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4th International Conference on web Intelligence, mining and Semantics(WIMS)

作者： Alqurashi, Tahani Wang, Wenjia Univ East Anglia Sch Comp Sci Norwich Norfolk England

ISBN: (纸本)9781450325387

web structure mining is to extract knowledge from the hyperlink structure data of world wide webs for improving web design for clear content presentation and easy navigation. This paper presents a graph-based methodology for web structure mining. The structure of a website is firstly mapped onto a graph with its nodes representing web pages and links representing hyperlinks between pages and other websites. Then the characteristics of the web graph, such as, the degree of each node, density, connectivity, the closeness centralisation, and the node clusters, can be analysed quantitatively. The methodology is tested on the web structural data collected from 110 UK's university websites. After cleansing and pre-processing the data, the graphs were constructed and analysed to obtain the aforementioned properties for each web and other useful information, such as page size and the length of the optimal path as they both affect the navigability. Based on the evaluation of the properties, some guidelines and criteria are devised for quantifying the structural quality of the webs into five categories from very poor to very good. The average degree and the percentage of strongly connected component (SCC) pages together with the average distance were found to be the most important properties in determining the structural quality of a web.

关键词： web structure mining Graph mining Link Analysis

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EXTENDING JMETER TO ALLOW FOR web structure mining 5

EXTENDING JMETER TO ALLOW FOR WEB STRUCTURE MINING

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5th International Conference on Internet Technologies and Applications (ITA 13)

作者： Sabater, Agustin Guerrero, Carlos Lera, Isaac Juiz, Carlos Univ Balearic Isl Comp Sci Dept Palma De Mallorca Islas Baleares Spain

ISBN: (纸本)9780946881819

This paper describes a JMeter extension created to allow for web structure mining process. This type of mining process is very important, for example, in the field of web performance engineering and web development validations. The extension allows users to define the HTML tags that wrap the content units and it creates a graph model to represent the content units of all the web pages and the relationships among them (aggregations). The usability of the extension has been validated in a real scenario.

关键词： web data mining web structure mining JMeter web engineering

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web mining from Interpretable Compressed Representation of Sparse web

Web Mining from Interpretable Compressed Representation of S...

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IEEE/WIC/ACM International Joint Conference on web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Hryhoruk, Connor C. J. Leung, Carson K. Univ Manitoba Dept Comp Sci Winnipeg MB Canada

ISBN: (纸本)9781665494021

Large datasets often contain computational constraints when under the non-trivial extraction of implicit, previously unknown, and potentially useful information. These datasets are everywhere, with a popular example being the World Wide web. It acts as a mass data producer and consumer across multiple devices in a distributed fashion worldwide, containing massive amounts of data. The discovery of knowledge on the web requires web intelligence solutions, which take advantages of data mining and data science. In the case of web mining, the mining of web structures provides commonly recommended web pages to web surfers by examining incoming and outgoing links on web pages. The overall size of the web is however sparse. Sparsity of the web comes from a high number of vertex nodes (i.e., web pages), with a small number of directed edges (i.e., incoming and outgoing hyperlinks between web pages). In this paper, we present a solution to the mining of frequent patterns from the sparse web. From the sparsity of the web, web pages are captured in compressed bitmaps that are then mined for discovery of these patterns. Our bitmap model ensures readability, flexibility, and allows for the capturing of important information across multiple 31-bit groups. The mining process is demonstrated on real-life web data to present its capacity of mining for interesting patterns from interpretable compressed representation of sparse data.

关键词： web intelligence Intelligent agent technology Data analytics Social media World wide web web of data Data mining Data science web mining web structure mining web usage mining Data compression Bitmap Recommendation Interpretability

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A Systematic Review web Content mining Tools and its Applications Systematic Review web Content mining Tools

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第8期12卷 752-759页

作者： Pujar, Manjunath Mundada, Monica R. MS Ramaiah Inst Technol Dept Comp Sci Belagavi Karnataka India

In recent years, the emergence of WWW (World Wide web) led to the accumulation of huge amount of information and data. Hence the web is found to consist of unstructured and structured information that impacts the day to day life of the society. Because of such availability of huge information, utilization of the required information becomes more challenging. This paper provided a comprehensive survey on the current situation and recent trends on web content mining (WCM) and its applications thereby contributing to the enhancement of the upcoming research in WCM. The paper focused mainly on the mining and retrieval techniques, various WCM approaches, challenges and process of information retrieval and information extraction. The paper describes the four major tasks of web content mining that is information retrieval, information extraction, generalization and validation in detail. WCM concentrates on orchestrating, sorting, classifying, collecting, congregating of web data and provide the improved data which can be easily accessed by the users. web content mining tools were needed to scan text, images and HTML documents and provide results to the search engine. It guides the search engine to provide better productive results for every search based on their importance. The paper also analysed different web content mining tools for the extraction of relevant information from the corresponding web page.

