of having a recommender system like cross-selling, personalization, keeping the customers informed and customer retention. The websites that use recommenders are Amazon, MovieLens, eBay, CDNow, MovieFinder etc. In collaborative filtering approach, the system recommends new items to the user by analyzing items purchased by similar users (Amazon.com) [4]. In all cases, the main challenge to building an efficient recommender to face is to process a large amount of data. Processing this much data “semantic web mining” is one of the solution.
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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

The Semantic Web would be an extension of the current one, in which information is given well defined meaning, better enabling computers and people to work in cooperation. Thus the current web is basically comprised of documents, presented by computers and read by man would also include data and information that would automatically be handled by agents and utilities. [3-5] Researchers from universities and company’s about semantics divided into three groups: The first deals with Semantic Web Services; the second presents Semantic Web processes; and the third deals with the applications for the Semantic Web. Exposing functionality in the form of Web Services is generally more attractive for market participants than publishing all relevant facts directly on the web. Refer to Table 1 which provides an overview of emerging semantic web services technologies.
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Semantic web mining trademark databases

Semantic web mining trademark databases

The work was motivated by increasing of fraud cases best an data similarities, where information retrieval system do not handle this particular issue and trademark similarity. The target on similarities during trademarks, which becomes when more than two or more trademarks like equal or relevant semantic implant. The advantages and limitations of each data similarity of reflow algorithm are described. The system work, conceptual similarities among trademarks like equal or relevant semantic implant. The desire of a hypothetic model of retrieval trademark is depends on hypothetical similarity. The main model language processing technology, data paths and lexical resources to calculate hypothetic similarity between different trademarks.
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Integrating Semantic Web and Web Mining into Semantic Web Mining

Integrating Semantic Web and Web Mining into Semantic Web Mining

When the customers log in to the website, they want to obtain information from the web. So, to solve this management problem we need the right techniques and methods that derive from different areas such as: Expert Systems (ES), Artificial Intelligent (AI), Database (DB), and one of the Information Retrieval (IR) methods such as Structure Query Language (SQL). In brief, Web Mining: automated discovery and analysis of useful information from web documents and services using one of the data mining techniques.

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Web Log Analyzer for Semantic Web Mining

Web Log Analyzer for Semantic Web Mining

In this study researchers presented a survey of the use of Web mining for Web personalization. More specifically, they introduce the modules that comprise a Web personalization system, emphasizing on the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization area are presented. The researchers proposed that Web personalization is the process of customizing the content and the structure of a Web site to the specific and individual needs of each user, without requiring from them to ask for it explicitly. This can be achieved by taking advantage of the user’s navigational behavior, as it can be revealed through the processing of the Web usage logs, as well as the user’s characteristics and interests. They also include the overall process of Web personalization consists of five modules, namely: user profiling, log analysis and Web usage mining, information acquisition, content management and Web site publishing. The main component of a Web personalization system is the usage miner. Log analysis and Web usage mining is the procedure where the information stored in the Web server logs is processed by applying statistical and data mining techniques, such as clustering, association rules discovery, classification and sequential pattern discovery, in order to reveal useful patterns that can be further analyzed. Such patterns differ according to the method and the input data used, and can be user and page clusters, usage patterns and correlations between user groups and Web pages. Those patterns can then be stored in a database or a data cube and query mechanisms or OLAP operations can be performed in combination with visualization techniques. The most important phase of Web
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A SURVEY ON DATA MINING FOR SEMANTIC WEB DATA

A SURVEY ON DATA MINING FOR SEMANTIC WEB DATA

Semantic web works in smarter way as it provide web service, which synchronizes and arranges all the data over web correctly and in a disciplined manner. With the success of the World Wide Web (WWW) addresses new challenge as the amount of data is so huge. The Semantic Web addresses the part of this challenge by trying to make the data machine understandable, and Web Mining addresses the other part by extracting the useful knowledge hidden in these data [4].Semantic Web Mining aims at combining the two areas Semantic Web and Web Mining along with data mining. As there is increase in the numbers of resercher work on improving the quality of Web data Mining by exploiting semantics in theWeb data, and using mining techniques for building the Semantic Web.
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Mining Data Using Various Sequential Patterns Mining Algorithm in Semantic Web Environment

Mining Data Using Various Sequential Patterns Mining Algorithm in Semantic Web Environment

Semantic Web Mining is an integration of two important research areas: Semantic Web and Data Mining [1]. The existing Web (WWW) has a huge amount of information that is often unstructured and only human understandable. Web is rich with data; gathering and making sense of the information in the web is more difficult because the document of the Web is largely unorganized and unstructured. On the unstructured human readable web data, semantic web is used to effectively and efficiently creating a machine-understandable. In Semantic Web Mining, it refers to the application of data mining techniques to mine knowledge from World Wide Web [2] or the part of data mining that refers to the use of algorithms for extracting patterns fro m resources scattered over in the web.
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Knowledge Extraction for Semantic Web using Web Mining with Ontology

