Web Directories Research Articles

A flexible interface design for Web directories to accommodate different cognitive styles

AbstractSearch engines are very popular tools for collecting information from distributed resources. They provide not only search facilities, but they also offer directories for users to browse content divided into groups. In this paper, we've adopted an individual differences approach to explore user's attitudes towards various interface features provided by existing Web directories. Among a variety of individual differences, cognitive style is a particularly important characteristic that influences the effectiveness of information seeking. Empirical results indicate that users' cognitive styles influence their reactions to the organization of subject categories, presentation of the results, and screen layout. We developed a set of design guidelines on the basis of these results, and propose a flexible interface that adopts these guidelines to accommodate the preferences of different cognitive style groups.

Peer-to-peer semantic coordination

Semantic coordination, namely the problem of finding an agreement on the meaning of heterogeneous schemas, is one of the key issues in the development of the Semantic Web. In this paper, we propose a method for discovering semantic mappings across hierarchical classifications (HCs) based on a new approach, which shifts the problem of semantic coordination from the problem of computing linguistic or structural similarities (what most other proposed approaches do) to the problem of deducing relations between sets of logical formulae that represent the meaning of concepts belonging to different schema. We show how to apply the approach and the algorithm to an interesting family of schemas, namely hierarchical classifications, and present the results of preliminary tests on two types of hierarchical classifications, web directories and catalogs. Finally, we argue why this is a significant improvement on previous approaches.

Efficient cooperative searching on the Web: system design and evaluation

The World Wide Web provides a convenient and inexpensive infrastructure for Computer-Supported Cooperative Work. Groupware systems allow distant users to work together in a shared virtual workspace. Awareness of group members’ actions is a basic feature and key functionality for groupware. Many times a group of people work together researching information on the Web about a topic. This type of collaboration can be decomposed into two tasks. First, team members have to access, process and filter by importance the Web pages gathered. Second, they have to synthesize and present them either as a whole in the form of a report, or in an organized way in the form of Web directories. A key issue that strongly affects this particular type of cooperative work is the revisiting of pages and, consequently, the time spent on accessing and processing the same information sources, which may be relevant or not to the topic. We propose group member URL traversal awareness (GMUTA) as significant functionality for Web-based collaboration tools in order to avoid conflicting or repetitive actions by group members. We then present a prototype system we developed, the Web Collaborative Searching Assistant (WCSA), which exploits GMUTA and helps distributed group members to work more efficiently. Experimental evaluation of the WCSA indicated that the functionality provided overcomes the above-mentioned problem, improves searching efficiency and adds substantial value to the collaboration.

Cognitive styles and users’ responses to structured information representation

Structured information repositories, such as digital libraries, Web directories, and subject gateways, require effective ways to organise and manage information. This paper focuses on Web directories and investigates the relationships between users' cognitive styles and information representation. The results indicate that cognitive style influences participants' reactions to the organisation of subject categories, presentation of the results, and screen layout. The findings are applied to develop a design framework that can support the improvement of Web directories and other structured digital information resources. Finally, implications for information representation are discussed.

A text mining approach on automatic generation of web directories and hierarchies

Abstract The World Wide Web (WWW) has been recognized as the ultimate and unique source of information for information retrieval and knowledge discovery communities. Tremendous amount of knowledge are recorded using various types of media, producing enormous amount of web pages in the WWW. Retrieval of required information from the WWW is thus an arduous task. Different schemes for retrieving web pages have been used by the WWW community. One of the most widely used scheme is to traverse predefined web directories to reach a user's goal. These web directories are compiled or classified folders of web pages and are usually organized into hierarchical structures. The classification of web pages into proper directories and the organization of directory hierarchies are generally performed by human experts. In this work, we provide a corpus-based method that applies a kind of text mining techniques on a corpus of web pages to automatically create web directories and organize them into hierarchies. The method is based on the self-organizing map learning algorithm and requires no human intervention during the construction of web directories and hierarchies. The experiments show that our method can produce comprehensible and reasonable web directories and hierarchies.

