Top PDF Knowledge based Recommendation System in Semantic Web A Survey

Knowledge based Recommendation System in Semantic Web   A Survey

Knowledge based Recommendation System in Semantic Web A Survey

Expert systems are computer programs used for producing recommendations or problem solving based on knowledge in some domain [36]. Traditional Web-based Expert system does not support the method for representing data in a format that can be used for machine reasoning, which becomes a major drawback for them [37]. This drawback can be overcome by using Semantic Web instead of traditional Web where there is representative format supporting representation of information in a machine process able format. Some of the approaches used for rule-based personalization in Semantic Web using expert systems are discussed in the following paragraphs. Garcia et al. [38] proposed an approach for producing recommendation based on expert system using fuzzy logic techniques. Customer is the users of this tourist personalization system and is required to submit their preferences information, which is converted into fuzzy sets by an expert describing customer characteristics. Fuzzy rules are evaluated to match the fuzzy representation of hotel with customer characteristics. Results of the match are defuzzified to obtain the concrete results according to the hotel ontology. The best hotel is recommended based on the calculated weights.
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A Survey on Semantic Web based E learning

A Survey on Semantic Web based E learning

Shrivastava et al.[12] proposed a semantic web based e- learning model based on agents. For implementing the e- learning system there are many agents as described by Dawn G. Gregg in his paper [4]. These are Instruction Agent, Lesson Planning Agent, Resource Location Agent, Personalization Agent, Learner Centred Agent, and Collaboration Agent. These agents were used to describe an e- learning system. Semantic web is used by some of these agents in order to make the-learning system more responsive and interactive. Shrivastava used these agents to describe the e-learning system. In proposed model there are two users instructor and learner. Both use the appropriate agent for proving and getting the-learning resource.
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Survey on recommendation system using semantic web mining

Survey on recommendation system using semantic web mining

Honey Jindal and Sandeep Kumar Singh developed a hybrid recommendation system using collaborative and content based filtering to solve the cold start problem. The system worked in two phases online and offline phase using movie rating dataset. In offline phase they construct a rating matrix from the given dataset and create similar user clusters. In online phase extract users’ demographic information from registration details. Search the cluster through the rules and after searching cluster, identify the users in the cluster. After that, identify the movies rated by the users in that cluster. Finally take average of those movies rating and give a list of recommended movies [20].
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Exploiting Semantic Web Technology for Personalized Recommendation System

Exploiting Semantic Web Technology for Personalized Recommendation System

through querying the knowledge. The experimental results are promising and indicates usefulness of the proposed models. Semantic recommendation system provided quick and relevant results for more précised search queries. Here, more the number of patterns matched to the Webpages, less time taken for execution of the search queries. Despite the difference in the execution time taken by these systems the performance of both the recommendation system in context in finding the relevant webpages is high. Time Execution required for Webpage Recommendation System was more as compare to Semantic Recommendation System. Semantic Recommendation System makes use of Domain Knowledge and Semantic network and improves the search results for user query. Recommendation Systems help users in sharing experience over the search results by providing recommended results in the Log file without disclosing the identity of the user whose recommendation where used.
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Knowledge based Grid Using Semantic Web for the University System

Knowledge based Grid Using Semantic Web for the University System

Grid was introduced in the mid1990s to denote a distributed computing environment for advanced science and technology. The concept of Computational Grid has been inspired by the „electric power Grid‟, in which a user could obtain electric power from any power station present on the electric Grid irrespective of its location, in an easy and reliable manner. When we need additional electricity we have to just plug into a power Grid to get additional electricity on demand, similarly for computational resources plug into a Computational Grid to access additional computing power on demand using resource. Internet users require lots of knowledge for their future need but problem is irrelevancy of data. The present web and search engines are fully based on syntax to retrieve the data from the distributed environment which does not full fill
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A Survey on Domain Relevant Web-Page Recommendation Using Ontology and Web Usage Knowledge

A Survey on Domain Relevant Web-Page Recommendation Using Ontology and Web Usage Knowledge