关键词： web content mining web structure mining web usage mining data mining information retrieval information extraction

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A web Intelligence Solution to Support Recommendations from the web

A Web Intelligence Solution to Support Recommendations from ...

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IEEE/WIC/ACM International Conference on web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Leung, Carson K. Madill, Evan W. R. Singh, Sehaj P. Univ Manitoba Dept Comp Sci Winnipeg MB Canada

ISBN: (纸本)9781450391870

Big data are everywhere. World Wide web and social networks are examples of these big data. They have become a vast data production and consumption platform, at which threads of data evolve from multiple devices, by different human interactions, over worldwide locations, under divergent distributed settings. Embedded in these big web data is implicit, previously unknown and potentially useful information and knowledge that awaited to be discovered. This calls for web intelligence solutions, which make good use of data science, data mining (especially, web mining) and social network analysis to discover useful knowledge and important information from the web (e.g., web of people/things). Such a web often consists of vertices (i.e., people/things) and edges (i.e., connections among people/things). When modeling a social network as a web of people, these edges can be undirected (e.g., for mutual friendships) or directed (e.g., for capturing a social entity who is following another social entity). In this paper, we present a web intelligence solution to discover interesting knowledge (e.g., most-followed people or most-referenced web pages) from these social connections. Due to the dynamic nature of the web, vertices and/or edges may be changed (e.g., added or deleted) over time. Hence, our solution is designed in such a way that it discovers knowledge not only from a static web but also from a dynamic web. Evaluation on real-life web data demonstrates the effectiveness and practicality of our solution for discovering knowledge and supporting recommendations from the web.

关键词： web intelligence Intelligent agent technology World wide web web of people Data science Data analytics Data mining Frequent pattern mining Social network analysis Friend recommendation Market basket analysis Product recommendation Recommendation web mining web structure mining web usage mining Graph theory Linear algebra Directed graphs

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Interpretable mining of Influential Patterns from Sparse web

Interpretable Mining of Influential Patterns from Sparse Web

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IEEE/WIC/ACM International Conference on web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Hryhoruk, Connor C. J. Leung, Carson K. Univ Manitoba Dept Comp Sci Winnipeg MB Canada

ISBN: (纸本)9781450391153

Big data are everywhere. World Wide web is an example of these big data. It has become a vast data production and consumption platform, at which threads of data evolve from multiple devices, by different human interactions, over worldwide locations, under divergent distributed settings. Embedded in these big web data is implicit, previously unknown and potentially useful information and knowledge that awaited to be discovered. This calls for web intelligence solutions, which make good use of data science and data mining (especially, web mining) to discover useful knowledge and important information from the web. As a web mining task, web structure mining aims to examine incoming and outgoing links on web pages and make recommendations of frequently referenced web pages to web surfers. As another web mining task, web usage mining aims to examine web surfer patterns and make recommendations of frequently visited pages to web surfers. While the size of the web is huge, the connection among all web pages may be sparse. In other words, the number of vertex nodes (i.e., web pages) on the web is huge, the number of directed edges (i.e., incoming and outgoing hyperlinks between web pages) may be small. This leads to a sparse web. In this paper, we present a solution for interpretable mining of influential patterns from sparse web. In particular, we represent web structure and usage information by bitmaps to capture connections to web pages. Due to the sparsity of the web, we compress the bitmaps, and use them in mining influential patterns (e.g., popular web pages). For explainability of the mining process, we ensure the compressed bitmaps are interpretable. Evaluation on real-life web data demonstrates the effectiveness, interpretability and practicality of our solution for interpretable mining of influential patterns from sparse web.