Knowledge Extraction for Semantic Web using Web Mining with Ontology

The authors Gerd Stumme, Andreas Hotho have proposed Semantic Web Mining aims at combining the two fast- developing research areas Semantic Web and Web Mining [13]. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of data Web mining operates on, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web resources and navigation behavior are increasingly being used. This fits exactly with the aims of the Semantic Web: the Semantic Web enriches the WWW by machine process able information which supports the user in his tasks. In this paper, from this paper we observed the interplay of the Semantic Web with Web Mining, with a specific focus on usage mining.
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Semantic Web Usage Mining Techniques for Predicting Users’ Navigation Requests

Semantic Web Usage Mining Techniques for Predicting Users’ Navigation Requests

ABSTRACT: The explosive growth of the World Wide Web (WWW) has resulted in intricate Web sites, demanding for tools and methods to complement user skills in the task of searching for the desired information. In this context Web usage mining techniques have been developed for the discovery and analysis of frequent navigation patterns from Web server logs, which can be used as input for recommendation engines. Web usage mining techniques have been associated with Web content mining approaches in order to increase the accuracy of recommendation mechanisms. Existing approaches represent Web pages’ content essentially by means of keywords, N-grams or ontologies of concepts, being, therefore, incapable of capturing the semantic information and the relationships among pages at the semantic level. Herein, we propose a method that combines usage patterns extracted from server logs with detailed semantic data that characterizes the content of the corresponding pages. Thus, a method to extract and analyze frequent semantic navigation patterns which are fed into a recommendation engine is proposed. We argue that by integrating usage and Web pages’ detailed semantic information in the personalization process we will be able to increase the recommendation accuracy. The proposed method is an example of semantic Web mining that combines two fast developing research areas; Semantic Web and Web Usage Mining. We conducted an extensive experimental evaluation that provides strong evidence that the recommendation accuracy increases with the integration of semantic and usage data. The results show that the proposed method is able to achieve 15-17% better accuracy than a usage based model, 5-7% better than a N-gram based model and 4-6% better than a ontology based model. Also the proposed method is able to address the new item problem of solely usage based techniques by augmenting navigation patterns with newly added pages in a Web site.
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On Coreference and the Semantic Web

On Coreference and the Semantic Web

One interesting technology for resolving equivalent references from a set of candidates is a form of graph analysis known as communities of practice (CoP) [28]. A community of practice is a “group of people connected by a shared interest in a task, problem, job or practice” [29]. In the context of the Semantic Web, this can be viewed for a given person as other people who are connected to a large number of things that the given person is also connected to. By obtaining the CoP for the members of sets of potential coreferences, or individual entities, we can derive a measure of similarity from the degree of overlap between CoPs. When this measure is above a threshold level, the sets of coreferences or individuals in question are likely represent the same entity, when combined with textual matching. A tool, ONTOCOPI [30], has been developed for calculating CoPs and has been tested as a component part of a system for coreference resolution [31]. A system was proposed for resolving coreferences that integrates mapping and populating ontologies from multiple, possibly legacy, sources. A CoP system could well be integrated with the framework proposed in this paper and would provide a desirable degree of automation.
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The Semantic Web Revisited

The Semantic Web Revisited

A growing need for data integration Meanwhile, the need has increased for shared seman- tics and a web of data and information derived from it. One major driver—one that this magazine has reported on extensively—has been e-science (IEEE Intelligent Sys- tems, special issue on e-science, Jan. 2004). For example, life sciences research demands the integration of diverse and heterogeneous data sets that originate from distinct communities of scientists in separate subfields. Scientists, researchers, and regulatory authorities in genomics, pro- teomics, clinical drug trials, and epidemiology all need a way to integrate these components. This is being achieved in large part through the adoption of common conceptual- izations referred to as ontologies. In the past five years, the argument in favor of using ontologies has been won— numerous initiatives are developing ontologies for biology (for example, see http://obo.sourceforge.net), medicine, genomics, and related fields. These communities are devel- oping language standards that can be deployed on the Web. Many other disciplines are adopting what began in the life sciences. Environmental science is looking to integrate data from hydrology, climatology, ecology, and oceanogra-
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A Survey of Web Mining and Various Web Mining Techniques

A Survey of Web Mining and Various Web Mining Techniques

Web mining play an important role in achieving the useful information role which we want. It refers to discover and analysis the useful content over the world wide web. It is basically obtaining knowledge from number of web page in websites[2]. The Area of research increasing day by day because of the interest various research communities. The splendid growth of knowledge resources accessible on internet, Now a days interested in E-commerce. The situation that is observed to exist partly create distraction. Although the constitutes web mining is the technique for fetching the information either online or offline from the text content which is present on the web like newsletter, newsgroup the text content html document achieve by deleting html tags and web resources a selected by manually[11]. The information selection is type of conversion process of initial data.We suggest decomposition web mining in to the sub task.
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A REVIEW ON SEMANTIC WEB

A REVIEW ON SEMANTIC WEB

Several formats and language form the building blocks of the semantic web. Some of these include Identifier (URI), Documents: Extensible Markup Language (XML), Statements: Resource Description Framework (RDF), variety of data interchange formats (e.g. RDF/XML, N3) and notations such as RDF Schemas (RDFS) and the Web Ontology Language (OWL), all of which are intended to provide a formal description of concepts, terms and relationships within a given knowledge domain, Logic, Proof and Trust.

