Web Mining

3
rd International Conference on Data Mining & Machine Learning (DMML 2022) will act as
a major forum for the presentation of innovative ideas, approaches, developments, and research
projects in the areas of Data Mining and Machine Learning. It will also serve to facilitate the
exchange of information between researchers and industry professionals to discuss the latest issues
and advancement in the area of Big Data and Machine Learning.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
Data Mining and Machine Learning.

Today it is almost impossible to retrieve information with a keyword search when the information is spread over several pages. The Semantic Web is an extension of the current web in which information is given well-defined meaning. Web personalization is the one application of semantic web usage mining. In this report we have explored comparison of various collaborative filtering techniques. Those techniques are memory based, model based and hybrid collaborative filtering. Our study shows that the performance of hybrid collaborative filtering technique is better than memory based and model based collaborative filtering technique. We have introduced normalization step, which will improve accuracy of traditional collaborative filtering techniques.

In today’s world, various tools and technologies have been used in learning. The rapid and constant pace of change in technology increases opportunity for students. The opportunities include greater access to rich, multimedia content, the widespread availability of mobile computing devices, the expanding role of social networking tools for learning and professional development, and the growing interest in the power of digital games for more personalized learning. According to the National School Boards Association, students who are exposed to a high volume of technology perform as well as expected on standardized test [1]. Various Multi-National Companies like Google and Apple are introducing new Educational Apps which combines interactive technology with educational materials that has been proven to help accelerate learning and promote more innovative methods of retention than a simple textbook and a set of cue cards could ever do. In this paper, we would like to emphasize the importance of Educational Apps in class room teaching. Specific topics addressed include: (a) Various Educational Apps and its use inside the classroom (b) Universities promoting the use of Educational Apps (c) Challenges and Disadvantages in using Educational Apps (d) Directions for future research.
Keywords— Educational Technology Tools, Technology and learning, Research designs and trends, Impact of technology on learning, Educational Apps, Google Apps, IPAD App.

This paper introduces a new method for the session construction problem, which is the first main step of the web usage mining process. The proposed method is capable of extracting all possible maximal navigation sequences of web users. Through experiments, it is shown that when our new technique is used, it outperforms previous approaches in web usage mining applications such as next-page prediction.

With increasingly higher numbers of non-English language web searchers the problems of efficient handling of non-English Web documents and user queries are becoming major issues for search engines. The main aim of this review paper is to make researchers aware of the existing problems in monolingual non-English Web retrieval by providing an overview of open issues. A significant number of

Graphical user interfaces design in software development process focuses on maximizing usability and the user's experience, in order to make the interaction for users easy, flexible and efficient. In this paper, we propose an approach for evaluating the usability satisfaction degree of a web-based system. The proposed method has been accomplished in two phases and implemented on an airlines website as a case study. In the first phase, a website usability test is implemented by a number of users, and then the results obtained are translated into charts for a final web-based system evaluation in the second phase. The results achieved were satisfactory, since the places where the weaknesses and gaps in the website are identified and recommended solutions to avoid them are drawn. The authenticity of the results have been confirmed by comparing them with user opinions acquired from a questionnaire, which proves the precision in which the website is rated.

In order to automatically provide the most appropriate learning objects to e-learners, a special interest should be given to the process of building the learners' models. First, we need to identify which relevant information to include in the learner's model, while taking into account various pedagogical considerations, and second how to accurately infer the learners' preferences and characteristics from their online behavior and activities. This paper presents a preliminary study of the possibility of integrating educational preferences in the learner's model and detecting them automatically within e-learning systems.

Named Entities Recognition (NER) has become one of the major issues in Information Retrieval (IR), knowledge extraction, and document classification. This paper addresses a particular case of NER, acronym expansion (or definition) when this expansion does not exist in the document using the acronym. Since acronyms may obviously expand into several distinct sets of words, this paper provides nine quality measures of the relevant definition prediction based on mutual information (MI), cubic MI (MI3), and Dice's coefficient. A combinaison of these statistical measures with the cosine approach is proposed. Experiments have been run on biomedical domain where acronyms are numerous. The results on our biomedical corpus showed that the proposed measures were accurate devices to predict relevant definitions. Povzetek: Predstavljene so metode spletnega preiskovanja dvoumnih akronimov v domeni biomedicinskih baz.

