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In intelligent web system web-page prediction plays a big role. People uses websites for various purpose like for study, education and entertainment point of view and access websites for online shopping purpose. Each website contains multiple WebPages. The proposed system consists of two models to provide better web page recommendation. The ?rst model use an ontology to represent the domain knowledge. The second model uses semantic network to represent domain terms, Web-pages, and the relations between them.
Keywords- Web-page recommendation, domain ontology, semantic network.
The web recommender system is use to predict web page or pages that are visited from a given web-page of a website. Web-page recommendation is shown as links to most viewed pages at websites or related books and stories.

When a user searches a website, a order of visited Web-pages during a session (the period from starting, to existing the browser by the user) can be produced. This order is organized into a Web session S = d1d2 . . . dk, where di is the page ID of the ith visited Web-page by the user.

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The growth of World Wide Web means internet results in high demand. But it is necessary to provide better required information to a user. This paper proposes a novel method to efficiently provide better Web-page recommendation through semantic-enhancement by integrating the domain and Web usage knowledge of a website. Two new models are proposed to represent the domain knowledge. The first model uses an ontology to represent the domain knowledge. The second model uses one automatically generated semantic network to represent domain terms, Web-pages, and the relations between them.

The classical systems failed to recommends the newly added pages or the products to the visitors since these pages or products are not in the current common navigation pro?les. So, to overcome the New page Problem the common navigation pro?le can be extracted in terms of semantic meaning or information. For that particular purpose ontology should be used. But in classical systems ontology was not used. Another problem is clustering in the existing systems. In this clustering number of recommended pages get increased. Non Useful or Unrelated pages or links are recommended which user never prefers.
The comparison of two sequences to determine their similarity is one of the fundamental problems in pattern matching technique. The Longest Common Subsequence method generates a sequence or a list of recommended products to the user. It is useful for the latest trend shopping that is online shopping purpose but what about the other users who does not use such online shopping websites but they still want the personalization in their web page recommendation system. The Numerical or numbering features should be represented by single components of vectors representing items. These components hold the exact value of that feature. Unfortunately, classi?ers of all types tend to take a long time to construct. For example, if wish to use decision trees, need one tree per user. Constructing a tree not only requires that look at all the item pro?les, but the problem is have to consider many different predicates, that could involve complex combinations of features. Hence, this approach tends to be used only for relatively small problem sizes.
The navigation of the users is largely driven by the semantics. Every time while user is searching he actually aims at ?nding some information concerning a particular di?erent subject. There are many methods to extract keywords that characterize the web content. Which should be the exact matching between the terms determines the similarity between documents. Previous systems use this approach to ?nd the similarity between documents. But by using this approach, only the binary similarity is achieved. That means no actual semantic similarity is considered. Semantic similarity is very important as far as similarity between documents is considered. Many number of research approaches integrate other information sources. require more abstract representation which enables a more ?exible and uniform document matching process. It uses the semantic web structures, such as ontology. Early systems possess a very common problem of caching of web pages. When a web user searches for an already cached page, this action is not recorded in the web sites log.
propose a mathematical programming model to improve the user navigation on a website while minimizing alterations to its current structure. Results from extensive tests conducted on a publicly available real data set indicate that our model not only signi?cantly improves the user navigation with very few changes, but also can be e?ectively solved. In addition, we de?ne two evaluation metrics and use them to assess the performance of the improved website using the real data set. Evaluation results con?rm that the user navigation on the improved structure is indeed greatly enhanced. More interestingly, ?nd that heavily disoriented users are more likely to bene?t from the improved structure than the less disoriented users.