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Web Semantic and Ontology

Web Semantic and Ontology

In the remainder of this paper, I will instead use an informal \human readable” syntax based on the one used in the Protege 4 ontology development tool [4]. A key feature of OWL is its basis in Description Logics (DLs), a family of logic-based knowledge representation formalisms that are descendants of Semantic Networks and KLONE, but that have a formal semantics based on rstorder logic [5]. These formalisms all adopt an objecto- riented model, similar to the one used by Plato and Aristotle, in which the domain is described in terms of indi- viduals, concepts (called classes in RDF), and roles (called properties in RDF). Individuals, e.g., “Hedwig”, are the basic elements of the domain; concepts, e.g., “Owl”, describe sets of individuals having similar characteristics; and roles, e.g., “hasPet”, describe relationships between pairs of individuals, such as “HarryPotter hasPet Hedwig”.
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AN OPTIMIZED PAGE RANK ALGORITHM WITH WEB MINING, WEB CONTENT MINING AND WEB STRUCTURE MINING

AN OPTIMIZED PAGE RANK ALGORITHM WITH WEB MINING, WEB CONTENT MINING AND WEB STRUCTURE MINING

Data preparation is the first issue in the preprocessing phase. Web log data may require to be cleaned from entries concerning pages that returned an error or graphics sleeve accesses. In addition, crawler action can be cleaned out, for the reason that such entries do not give useful information regarding the site's usability. An additional difficulty to be met has to do with caching. Accesses to cached pages are not recorded in the Web log; as a result such information is lost. Caching is deeply reliant on the client-side technologies applied and as a result cannot be dealt with without difficulty. In such cases, cached pages can more often than not be incidental using the referring information starting the logs.
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Novel Web Usage Mining for Web Mining Techniques

Novel Web Usage Mining for Web Mining Techniques

In this paper, a new session reconstruction algorithm is introduced. This algorithm is better than previously developed both time and navigation oriented heuristics as it does not allow page sequences with any unrelated (without any hyperlinks from the preceding page to the next page) consecutive requests to be in the same session. Navigation oriented heuristics insert artificial browser (back) requests into a session in order to guarantee that consecutive requests will have connectivity between each other. Thus, the session sequences are shorter and easier to process than those generated by navigation oriented heuristics. This algorithm also enhances navigation-oriented heuristics by using a time oriented extension restricting requests in a session to be at most within a 30 minute period. Another advantage of our heuristic is that it guarantees that all sessions generated will be maximal sequences and do not subsume any other session. We have implemented agent simulator for generating real user sessions. Our agent simulator generates real sessions satisfying both connectivity and timestamp rules. We have compared the sessions reconstructed by our heuristic and previous heuristics against the real sessions generated by the agent simulator. We have also defined the real accuracy of the constructed sessions as a sequence and subsequence relationship. As a result, our approach seems a reasonable method for using reactive web usage mining in real world applications.
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Web Mining for Web Personalization

Web Mining for Web Personalization

Web site personalization can be defined as the process of customizing the content and structure of a Web site to the specific and individual needs of each user taking advantage of the user’s navigational behavior. The steps of a Web personalization process include: (a) the collection of Web data, (b) the modeling and categorization of these data (preprocessing phase), (c) the analysis of the collected data, and (d) the determination of the actions that should be performed. The ways that are employed in order to analyze the collected data include content-based filtering, collaborative filtering, rule-based filtering, and Web usage mining. The site is personalized through the highlighting of existing hyperlinks, the dynamic insertion of new hyperlinks that seem to be of interest for the current user, or even the creation of new index pages. Content-based filtering systems are solely based on individual users’ preferences. The system tracks each user’s behavior and recommends items to them that are similar to items the user liked in the past. Collaborative filtering systems invite users to rate objects or divulge their preferences and interests and then return information that is predicted to be of interest to them. This is based on the assumption that users with similar behavior (e.g. users that rate similar objects) have analogous interests. In rule-based filtering the users are asked to answer a set of questions. These questions are derived from a decision tree, so as the user proceeds to answer them, what he finally receives as a result (e.g. a list of products) is tailored to his needs. Content-based, rule-based, and collaborative filtering may also be used in combination, for deducing more accurate conclusions. In this work we focus on Web usage mining. This process relies on the application of statistical and data mining methods to the Web log data, resulting in a set of useful patterns that indicate users’ navigational behavior
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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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