Data preprocessing is considered as an important phase of Web usage mining due to unstructured, heterogeneous and noisy nature of log data. Complete and e ective data pre- processing insures the eciency and scalability of algorithms used in pattern discovery phase of Web usage mining. Data preprocessing generally includes the steps- Data fusion, Data cleaning, User identi cation, Session identi cation, Path com- pletion etc. Data cleaning is the initial and important step in preprocessing to extract cleaned data for further process- ing. It is important to apply data extraction before data cleaning on raw log data in analysis of speci c time-duration i.e. one day, one week or one month etc. In this paper we have mainly focused on data fusion, data extraction and data cleaning steps of preprocessing and proposed an algo- rithm for data extraction which extracts log data according to analysis of time duration. This algorithm also sorts log entries according to their date and time which will be further used in prediction of browsing sequence of user. After that we have applied data cleaning algorithm on extracted real Web server log. In data cleaning almost all irrelevant les, irrelevant HTTP methods and wrong HTTP status codes are considered and after experiment it is analyzed that raw log data reduces to almost 80% which shows the importance of initial phases of data preprocessing.

In this paper, we present an overview of research issues in web mining. We discuss mining with respect to web data referred here as web data mining. In particular, our focus is on web data mining research in context of our web warehousing project called WHOWEDA (Warehouse of Web Data). We have categorized web data mining into threes areas; web content mining, web structure mining and web usage mining. We have highlighted and discussed various research issues involved in each of these web data mining category. We believe that web data mining will be the topic of exploratory research in near future.

Filtering the immense amount of data available electronically over the World Wide Web is an important task of search engines in data mining applications. Users when performing search often formulate hypotheses that they want to find supporting data for. The initial hypothesis reflects their preliminary knowledge of the subject. The final hypotheses at the end of the search reflect what

Determining similarity between web pages is a key factor for the success of many web mining applications such as recommendation systems and adaptive web sites. In this paper, we propose a new hybrid method of distributed learning automata and graph partitioning to determine similarity between web pages using the web usage data. The idea of the proposed method is that if different users request a couple of pages together, then these pages are likely to correspond to the same information needs therefore can be considered similar. In the proposed method, a learning automaton is assigned to each web page and tries to find the similarities between that page and other pages of a web site utilizing the results of a graph partitioning algorithm performed on the graph of the web site. Computer experiments show that the proposed method outperforms Hebbian algorithm and the only learning automata based method reported in the literature.

Large web search engines are now processing billions of queries per day. Most of these queries are interactive in nature, requiring a response in fractions of a second. However, there are also a number of important scenarios where large batches of queries are submitted for various web mining and system optimization tasks that do not require an immediate response. Given the significant cost of executing search queries over billions of web pages, it is a natural question to ask if such batches of queries can be more efficiently executed than interactive queries.

Web is a vast data repository. By mining from this data efficiently, we can gain valuable knowledge. Unfortunately, in addition to useful content there are also many Web documents considered harmful (e.g. pornography, terrorism, illegal drugs). Web mining that includes three main areas – content, structure, and usage mining – may help us detect and eliminate these sites. In this paper, we concentrate on applications of Web content and Web structure mining. First, we introduce a system for detection of pornographic textual Web pages. We discuss its classification methods and depict its architecture. Second, we present analysis of relations among Czech academic computer science Web sites. We give an overview of ranking algorithms and determine importance of the sites we analyzed.

رایانه ها به هنگام ظهور این وعده را دادند که به عنوان یک مخزن دانش و خرد باشند، اما در عوض حجم عظيمی از داده ها را به سوی ما روانه ساختند وب کاوی فرآیند کشف اطلاعات و دانش از داده های وب می باشد. در وب کاوی این داده ها از سمت سرور ، مشتری ، پروکسی سرور یا پایگاه داده سازمان جمع آوری می شود .  روش های وب کاوی  دسته : کاوش محتوای وب ، کاوش ساختار وب و کاوش استفاده از وب تقسيم می شود 3به . زمينه های کاریردی متعددی در وب کاوی وجود دارد از جمله تجارت الکترونيک ، متن کاوی و مدیریت رفتار مشتری .  تحقيقات در زمينه وب کاوی بيشتر بر توسعه تکنيک های  روش اصلی که 3استخراج دانش که برای تحليل داده های وب به کار می روند تمرکز دارد . برای کاوش داده ها در وب به کار می روند شامل: قوانين پيوستگی یا انجمنی ، الگوهای ترتيبی و خوشه بندی می باشند. هدف اصلی کاوش استفاده از وب، جمع آوری اطلاعات راجع به الگوهای پيمایش کاربران می باشد. البته وب کاوی با چالش ها و محدودیت های متنوعی روبه رو است. و در حال حاضر تحقيقات بسياری در زمينه وب کاوی در حال انجام است که هدف آن ها حل این مشکلات می باشد