In the connection of Web-page proposal, the info information is Web logs that record client sessions on a day by day premise. The client sessions incorporate data about clients’ Webpage route exercises. Every Web-page has a title, which contains the magic words that grasp the semantics of the Web-page. In view of these certainties, mean to ?nd space learning from the titles of went to Web-pages at a web- website and speak to the found information in an area philosophy to help powerful Web-page proposal. A space philosophy is characterized as a theoretical model that points out the terms and connections between them expressly and formally, which thus speak to the area information for a particular space. The three principle segments are recorded as takes after: Domain terms (ideas), Relationships between the terms (ideas), and Features of the terms and connections.
Algorithm for Domain Ontology
Step 1-Collect The Domain Terms: collect the Web log record from the Web server of the site for a time of time run a preprocessing unit to dissect the Web log document and produce a list of URLs of Web-pages that were gotten to by clients, run a product specialists to slither all the Webpages in the URL rundown to concentrate the titles, and apply a calculation to concentrate terms from the recovered titles.
Step 2-Define the Terms: In this steps the Domain Concept Will be defined based on extracted terms. In this step, the area ideas will be characterized for the given site in light of the removed terms. show the MS web webpage as a sample. This site concentrare on the application programming, for example, MS Office, Windows Operating System, and Database.
Step 3-De?ne Taxonomic And Non Taxonomic relation: There are three conceivable ways to add to the taxonomic connection boats, for example, a top-down advancement methodology begins from the most general ideas in the area and after that distinguishes the ensuing specialization of the general concepts, a base up improvement process begins from the most particular ideas as the leave hubs in the concept various leveled structure tree structure, then gatherings these most particular ideas into more general ideas, a crossover advancement procedure is the mix of the topdown and base up methodologies. recognize the center concepts in the area ?rst and afterward sum up and practice them ?ttingly. The non taxomonic connections can be the connection boat sorts utilized as a part of a social database aside from the connections between a superset and a subset, for example, referencing toward oneself, 1-M and M-N connections. In the MS webwebsite case, the fundamental sorts of nontaxonomic connections are recorded as beneath. The “gives” connection depicts the M:N connection send between idea Manufacturer and ideas Product, Solution, Support, and News. The ‘is Provided’ connection is the converse of the “gives” connection. The “has” connection portrays the M:N relationship between idea Application and ideas Product, Solution, Support, and News. The “is Applied For” relation is the opposite of the “has” connection. The “has Page” connection portrays the M:N connection deliver between an idea, for example, Application and product.
The second model, i.e. new semantic net of a website, is generated which is a graph of concepts representing domain terms, Web-pages, and relations enclosing the collocations of domain terms, and the associations between domain terms and Webpages. Initially, the domain terms are gathered from the Web-page titles based on the assumption that a generally composed Webpage ought to have an instructive title; then the relations among these terms are extracted from the accompanying two perspectives.
Step 1- Collection Of Visited Web-Pages Titles: To collect the titles, collect the Web log le from the Web server of the website for a certain length of time, run a pre-processing unit that examines the Web log le thus producing a list of URLs of Webpages that were approached by users, and run a software agent to crawl all the Web-pages in the list to extract the titles.
Step 2-Extraction Of Sequence Terms: The algorithm used in the domain ontology construction is applied to extract the terms from the retrieved titles. The extracted terms are presented in the order as they appear in each title, namely they are collectedas sequence terms.
Step 3- Build The Semantic Net Semnetweb: In SemNetWeb, each node represents a term in the extracted sequence terms and the order of sequence determines the from- Instance and to Instance relations of a term between other terms.

Step 4- Implemention of automatic construction of SemNetWeb: The SemNetWeb is incorporated in RDF to enable reusability and liability of the domain term network by other parts of a Web-page recommendation system. The table shows the algorithm to automatically construct a SemNetWeb FVTP (Frequently Viewed Term Patterns) FWAP (Frequent Web Access Patterns) CPM (Conceptual Prediction Model).
V. Conclusion
Ontology based learning and domain knowledge extraction is used to perform better enhancement in web page
recommendation system. A number of Web-page recommendation strategies have been proposed to predict next Webpage requests of users through querying the knowledge bases. The experimental results are promising and are indicative of the usefulness of the proposed models.
For the future work, a key information extraction algorithm will be developed to Compare with the term extraction method in this work, and will perform intense comparisons with the existing semantic Webpage recommendation systems.

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Abstract- In intelligent web system web-page prediction plays a big role. (2019, Apr 28). Retrieved January 24, 2021, from

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