关键词： web intelligence Intelligent agent technology World wide web web of data Data science Data analytics Data mining Frequent pattern mining web mining web structure mining web usage mining Data compression Bitmap Recommendation Explainability Interpretability

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website structure Improvement Based on the Combination of Selected web structure and web Usage mining Methods

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2018年第6期17卷 1743-1776页

作者： Kapusta, Jozef Munk, Michal Drlik, Martin Constantine Philosopher Univ Nitra Nitra Slovakia

The different web mining methods and techniques can help to solve some typical issues of the contemporary websites, contribute to more effective personalization, improve a website structure and reorganize its web pages. However, only several papers tried to combine web structure and web usage mining (WUM) methods with this aim. The paper researches if and how the combination of selected web structure and WUM methods can identify misplaced web pages and how they can contribute to improving the website structure. The paper analyzes the relationship between the estimated importance of the web page from the web page creator's point of view using the web structure mining method based on PageRank and visitors' real perception of the importance of that individual web page using the WUM method based on sequence patterns analysis, which eliminates the problem with repeated visits of the same web page during one session. The results prove that the expected probability of accesses to the individual web page correlates with the observed visit rate obtained from the log files using the WUM method. Furthermore, the website can be improved based on the consequent application of the residual analysis on the obtained results. The applicability of the proposed combination of the web structure and WUM methods is presented on two case studies from different application domains of the contemporary web. As a result, the web pages, which are underestimated or overestimated by the web page creators, are successfully identified in both cases.

关键词： web usage mining web structure mining PageRank sequence rules analysis website improvement website reorganization

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An Improved PageRank Algorithm Based on Reachability to Reduce Mutual Reinforcement Effect 10

An Improved PageRank Algorithm Based on Reachability to Redu...

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10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Sardhara, Rinkal Lakhataria, Kamaljit I. LJ Inst Comp Applicat Comp Applicat Ahmadabad Gujarat India Gujarat Univ Dept Comp Sci Ahmadabad Gujarat India

ISBN: (纸本)9781538659069

It is very difficult to find relevant information in this rapidly growing hyper structure. Basically web site owner creates web site to provide relevant information to cater user's need. Therefore, it is very important to retrieve web pages based on user's interest and behavior by finding the content of the web page. Link analysis is the significant parameter to find related information. PageRank and HITS are two basic algorithms which works on web structure mining. Numerous algorithms have been developed to improve performance based on these two basic algorithms. An Improved PageRank Algorithm (IPRA), is the extension of the page Rank algorithm, is introduced in this paper. IPRA takes into account the number of different domain inlink and outlink and distributes the rank score based on the reachability of the web pages. The result of our model studies shows that IPRA algorithms give better result than the standard PageRank algorithm.

关键词： web minig web structure mining PageRank Link Analysis Weighted Page Rank Improved PageRank Algorithm

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20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2019)

作者： Omasa, Asuka Inoue, Ushio Tokyo Denki Univ Grad Sch Engn Tokyo Japan

ISBN: (纸本)9781728116518

Wikipedia has been used as a corpus for research on natural language processing due to a huge number of articles and a high comprehensiveness of its vocabulary. The purpose of this research is to measure the semantic relatedness between two concepts covered by Wikipedia articles. The hyperlink structure among articles is focused because a link from an article to another article indicates an explicit relationship between them. However, the cost of analyzing links may be a problem if the article links to or linked from a lot of articles. A graph database system enables quick and efficient access to related data without handling unrelated data, because it stores data with predefined relationships among data. This paper proposes a method for extracting related concepts from Wikipedia by using a graph database system.

关键词： semantic relatedness graph databases Wikipedia web structure mining

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Impact of Different Domain Inlink, Outlink and Rechability on Relevance of web Page Using Correlation

Impact of Different Domain Inlink, Outlink and Rechability o...

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International Conference on Intelligent Computing and Control Systems (ICCS)

作者： Sardhara, Rinkal Iakhataria, Kamaljit I. LJ Inst Comp Applicat Comp Applicat Ahmadabad Gujarat India Gujarat Univ Dept Comp Sci Ahmadabad Gujarat India

ISBN: (纸本)9781538681138

web site users are increasing promptly and because of that web structure mining becomes more difficult. web structure mining plays a big role in search engine optimization. There are many algorithms which mines the web structures. HITS and PageRank are two basic algorithms which works on web structure mining. Other algorithm uses these algorithms and added different parameters like bookmarked users, number of visits, topic, anchor text, popularity and content to calculate web page score. In this paper we introduce new parameters, inlink, outlink and reachability which helps to reduce mutual reinforcement effect on web page ranking. We collected data set on "abortion" keyword. We analyzed that data set and generated score for reachability and inlink and outlink from different domain. We have observed that data set and found that there is a positive correlation between these parameters and relativity of web page. We used Pearson correlation technique to find correlation co-efficient.

关键词： web structure mining PageRank HITS Mutual reinforcement link analysis correlation

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