As the number of web pages increases, search for useful information by users on web sites will become more significant. By determining the similarity of web pages, search quality can be improved; hence, users can easily find their relevant information. In this paper, distributed learning automata and probabilistic grammar were used to propose a new hybrid algorithm in order to specify the similarity of web pages by means of web usage data. In the proposed algorithm, a Learning Automata (LA) for each web page is assigned which its function is to evaluate association rules extracted by hypertext system. This learning process continues until the similarity of web pages are determined. Experimental results demonstrate the efficiency of the proposed algorithm over other existing techniques.

Due to the continuous growth and spread of the internet using Web Mining to improve the quality of different services has become a necessity. Web Mining is nothing else than applying data mining techniques and algorithms on web data. In this work we present two algorithms used in Web Structure Mining namely Page Rank and HITS. Both algorithms draw their origin from social networks analysis and they are modeled based on the Theory of Markov Chains. Page Rank is used by the search engine GOOGLE and HITS by the search engine CLEVER. We present their strengths, weakness and other areas of applicability.

By our 1 demonstration we want to introduce our achievements in combining different purpose algorithms to build a chatbot which is able to keep a conversation on any topic. It uses snippets of Internet search results to stay within a context, Nakamura's Emotion Dictionary to detect an emotional load existence and categorization of a textual utterance and a causal consequences retrieval algorithm when emotive features are not found. It is also able to detect a possibility to make a pun by analyzing the input sentence and create one if timing is adequate.

Web mining is the application of data mining techniques to extract knowledge from Web. Web mining
has-been explored to a vast degree and different techniques have been proposed for a variety of applications that
includes Web Search, Classification and Personalization etc. Most research on Web mining has been from a „datacentric‟
point of view. In this paper, we highlight the significance of studying the evolving nature of the Web
personalization. Web usage mining is used to discover interesting user navigation Patterns and can be applied to many
real-world problems, such as improving Web sites/pages, making Additional topic or product recommendations,
user/customer behavior studies, etc. A Web usage mining system performs five major tasks: i) data gathering, ii) data
preparation, iii) navigation pattern discovery, iv) pattern analysis and visualization, and v) pattern applications. Each
task is explained in detail and its related technologies are introduced. The Web mining research is a converging research
area from several research communities, such as Databases, Information Retrieval and Artificial Intelligence. In this
paper we implement how Web mining techniques can be apply for the Customization i.e. Web personalization

Data mining is the useful tool to discovering the
knowledge from  large  data.  Different  methods &  algorithms
are  available  in  data  mining.  Classification  is  most  common
method used for finding the  mine rule from the large database.
Decision  tree  method  generally  used  for  the  Classification,
because it is the simple hierarchical structure for the user
understanding  &  decision  making.  Various  data  mining
algorithms  available  for  classification  based  on  Artificial
Neural Network, Nearest Neighbour Rule & Baysen classifiers
but  decision  tree  mining  is  simple  one.  ID3  and  C4.5
algorithms have been introduced by J.R Quinlan which  produce
reasonable decision trees. The objective of this paper is to present
these  algorithms. At first we present the classical algorithm that
is ID3, then highlights of this study we will discuss in more detail
C4.5 this one is a natural extension of the ID3 algorithm. And we
will make a comparison between these two algorithms and others
algorithms such as C5.0 and CART.

