Search

Due to the daily expansion of the web, the amount of information has increased significantly. Thus, the need for retrieving relevant information has also increased. In order to explore the internet, users depend on various search engines. Search engines face a significant challenge in returning the most relevant results for a user's query. The search engine's performance is determined by the algorithm used to rank web pages, which prioritizes the pages with the most relevancy to appear at the top of the result page. In this paper, various web page ranking algorithms such as Page Rank, Time Rank, EigenRumor, Distance Rank, SimRank, etc. are analyzed and compared based on some parameters, including the mining technique to which the algorithm belongs (for instance, Web Content Mining, Web Structure Mining, and Web Usage Mining), the methodology used for ranking web pages, time complexity (amount of time to run an algorithm), input parameters (parameters utilized in the ranking process such as InLink, OutLink, Tag name, Keyword, etc.), and the result relevancy to the user query.

[EN] The main content of a webpage is often surrounded by other boilerplate elements related to the template, such as menus, advertisements, copyright notices, and comments. For crawlers and indexers, isolating the main content from the template and other noisy information is an essential task, because processing and storing noisy information produce a waste of resources such as bandwidth, storage space, and computing time. Besides, the detection and extraction of the main content is useful in different areas, such as data mining, web summarization, and content adaptation to low resolutions. This work introduces a new technique for main content extraction. In contrast to most techniques, this technique not only extracts text, but also other types of content, such as images, and animations. It is a Document Object Model-based page-level technique, thus it only needs to load one single webpage to extract the main content. As a consequence, it is efficient enough as to be used online (in real-time). We have empirically evaluated the technique using a suite of real heterogeneous benchmarks producing very good results compared with other well-known content extraction techniques., This work has been partially supported by the EU (FEDER) and the Spanish MCI/AEI under grants TIN2016-76843-C4-1-R and PID2019-104735RB-C41, by the Generalitat Valenciana under grant Prometeo/2019/098 (DeepTrust), and by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215.

With the rapidly growing amount of information on the internet, real-time system is one of the key strategies to cope with the information overload and to help users in finding highly relevant information. Real-time events and domain-specific information are important knowledge base references on the Web that frequently accessed by millions of users. Real-time system is a vital to product and a technique must resolve the context of challenges to be more reliable, e.g. short data life-cycles, heterogeneous user interests, strict time constraints, and context-dependent article relevance. Since real-time data have only a short time to live, real-time models have to be continuously adapted, ensuring that real-time data are always up-to-date. The focal point of this manuscript is for designing a real-time web search approach that aggregates several web search algorithms at query time to tune search results for relevancy. We learn a context-aware delegation algorithm that allows choosing the best real-time algorithms for each query request. The evaluation showed that the proposed approach outperforms the traditional models, in which it allows us to adapt the specific properties of the considered real-time resources. In the experiments, we found that it is highly relevant for most recently searched queries, consistent in its performance, and resilient to the drawbacks faced by other algorithms

How to integrate multimodal information sources for face naming in multimodal news is a hot and yet challenging problem. A novel deep cross-modal face naming scheme is developed in this paper to facilitate more effective people news retrieval for large-scale multimodal news. This scheme integrates deep multimodal analysis, cross-modal correlation learning, and multimodal information mining, in which the efficient naming mechanism aims to cluster the deep features of different modalities into a common space to explore their inter-related correlations, and a special Web mining pattern is designed to optimize the name-face matching for rare non-celebrity. Such a cross-modal face naming model can be treated as a problem of bi-media semantic mapping and modeled as an inter-related correlation distribution over deep representations of multimodal news, in which the most important is to create more effective cross-modal name-face correlation and measure to what degree they are correlated. The experiments on a large number of public data from Yahoo! News have obtained very positive results and demonstrated the effectiveness of the proposed model.