S. T. T. Nguyen [9] in 2009 proposed 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. This process consists of three main stages: preprocessing web access sequences from web server log, mining pre-processed web log access sequences by tree-based algorithm, and predicting web access sequences by using a dynamic clustering- based model. It is designed based on the integration of the dynamic clustering-based markov model with the Pre-Order Linked WAP-tree mining (PLWAP) algorithm to enhance mining performance. Web logs contain not only simple web usage sessions, but also useful information which can be used to trace web usage patterns in relation to browsing behavior and recommending more relevant web-pages to users. This process is called Web Usage Mining (WUM), which aims to discover potential knowledge hidden in the web browsing behavior of users. There are two major methods of sequential pattern discovery: deterministic techniques (recording the navigational behavior of the user) and stochastic methods (the sequence of web pages that have been visited in order to predict subsequent visits). This method focuses on the advanced model-based techniques of the stochastic methods for predicting hidden sequential patterns. Predicting web access patterns using the Markov model is very interesting in web personalization because of its special features, such as modeling a collection of navigation records, modeling user web navigation behavior. In order to resolve this complexity problem, this proposes a new mining algorithm for WUM based on the Markov model from the stochastic approach. The new feature of this algorithm is that it predicts user’s web navigation patterns using the frequent web access sequences extracted from the web logs rather than all web-pages. As a result, the complexity of the Markov model can significantly decrease because only frequently accessed web-pages are used, resulting into small number of states.
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Semantic-based Friends Recommendation System in Social Networks

Semantic-based Friends Recommendation System in Social Networks

recommendation systems prosperously. We will talk about elite website in our coming up segment that supplies recommendation. Items are suggested by visually examining the demeanor of like-minded-users. Groups are composed of such users, and items preferred by such groups are recommended to the utilizer, whose relishing and demeanor is akin to the group. In our model we have incorporated utilizer predilections obtained from Convivial Networking Site. Gregarious Networking sites are utilized intensively from last decade. According to the current survey, Convivial Networking sites have the most immensely colossal data set of users. Each gregarious networking internet site notes/records for each one and all action of utilizer (like: what utilizer relishes? what utilizer is doing? what is user’s hobby? Etc). Convivial Networking site will prove to be most sizably voluminous domain in understanding the utilizer comportment. One of the best examples of convivial networking is FACEBOOK. According to current news FACEBOOK is endeavoring to develop algorithm, to understand utilizer deportment. Convivial Networking sites can avail us in getting paramount information of users, such as age, gender, location, language, actives, relishes etc. our example brings into explanation these arguments of the utilizer to recommend books. Almost of the friend propositions mechanism trusts on pre-subsisting utilizer human relationship to choice friend candidates. For example, Facebook trusts on a convivial link analysis among those who already share prevalent friends plus encourages symmetrical users as possible friends. The patterns to group people together let in:
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Folksonomies, the Semantic Web, and Movie Recommendation

Folksonomies, the Semantic Web, and Movie Recommendation

In this paper, we have demonstrated that a movie recommendation system can be built purely on the keywords assigned to movie titles via collaborative tag- ging. By building different tag-clouds that express a user’s degree of interest, a prediction for a previously unrated movie can be made based on the similarity of its keywords to those of the user’s rating tag-clouds. With further work, we believe our recommendation algorithms can be improved by combining them with more traditional content-based recommender strategies. Since imdb pro- vides extensive information on the actors, directors, and writers of movies, as well as demographic breakdowns of the ratings, a more detailed profile can be constructed for each user. Also, our recommendation algorithms have not ex- ploited any collaborative recommender techniques. Further research may show that rating tag-clouds are a useful and more efficient way to find neighbours with similar tastes.
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A Survey on Various Techniques of Recommendation System in Web Mining

A Survey on Various Techniques of Recommendation System in Web Mining

There are many Researchers have worked in this area for finding problem and solutions in recommendation system. Aprojyoti Lopes, Bididha Roy[6] considered the research challenges in collaborative filtering is used user’s past data for finding user’s behavior. Authors suggest that used action based rational technique that provide recommendation as per changing user’s behavior. XI-Ze Heng zhang[7] considered the problem of user’s past preference is only used for recommendation. So authors proposed a personalized recommendation system using association rule mining and classification(CBA-CB). CBA-CB classifier is used to predict the item labels for new customers requirement and then assigns the class labels to the new customers.
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Social Networking- A Semantic-based Friend Recommendation System