Creating an efficient user knowledge model is a crucial task for web-based adaptive learning environments in different domains. It is often a challenge to determine exactly what type of domain dependent data will be stored and how it will be evaluated by a user modeling system. The most important disadvantage of these models is that they classify the knowledge of users without taking into account the weight differences among the domain dependent data of users. For this purpose, both the probabilistic and the instance-based models have been developed and commonly used in the user modeling systems. In this study a powerful, efficient and simple ‘Intuitive Knowledge Classifier’ method is proposed and presented to model the domain dependent data of users. A domain independent object model, the user modeling approach and the weight-tuning method are combined with instance-based classification algorithm to improve classification performances of well-known the Bayes and the k-nearest neighbor-based methods. The proposed knowledge classifier intuitively explores the optimum weight values of students’ features on their knowledge class first. Then it measures the distances among the students depending on their data and the values of weights. Finally, it uses the dissimilarities in the classification process to determine their knowledge class. The experimental studies have shown that the weighting of domain dependent data of students and combination of user modeling algorithms and population-based searching approach play an essential role in classifying performance of user modeling system. The proposed system improves the classification accuracy of instance-based user modeling approach for all distance metrics and different k-values.

We consider a situation where vents (e.g. manufacturing plant alarms, web-page accesses) occur in sequence. In such cases, the ability to predict future events based on current events can be valuable. A key question, in any given domain, is whe ther the domain is accurately modeled by a simple Markov chain or whether, alt n tively, past history is helpful in predicting future events. If past history is indeed helpful, then data mining algorithms can be helpful in extracting frequent episodes or m otifs from historical databases. In this paper we consider the domain of web-page accesses and show tha t the domain is not Markov. We describe a novel data mining algorithm that, given a set of training sequences, learns to predict future events based on frequent motifs in the training data. The algorithm was tested on a database of sequences of web-page ac cesses recorded over a year at a large WWW site. Our algorithm yielded predicti ve accuracy of 41.8% and coverage of 62% (higher accur...

Recently, the web is becoming an important part of people’s life. The web is a very good place to run successful businesses. Selling products or services online plays an important role in the success of businesses that have a physical presence, like a retail business. Therefore, it is important to have a successful website to serve as a sales and marketing tool. One of the effective used technologies for that purpose is data mining. Data mining is the process of extracting interesting patterns from large databases. Web mining is the usage of data mining techniques to extract interesting information from web data. This paper presents the three components of web mining: web usage mining, web structure mining and web content mining and the main data preprocessing tasks for web usage mining.

Recently, the web is becoming an important part of people’s life. The web is a very good place to run successful businesses. Selling products or services online plays an important role in the success of businesses that have a physical presence, like a retail business. Therefore, it is important to have a successful website to serve as a sales and marketing tool. One of the effective used technologies for that purpose is data mining. Data mining is the process of extracting interesting patterns from large databases. Web mining is the usage of data mining techniques to extract interesting information from web data. This paper presents the three components of web mining: web usage mining, web structure mining and web content mining and the main data preprocessing tasks for web usage mining

Machine learning has become one of the most envisaged areas of research and development field in modern times. But the area of research related to machine learning is not new. The term machine learning was coined by Arthur Samuel in 1952 and since then lots of developments have been made in this field. The data scientists and the machine learning enthusiasts have developed myriad algorithms from time to time to let the benefit of machine learning reach to each and every field of human endeavors. This paper is an effort to put light on some of the most prominent algorithms that have been used in machine learning field on frequent basis since the time of its inception. Further, we will analyze their area of applications.

در عصر فناوری ‌اطلاعات و ارتباطات، نگرش مصرف‌کنندگان به ادراک از خلاقیت پیامک‌های تبلیغاتی، یکی از موضوعات مطرح در حوزه بازاریابی است و هنوز شناخت چندانی درباره آن وجود ندارد. هدف این پژوهش، بررسی تأثیر ادراک از خلاقیت پیامک‌های تبلیغاتی بر نگرش و واکنش مصرف‌کنندگان به پیامک‌های تبلیغاتی ارسالی به تلفن همراه آنهاست. جامعه آماری این تحقیق کاربران تلفن همراه در شهر تهران و حجم نمونه ۳۸۵ نفر است که به‌صورت نمونه‌گیری دردسترس انتخاب شدند. تحقیق حاضر از نظر هدف، کاربردی و از نظر نحوه گردآوری‌ داده‌ها توصیفی- پیمایشی است. مدل مورد مطالعه با استفاده از مدل‌سازی معادلات ساختاری و رویکرد کمترین مربعات جزئی آزمون شد. نتایج بیانگر آن است که ادراک از خلاقیت پیامک تبلیغاتی بر نگرش و واکنش‌رفتاری مصرف‌کنندگان به پیامک‌های تبلیغاتی ارسالی به تلفن همراه آنان تأثیر مثبت معناداری دارد، درحالی‌که ادراک از خلاقیت پیامک تبلیغاتی به‌صورت غیرمستقیم و از طریق متغیر میانجی نگرش نیز بر واکنش ‌رفتاری مصرف‌کنندگان تأثیر می‌گذارد.