Web siteleri, kurumsal ya da bireysel açıdan hitap edilen kitleyle ilk temasın sağlandığı bir etkileşim aracıdır. Bu araç, yoğun bir bilgi erişim ve ziyaret trafiğinin bulunduğu süreçlerde, kullanıcı davranışlarındaki farklı desenlerin tespit edilebileceği önemli bir potansiyeli içinde barındırmaktadır. Bu desenler, kullanıcı ihtiyaçlarının daha belirginleştirilmesi ve site geliştiricilerinin bu ihtiyaçlar doğrultusunda güncellemeler yapabilmesi açısından oldukça kritik görevler üstlenebilir. Bu çalışmanın amacı, dünya genelinde yaşanan Covid-19 pandemisinin ülkemizde etkinliğini arttırdığı süreçte, Kırklareli Üniversitesi web sitelerindeki kullanıcıların bilgi erişim ihtiyaçlarındaki değişimin belirlenmesidir. Bu amaç doğrultusunda, kullanıcıların bilgi erişim ve ziyaret davranışları, apriori algoritmasıyla bağımsız ve birlikte olacak şekilde incelenerek, aralarındaki ilişkilerin ortaya çıkarılması hedeflenmiştir. Bilgi erişim kavramı açısından çalışma sonuçları, kullanıcıların “tez yazımı”na yönelik çeşitli arama terimleriyle bilgi ihtiyaçlarını karşılamaya çalıştıklarını göstermiştir. Bu sonuç, özellikle lisansüstü öğrencilerin ilgili süreçte aktif olduklarına işaret etmektedir. Ziyaret davranışları açısından, “uzaktan eğitim”, “koronavirüs” ve “tatil” temalı sayfaların ağırlıklı olarak ziyaret edildiği ortaya çıkmıştır. Bilgi erişim davranışları sonrasında sergilenen ziyaret davranışları açısındansa, “tez yazımı”, “tatil” ve “eğitim öğretimin ertelenmesi” temalı ziyaretlerin birliktelikleri göze çarpmıştır. Çalışma sonucunda ortaya çıkarılmış olan davranış desenleri ve bu desenlerden nasıl faydalanılabileceğine yönelik öneriler çalışma kapsamında detaylı bir şekilde açıklanmıştır.

The rapid increase in web sites has created large volumes of data in web environment, which generates a problem of how to extract and gain useful knowledge from such data. Extracting such knowledge helps to discover, understand and predict user behaviors based on his interaction with the website. This paper introduces two neural network based systems, one for web visitor recognition according to their web logs pattern, and the other for web visitor classification according to the visited pages. This will introduce rapid services and save user time with web through a database connected to these neural networks Such system can be used to improve efficiency and effectiveness in searching for information on the web. Complete architecture of the networks is given based on supervised and unsupervised learning paradigms. Experiments have been carried out in order to validate this approach.

With the evolution in technology huge amount of data is being generated, and extracts the necessary data from large volumes of data. This process is significantly complex. Generally the web contains bulk of raw data and the process of converting this data to information mining process can be performed. At whatever point the user places some inquiry on particular web search tool, outcomes are produced with respect to the requests which are dependent on the magnitude of the document created via web information retrieval tools. The results are obtained using calculations and implementation of well written algorithms. Well known web search tools like Google and other varied engines contain their specific manner to compute the page rank, various outcomes are obtained on various web crawlers for a same inquiry because the method for deciding the importance of the sites contrasts among number of algorithm. In this research, an attempt to analyze well-known page ranking calculation on the basis of their quality and shortcomings. This paper places the light on a portion of the extremely mainstream ranking algorithm and attempts to discover a better arrangement that can optimize the time spent on looking through the list of sites.

Nowadays, internet has become the easiest way to obtain more information from the web and millions of users search internet to find out the information. The continuous growth of web pages and users interest to search more information about various topics increases the complexity of recommendation. The user's behavior is extracted by using the web mining techniques, which are used in web server log. The main aim of this research study is to identify the navigation pattern of users from the log files. There are three major steps in the web mining process namely pre-processing the data, classification of pattern and users discovery. In recent periods, the web page articles are classified by the researchers before recommending the requested page to users. However, every category size is too large or manual labors are often needed for classification tasks. A high time complexity issues are faced by some existing clustering methods or according to the initial parameters, these techniques provides the iterative computing that leads to insufficient results. To address the above issues, a recommendation for web page is developed by initializing the margin parameters of classification techniques which considers both effectiveness and efficiency. This research work initializes the Random Forest's (RF) margin parameters by using the FireFly Algorithm (FFA) for reducing the processing time to speed up the process. A large volume of user's interest data is processed by these margin parameters, which provides a better recommendation than existing techniques. The experimental results show that RF-FFA method achieved 41.89% accuracy and recall values, when compared with other heuristic algorithms.