Social Networking- A Semantic-based Friend Recommendation System

mobile computing, data mining, marketing and management. There are many subsisting e- commerce websites which have implemented recommendation systems prosperously. We will talk about elite website in our coming up segment that supplies recommendation. Items are suggested by visually examining the demeanor of like- minded-users. Groups are composed of such users, and items preferred by such groups are recommended to the utilizer, whose relishing and demeanor is akin to the group. In our model we have incorporated utilizer predilections obtained from Convivial Networking Site. Gregarious Networking sites are utilized intensively from last decade. According to the current survey, Convivial Networking sites have the most immensely colossal data set of users. Each gregarious networking internet site notes/records for each one and all action of utilizer (like: what utilizer relishes? what utilizer is doing? what is user’s hobby? Etc). Convivial Networking site will prove to be most sizably voluminous domain in understanding the utilizer comportment. One of the best examples of convivial networking is FACEBOOK. According to current news FACEBOOK is endeavoring to develop algorithm, to understand utilizer deportment. Convivial Networking sites can avail us in getting paramount information of users, such as age, gender, location, language, actives, relishes etc. our example brings into explanation these arguments of the utilizer to recommend books. Almost of the friend propositions mechanism trusts on pre-subsisting utilizer human relationship to choice friend candidates. For example, Facebook trusts on a convivial link analysis among those who already share prevalent friends plus encourages symmetrical users as possible friends. The patterns to group people together let in:
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Ontology Based Knowledge Grid in Semantic Web to Discover Knowledge in Distributed Environment

Ontology Based Knowledge Grid in Semantic Web to Discover Knowledge in Distributed Environment

the processes involved in the developing, deploying and utilizing E-course for deployment in the State of Kerala in India. It made possible by advanced technology web-servers based on a new architecture to establish effective and well managed learning management and collaboration systems and subject-specific interface which support to enhance the quality of education in distributed environment. The Knowledge Gr id based knowledge discovery model is builds on a computational grid which provides dependable and consistent access to computational resources. It used basic grid services and defines a collection of extra additional layers to develop the services of distributed knowledge discovery on the distributed connected resources where each node can be a sequential or a parallel machine. The Knowledge Grid enables the collaboration of scientists that must mine data that are stored in different research centers as well as executive managers that must use a knowledge management system that operates on several data warehouses located in the different company establishments. This represented an initial step for the design and implementation of a grid architecture that integrate different data mining techniques, algorithm and computational grid resources in the distributed environment 7 . The Grid-based data mining and
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The Application of Advanced Knowledge Technologies for Emergency Response

The Application of Advanced Knowledge Technologies for Emergency Response

This paper discusses a system that is composed of and constructed using a number of different AI/semantic web technologies for the purpose of supporting the high-level tactical command of the response to a large-scale emergency. More specifically, the system is structured around a sense-making tool representing one agent within a wider collaborative enactment framework, based on the definition of the form, roles and responsibilities of an existing major emergency response organisation, LESLP. While no evaluation of the system has been attempted and, in many respects, the semantic web is still in its infancy, we hope that this system and its description present a compelling case for the role that technologies of the sort discussed here could come to play in assisting in dynamic situations where access to the ‘right’ information is crucial.
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A Survey on Domain Relevant Web-Page Recommendation Using Ontology and Web Usage Knowledge

A Survey on Domain Relevant Web-Page Recommendation Using Ontology and Web Usage Knowledge

A. Bose, K. Beemanapalli, J. srivastava and s. Sahar in 2006 proposed a novel technique to incorporate the conceptual characteristics of a website into a usage-based recommendation model. We use a framework based on biological sequence alignment. Similarity scores play a crucial role in such a construction and introduce a scoring system that is generated from the website’s concept hierarchy. These scores fit seamlessly with other quantities used in similarity calculation like browsing order and time spent on a page. Additionally they demonstrate a simple, extensible system for assimilating more domain knowledge. They have focused on using the extracted patterns to predict the next user request during an online session with the website. Such systems are called Recommender Systems and are useful tools to predict user requests. This predictive ability has application in areas like pre-fetching of pages, increase in overall usability of the website. Recommendation models based only on usage information are inherently incomplete because they neglect domain knowledge. Better predictions can be made by modeling and incorporating context dependent information: concept hierarchy, link structure and semantic classification allow us to do so. This study described a method to combine usage information and domain knowledge based on ideas from bioinformatics and information retrieval. Investigate similarity calculations that use information content values weighted by context that could provide better estimates for similarity. There is a substantial scope for improvement in the ranking of recommendations: domain knowledge can again be used along with the local sequence alignment.
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ISSN: 2321-8363 UGC Approved Journal Impact Factor: 5.515

