Automatic Identification of Home Pages on the Web

Kennedy, Alistair; Shepherd, Michael

doi:10.1109/hicss.2005.114

Cited by 48 publications

(43 citation statements)

References 12 publications

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“…Jackson,1999, showed four ways for categorizing of web pages according to the organizer identity, number of pages, organizer's goal, which based on the measure and web pages [23]. Also Alistair Kennedy and categories which are: personal homepage, corporate homepage and organization homepage [24]. We choose a case study in each category that shown in Table I.…”

Section: Case Study and Analysismentioning

confidence: 99%

A Comparison of Navigation Model between UWE and WebML: Homepage Development Case Study

Wakil¹,

Jawawi²,

Safi³

2015

IJIET

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Abstract-Web Engineering is a systematic approach for the development of web applications. In this area navigation model is defined as an important model in all the hypermedia and web engineering methods. Also homepage is one of the most important pages on the websites, because it is considered to be the key element that demonstrates the quality of websites. The web engineering methods still have weaknesses that are difficult for designers to design homepage's content perfectly. In this paper we compare navigation model between UML-Based Web Engineering (UWE) and Web Modeling Language (WebML). The purpose of this paper is to find a quality in these methods for the homepage's development, through designing navigation model in UWE and WebML, and comparing the elements of the navigation model. The result of this paper helps the designers to develop the homepages in order to solve of the weakness's navigation model.

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Section: Case Study and Analysismentioning

confidence: 99%

A Comparison of Navigation Model between UWE and WebML: Homepage Development Case Study

Wakil¹,

Jawawi²,

Safi³

2015

IJIET

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“…Kennedy and M. Shepherd discussed the task of web page classification by genre, namely how to distinguish home pages from non-home pages as noise, and then classify those home pages as personal home page, corporate home page or organization home page [4]. The corpus they used is rather small: 312 web pages.…”

Section: Related Workmentioning

confidence: 99%

Feature-Opinion Extraction using Formal and Informal Classifier Technique

Selvaraj¹,

Bakal²

2014

IJCA

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Merchants selling products on the Web often ask their customers to share their opinions and hands-on experiences on products they have purchased. This is not only true for organizations but also true for individuals. Our beliefs and perceptions of reality, and the choices we make, are, to a considerable degree, conditioned upon how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. Unfortunately, reading through all customer reviews is difficult, especially for popular items, the number of reviews can be up to hundreds or even thousands. This makes it difficult for a potential customer to read them and make an informed decision. Thus a compressed and precise opinion list is what a user would generally desire. The contents available on the Web are not in the standard format. The idea is to classify these contents as formal and informal. The type of algorithm used is linguistic. It incorporates grammatical and other knowledge of the language which helps in understanding the text, thus trying to improve the mining approach to mine product features and their opinions from Web opinion sources for formal as well as for informal text.

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“…Kennedy and Shepherd [14] focused on a specific genre and its sub-genres. Using a neural network, they attempted to discriminate between home pages and non-home pages.…”

Section: Related Workmentioning

confidence: 99%

Exploiting link structure for web page genre identification

Zhu

Xie

et al. 2015

Data Min Knowl Disc

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As the World Wide Web grows at an unprecedented pace, web page genre identification has recently attracted increasing attention because of its importance in web search. A common approach for genre identification is to utilize textual features that can be extracted directly from the web page itself, i.e., On-Page features. The extracted features are subsequently given to a machine learning algorithm that will perform classification. However, these approaches may not be e↵ective when the web page contains limited textual information (e.g., full of images). In this paper, we tackle the genre identification of web pages in such situation. We propose a framework that not only uses On-Page features, but also takes into account information in neighboring pages, i.e., the pages that are connected to the original page by backward and forward links. We first introduce a graph-based model called GenreSim which selects an appropriate set of neighboring pages. We then construct a multiple classifier combination module that utilizes information from the selected neighboring pages and On-Page features to improve genre identification performance. The experiments are conducted on well-known corpora, and the favorable results indicate that our proposed framework is e↵ective, particularly in identifying web pages with limited textual information.

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Automatic Identification of Home Pages on the Web

Cited by 48 publications

References 12 publications

A Comparison of Navigation Model between UWE and WebML: Homepage Development Case Study

A Comparison of Navigation Model between UWE and WebML: Homepage Development Case Study

Feature-Opinion Extraction using Formal and Informal Classifier Technique

Exploiting link structure for web page genre identification

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