In African countries including Ghana, where there is increasing urbanization, solid waste management constitutes one of the most crucial health and environmental problem in most towns and cities. The situation is similar in the Tain District, where the rapid pace of urbanization has come with a rapid increase in the volume of solid waste generated from production and consumption activities. In addition, the recent proliferation of polythene bags for packaging food, water and other packageable goods has seriously aggravated the situation in the district. This study examined the effects of improper solid waste management on public health and the environment in the Tain District. Interview schedule and field observation were the main tool and method respectively for gathering data from 152 households and 4 key informants which were selected through the convenient and purposive sampling techniques. Data gathered from the households was analyzed using Statistical Product for Service Solution and Excel software. In addition, content analysis was employed to analyze data gathered from the key informants. The findings indicate that air pollution, outbreak of diseases, flooding and river contamination are the major effects of improper solid waste management in the study communities. Based on these findings, the study recommends that the Waste Management Department and the Environmental Health and Sanitation Units should enforce the waste management legislations in the Tain District. In addition, an introduction of waste management into the school curriculum will enable the country have a generation with a new mindset towards the huge volumes of solid waste we generate in our neighborhoods.

ABSTRACT The rapid growth of emerging technologies and Internet has moved the world towards an e-world where most of the things are digitized and available on a mouse click. Most of the commercial transactions are performed on Internet with the help of on-line shopping. The huge amount of data puts an extra overload to the user in performing on-line task. Recommender Systems are being used widely to reduce this extra overload and recommend the scrutinized product to the customers. Several data mining techniques are frequently being used for recommendation technology to enhance the online business, amongst which Collaborative filtering, Association rules and web mining are on top . In this paper we try to give an overview of these recommendation techniques with suitable examples and illustrative diagrams, and change of trends in recommender systems. Also the SWOT analysis is discussed for these technologies that give an idea about the respective effects of these systems on business strategies. Various diagrammatic representations are illustrated. And finally we conclude that there is a need of an extra effort to overcome the limitations in existing technology also the aspects of these technology to be used as a business strategy are discussed.

The Internet may be free, but service provider’s indispensable to access services are not, to the extent that while the complexity and burden of the sites increases, it is becoming more and more expensive to surf the net. Blocking access under the guise of protecting us from offensive or sexually explicit content, to pages, chat rooms, newsgroups and other Web options is not anymore an excuse, but a lie. Government surveillance in the Internet, uncontrolled practices of data harvesting and restriction of free speech, open discussion of issues and even political activism has spread in the last 20 years to include countries that are considered democracies such as the U.S. Internet has become a battleground of power and therefore become increasingly militarized.

In this paper we propose a classification for different observable trends over time for user web queries. The focus is on the identification of general collective trends, based on search query keywords, of the user community in Internet and how they behave over a given time period. We give some representative examples of real search queries and their tendencies. From these examples we define a set of descriptive features which can be used as inputs for data modelling. Then we use a selection of non supervised (clustering) and supervised modelling techniques to classify the trends. The results show that it is relatively easy to classify the basic hypothetical trends we have defined, and we identify which of the chosen learning techniques are best able to model the data. However, the presence of more complex, noisy or mixed trends make the classification more difficult.

Convolutional Neural Network (CNN) is the most important Deep Neural Network (DNN) architecture to implement the Deep Learning's application of data and pattern representation in an effective and efficient manner. It uses the idea of animal's visual cortex organization to achieve connectivity pattern between its neurons. A receptive field is a restricted part of the space where a respond to stimuli are done by the individual cortical neurons. The main motivation behind the development of convolutional networks is the biological processes and CNNs are considered as the multilayer perceptrons' variations that are designed for the purpose of providing the minimal usage of pre-processing. The major applications of convolutional neural network include image recognition, natural language processing, recommender systems and video recognition. We have tried to put an honest effort in this paper to analyze the Convolutional Neural Network (CNN) and the various developments made in its area of research.