ISSN: 2321-8363 UGC Approved Journal Impact Factor: 5.515

Abstract— In previous years we have seen an amplified interest in recommender systems. In spite of significant progress in this field, there still remain several avenues to explore. Indeed, this paper provides a study of exploiting online travel information for Customized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To that end, in this paper, we first examine the characteristics of the prevailing travel packages and develop a tourist-area-season topic (TAST) model. This TAST model can represent travel packages in detail and tourists by different topic distributions, where the topic withdrawal is conditioned on both the tourists and the basic features (i.e., locations, travel seasons) of the landscapes. Then, based on this topic model representation, we propose a Semantic Web site roach to generate the lists for Customized travel package recommendation. Furthermore, we also extend the TAST model to the tourist-relation- area-season topic (TRAST) model for capturing the latent interaction among the tourists in each travel group. Finally, we evaluate the TAST model, the TRAST model, and the Semantic recommendation Web site roach on the real-world travel package data. Experimental results show that the TAST model can effectually capture the unique characteristics of the travel data and the Semantic Web site roach is, thus, much more effective than traditional recommendation techniques for travel package recommendation. Also, by considering tourist interaction, the TRAST model can be used as an effective assessment for travel group formation.
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SemAware: An Ontology-Based Web Recommendation System

SemAware: An Ontology-Based Web Recommendation System

with an expert domain ontology is used to conduct experiments and find the percentage of resulting frequent patterns which match the patterns of the case of non-semantics- aware mining. In this case, we were able to construct a sequence database from the publicly available MovieLens dataset 1 . The semantic distance matrix is constructed from the publicly available movie ontology MO-Movie Ontology [Bouza, 2010]. The dataset was mined by the semantics-aware PLWAP-sem algorithm. Figure 5.3 shows the percentages of frequent patterns that match the case of non-semantics-aware PLWAP mining of the same dataset, in two different cases. The first case (dotted line in the figure, refer to it as Case-1) represents results when using PLWAP-sem with a shallow ontology. That is, the semantic distance is computed using only is-a relations by counting the number of separating edges between ontology concepts. The second case (the solid line in the figure, refer to it as Case- 2) shows the percentages when the proposed semantic distance measure in Equation (4.1) is used, this is the measure that incorporates all semantic relations and relation weights. This should support the thesis hypothesis in Section 3.2 that the use of domain ontology adds to the effectiveness of the algorithms and enhances scalability, in that, Figure 5.3 shows that in Case-2 the resulting frequent patterns match those of the real user behavior (found by using PLWAP without semantics) to a large extent (90%-100%) for early values of the allowed maximum semantic distance, i.e. η ≥ 7. Thus, allowing for more pruning to take place while maintaining a good match. On the other hand, pruning does take place in Case-2, but matches start to appear later, for values of η ≥ 10. This means that the proposed semantic distance measure in Equation (4.1) provides for effective pruning that is sensitive to actual user behavior, i.e., more matching patterns while having low values for η.
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Semantic Web and Knowledge Management in eHealth Care System

Semantic Web and Knowledge Management in eHealth Care System

We have simulated our system in Java. We implemented and tested with a system configuration on Intel Dual Core processor, Windows XP and using Netbeans 7.0. We have used the following modules in our implementation part. The details of each module for this system are as follows. We have implemented and tested with the 5 moudles. They are: PHR Owner Module,Attribute based Access Policy Module, Data confidentiality Module,Search Engine and Verify Files. We describe the each module in details now as follows. In the first module, the main goal of our framework is to provide secure patient-centric PHR access and efficient key management at the same time. The key idea is to divide the system into multiple security domains (namely, public domains (PUDs) and personal domains (PSDs)) according to the different users’ data access requirements. The PUDs consist of users who make access based on their professional roles, such as doctors, nurses and medical researchers. In practice, a PUD can be mapped to an independent sector in the society, such as the health care, government or insurance sector. For each PSD, its users are personally associated with a data owner (such as family members or close friends), and they make accesses to PHRs based on access rights assigned by the owner. Each data owner (e.g., patient) is a trusted authority of her own PSD, who uses a KP-ABE system to manage the secret keys and access rights of users in her PSD. Since the users are personally known by the PHR owner, to realize patient-centric access, the owner is at the best position to grant user access privileges on a case-by-case basis. For PSD, data attributes are defined which refer to the intrinsic properties of the PHR data, such as the category of a PHR file. For the purpose of PSD access, each PHR file is labeled with its data attributes, while the key size is only linear with the number of file categories a user can access. Since the number of users in a PSD is often small, it reduces the burden for the owner. When encrypting the data for PSD, all that the owner needs to know is the intrinsic data properties. In the second module, we provide security using Attribute based encryption technique. In our framework, there are multiple SDs, multiple owners, multiple AAs, and multiple users. In addition, two ABE systems are involved. We term the users having read and write access as data readers and contributors, respectively. In the third module,
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Research on construction of natural language processing system based on semantic web ontology