Semantic Web content extracting are the augmentation of the present web where the data is given in the better importance and allowing users to work close by close by utilizing the assets of web. Due to expanding of web content at speedier rate, it is difficult to adapt up to new procedures of separating to create the exact data in light of the user query. Later, number of scientists has taking a shot at enhancing the aftereffects of Web content mining by abusing Web Semantic Structure and can be executed in the web expresses through machine-processable data to bolsters the errands of users. But these methods bring about low precision [unseemly data from different inquiry result (equivocal)] and getting of off base data (i.e. low recall) with high surfing time. Therefore, the paper proposes the Semantic Web content mining utilizing two ways to deal with dodge low precision and low recall: the one is Kalman–Bucy filters, to channel the comparable information; the next is adaptive Helmholtz machine, to get the exact data in light of the user query. The proposed Kalman–Bucy filtering approach with adaptive Helmholtz machine execution is be assessed and contrasted with existing methodologies regarding retrieval accuracy and surfing time by dual predicted output and the optimized information retrieval rather than probability.

WOS:000594426400001 Web pages contain irrelevant images along with relevant images. The classification of these images is an error-prone process due to the number of design variations of web pages. Using multiple web pages provides additional features that improve the performance of relevant image extraction. Traditional studies use the features extracted from a single web page. However, in this study, we enhance the performance of relevant image extraction by employing the features extracted from different web pages consisting of standard news, galleries, video pages, and link pages. The dataset obtained from these web pages contains 100 different web pages for each 200 online news websites from 58 different countries. For discovering relevant images, the most straightforward approach extracts the largest image on the web page. This approach achieves a 0.451 F-Measure score as a baseline. Then, we apply several machine learning methods using features in this dataset to find the most suitable machine learning method. The best f-Measure score is 0.822 using the AdaBoost classifier. Some of these features have been utilized in previous web data extraction studies. To the best of our knowledge, 15 new features are proposed for the first time in this study for discovering the relevant images. We compare the performance of the AdaBoost classifier on different feature sets. The proposed features improve the f-Measure by 35 percent. Besides, using only the cache feature, which is the most prominent feature, corresponds to 7 percent of this improvement.

Web mining plays a salient role in today’s era of an interconnected network to comprehend the manner in which users browse the websites. Web mining can be classified into three main categories such as web content mining, web structure mining, and web usage mining. Web structure mining deals with the analysis of the structure of web sites, web pages while web content mining deals with the analysis of the contents on the web pages. Web usage mining processes web server log files for the applications of various functionalities such as classification, clustering, and association rules on weblog data. In this paper, the authors presented the classification of data on the server log files by applying various popular and most widely used approaches of classification on the dataset. Then, based on the highest predictive accuracy, the random forest algorithm was further optimized using ant colony optimization (ACO) techniques and the authors have observed that the classification accuracy can be improved using ACO on the results of the random forest algorithm.

The growing importance of data science and analytics has led to innovative applications spread across various fields and genres over a short period of time. While data and the associated science are challenges in themselves, the applications using them to solve existing problems have instigated and brought to the spotlight new obstacles. To explore the new found understanding of the data middle ground between natural language and computer grammar, we propose a data science application model, Po-Miner. Po-Miner is an online poem generator based on scraped online web data. Along with syllable counts, rhymes, and other poem notions, Po-Miner induces clustering, and other data mining techniques via the Po-Select algorithm for random poem generation. Though a random poem lacks completeness and meaning, the very aspect of randomness brings in creativity and depth. Po-Miner operates on the grammar rules to generate a poem structured in a 5 × 5 grid. The paper presents the Po-Miner model, implementation, poems, analysis, and further, extends to its applicability in the security domain using the gird poem model. The poem generated by the Po-Miner can be used as an alternative and an enhancement to the one-time password system as its application. The Po-Miner appears to be a promising application that can be extended to several other domains.

Users of web site usually perform their interest oriented actions by clicking or visiting web pages, which are traced in access log files. The clustering web client’s access pattern may catch common client interest in a website. The conventional web usage mining techniques for clustering web user sessions can discover usage patterns directly, but cannot identify the latent factors or hidden relationships among users’ navigational behaviour. One of the best tasks of internet usage mining, web client grouping, which sets up clusters of clients displaying comparative browsing designs gives beneficial information to customized web benefits. This paper presents a survey covering the strategies and techniques of clustering algorithms and demanding situations appear within the area.