Literature Review enables us to gain a comprehensive overview and summary of the available information on a particular topic. Literature reviews are generally more useful to all practitioners than any one individual piece of research because they allow one piece of research to be viewed within the wider context of others. We see in this article how and why literature reviews are such an essential tool for every researcher. The reasons of undertaking a literature review is also been introduced. Emphasis has been placed on the literature review of library resources, services and information seeking behavior w.r.t ICT environment. This literature review article helps to library, information science and behavioural science professionals. We have also discussed in this article about different approaches of literature review and how this review helps for researchers. The literature on information seeking behavior has been, and remains, a hot topic for research within various disciplines, but has been limited to review in this article to the field of library and information science, which is where many of the key articles are to be found.

Affordable and ubiquitous online communications (social media) provide the means for flows of ideas and opinions and play an increasing role for the transformation and cohesion of societyyet little is understood about how online opinions emerge, diffuse, and gain momentum. To address this problem, an opinion formation framework based on content analysis of social media and sociophysical system modeling is proposed. Based on prior research and own projects, three building blocks of online opinion tracking and simulation are described: (1) automated topic, emotion and opinion detection in real-time, (2) information flow modeling and agent-based simulation, and (3) modeling of opinion networks, including special social and psychological circumstances, such as the influence of emotions, media and leaders, changing social networks etc. Finally, three application scenarios are presented to illustrate the framework and motivate further research.

XHTML and XBRL are the standard languages for creating a database for the purpose of displaying financial statements on web applications. Today, XBRL is one of the most popular languages for business reporting. A large number of countries in the world recognize the role of XBRL language for financial reporting and the benefits that the reporting format provides in the collection, analysis, preparation, publication and the exchange of data (information) which is the positive side of this language. Here we present all advantages and opportunities that a company may have by using the XBRL format for business reporting. Also, this paper presents XBRL and other languages that are used for creating the database, such XML, XHTML, etc. The role of the AJAX complex model and technology will be explained in detail, and during the exchange of financial data between the web client and web server. Here will be mentioned basic layers of the network for data exchange via the web.

The Web has an enormous influence on our everyday life. Thus, more efficient intelligent approaches and technologies are needed to realize the Web's full potential. Intelligence can be achieved by making the Web aware of the semantics of its own structures and content and by applying intelligent techniques to effectively access web resources. The Semantic Web was one of the significant steps towards bringing Intelligence to the Web. Based on this starting point, the Web Intelligence, Mining, and Semantics (WIMS) community works toward researching and implementing the next generation of the intelligent Web for humans and machines. In this editorial, opening the volume of the proceedings of WIMS'14, we review the topics of interest for the WIMS community, analyze the response of this year's authors to these topics, and present the program of the conference. We hope that this material will be useful for a reader as a key for the structure and content of these proceedings.

View the publication online: https://developing-an-organic-web-channel.ghost.io/2017/06/18/how-to-do-keyword-research-and-develop-great-keyword-strategies/

Keyword research is the missing piece for many web content creators. While I agree that writing is an art form that requires an open and free mind, you can still create entertaining and engaging content that better captures your audience by starting with keyword analysis BEFORE you begin to compose your content.

Keywords and keyword phrases can serve as an outline of the page you are about to create. Creating content without keyword research is akin to getting in the car and driving down the road without a destination in mind. It is a waste of energy and will do little to get you where you need to be. Keywords and keyword phrases should be a road map to better SEO and driving more traffic to your website.

Social Media Monitoring and Analysis are the new trends in technology business. The challenge is to extract correct information from free-form texts of social media communication. Natural Language Processing methods are sometimes used in social media monitoring to improve accuracy in extracting information. This paper discusses a web mining system that is based on Natural Language Processing to analyze social media information. In that process, this research examines Natural Language methods that are important for such analysis. Then the traditional web mining steps are discussed along with proposed use of Natural Language Processing methods.

A social networking service can become the basis for the information infrastructure of the future. For that purpose, it is important to extract social networks that reflect actual social networks which users have already had. Providing a simple means for users to register their social relations is also important. We propose a method that combines various approaches to extract social networks. Especially, three kinds of networks are extracted: user-registered Know-link networks; Web-mined Web-link networks; and face-to-face Touch-link networks. This paper describes the combination of social network extraction for an eventparticipant community. Analyses on the extracted social networks are also presented. 3

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