Research on construction of natural language processing system based on semantic web ontology

(2) If this is a good exercise, so the tree with the general should be a good bridge between them. Therefore, MACBETH uses a new description and the tree in order to store for future use. Practice mentioned precedents explain part of the template generation reunion, because the new description can include many model, so it is called Reunion (recollection), which means to use the new method to collect the knowledge, or knowledge is to collect. Using Macbeth and Greed to practice as a simple example of precedent, and it is precedent and practice by the reunion. For the grammar G = (VT, VN, S, R), if S=*=> is called alpha, alpha is a sentence. Containing only terminator good sentence is a sentence. Grammar G generated sentences all is a language, it will be denoted as L (G), as in equation (3) [6].
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A Survey on a Semantic-based Friend Recommendation Systems for Social Networks

A Survey on a Semantic-based Friend Recommendation Systems for Social Networks

ABSTRACT: Already in social networking services recommend friends to users based on their social graphs, which may not be themost appropriate to reflect a user’s preferences on friendselection in real life. In this paper, we present Friendbook, a novel semantic-based friend recommendation system for social networks, which recommends friends to usersbased on their life styles instead of social graphs. By takingadvantage of sensor-rich smartphones, Friendbook discovers life styles of users from user-centric sensor data, measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. Inspired by text mining, we model a user’s daily life as life documents, from which his/her life styles are extracted by using the Latent Dirichlet Allocation algorithm.We further propose a similarity metric to measure thesimilarity of life styles between users, and calculate users’ impact in terms of life styles with a friend-matching graph. Upon receiving a request, Friendbook returns a list of people with highest recommendation scores to the query user. Finally, Friendbook integrates a feedback mechanism to further improve the recommendation accuracy. We have implemented Friendbook on the Android-based smartphones, and evaluated its performance on both small-scale experiments and large-scale simulations.
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Web Page Prediction System Based on Web Logs and Web Domain Using Cluster Technique

Web Page Prediction System Based on Web Logs and Web Domain Using Cluster Technique

The objective of web recommender system is to effectively predict web page or pages that are visited from a given web-page of a website. The performance of these approaches depends on the sizes of present datasets. The bigger the training dataset size is, the higher the prediction accuracy is. In this, system approach is based on web access sequence learnt from web usage data. Therefore, the predicted pages are limited within the discovered Web access sequences, i.e., if a user is visiting a Web-page that is not in the discovered Web access sequence, then these approaches cannot offer any recommendations to this user. This problem is called as new-page problem. The semantic-enhanced approaches are effective to overcome this problem.
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Semantic Information and Web based Product Recommendation System – A Novel Approach

Semantic Information and Web based Product Recommendation System – A Novel Approach

This paper has used consumer electronics oriented dataset which consists of 1000 sessions, 150 users, 120 products and 30 classes. The evaluation method of this work is based on the techniques introduced in [15] which define 3 parameters accuracy, precision and F1 Metric. The effectiveness of the recommendation is measured in terms of coverage and precision. 10-fold cross validation is performed for each of the data sets. Each transaction t in the test set is divided into two parts. The first part is the first n items in t for recommendation generation. n is called the window count. The other part, which is denoted as eval, is the remaining portion of t to evaluate the recommendation. Once the recommendation phase produces a set of products, which is denoted as Rec, the set is compared with eval products. This paper splits the session log to 90 % and 10 % at the last. For the users in the 10 % list, this paper finds the recommendation and matching to the recommendation already bought now.
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