Measurement of the presence of keywords and description meta‐tags on a selected number of Iranian Web sites

Digital information retrieval has been as a problem, and at the same time a research interest for information scientists in recent years. They have planned some solutions to solve problems manifested during the 1990s. Designing meta‐tags and applying them to HTML documents was a remedy in this direction. Meta‐tags can help authors, publishers and indexers of Web pages to analyze intended content more precisely and efficiently. The aim of the present survey is to measure meta‐tags of the Iranian Web sites in accordance with an international criterion. To carry out the research, 346 Iranian Web sites were selected among 3,342, which represented a sample of all Web sites existed in Iranhoo, an Iranian Web directory. The source codes of the sample home pages were reviewed in terms of the presence of keywords and description meta‐tags. The findings of the survey showed that 31.5 percent and 24.6 percent of the Iranian Web sites have keywords and description meta‐tags respectively. The paper concludes that the Iranian Web sites are lower than non‐Iranian Web sites in terms of the use of meta‐tags.

Are webliographies still in use?

Information retrieval has emerged as one of the most important areas of study in the field of information science during the past decades. Since the invention of the World Wide Web, some solutions have been planned to solve the problem of digital information retrieval, including search engines and Web directories. A webliography is a special type of Web directory and consists of an enumerative list of hypertext links to the scientific sources of information on the Net. According to the last time of updating of the webliographies, this article tries to provide an answer to this question: are webliographies still in use? In order to do this, two searches on webliography and webography made using Google. Among 357 updated and retrieved hits, 303 items have been updated at the second period of webliographies lifetime. It concludes that doing some research projects on webliographies is not only possible, but essential.

Weblogs, social software, and new interactivity on the web.

Weblogs, social software, and new interactivity on the web.

국내 웹 디렉토리들의 커버리지 및 커버리지 중복성 분석

본 연구에서는 국내 주요 웹 검색 포탈인 네이버, 야후 코리아, 엠파스가 제공하는 웹 디렉토리들의 커버리지 및 커버리지 중복성을 분석하였다. 이를 위하여 본 연구는 웹 디렉토리에 등록된 사이트들의 수집 방법을 개발하고, 대분류 매핑, 중복 분류 및 참조 링크 고려와 같은 커버리지 및 커버리지 중복성 분석에 필요한 방법론을 제시하였다. 조사 결과, 참조 링크의 허용 여부가 웹 디렉토리의 커버리지에 매우 큰 영향을 미치며, 국내 웹 디렉토리들 사이의 커버리지 중복성이 매우 낮은 것으로 나타났다. 본 연구는 국내 웹 디렉토리들에 대한 이해를 넓히고, 웹 디렉토리들의 커버리지 및 커버리지 중복성 분석에 필요한 방법론을 제시함으로써, 웹 디렉토리에 관한 연구에 기여할 것으로 기대된다.

Additional paper: computational resources for metabolomics.

Metabolomics, a comprehensive extension of traditional targeted metabolite analysis, has recently attracted much attention as the biological jigsaw puzzle's missing piece that can complement transcriptome and proteome analysis. This tutorial survey introduces practical web resources with special emphasis on the computational aspects involved in processing and navigating metabolome data. The introduced materials are also accessible from the author's web directory (Atomic Reconstruction of Metabolism or ARM).

Peer-to-Peer Semantic Coordination

Semantic coordination, namely the problem of finding an agreement on the meaning of heterogeneous schemas, is one of the key issues in the development of the Semantic Web. In this paper, we propose a method for discovering semantic mappings across hierarchical classifications based on a new approach, which shifts the problem of semantic coordination from the problem of computing linguistic or structural similarities (what most other proposed approaches do) to the problem of deducing relations between sets of logical formulae that represent the meaning of concepts belonging to different schema. We show how to apply the approach and the algorithm to an interesting family of schemas, namely hierarchical classifications, and present the results of preliminary tests on two types of hierarchical classifications, web directories and catalogs. Finally, we argue why this is a significant improvement on previous approaches.

Context matching for electronic marketplaces: a case study

Matching algorithms automatically discover semantic relations between two autonomously developed conceptual representations of two overlapping domains. Typical examples of such conceptualisations are electronic market catalogues (e.g., unspsc and eCl@ss) and Web directories (e.g., google and yahoo). The objective of this paper is the description of a use case in which the matching algorithm CtxMatch, developed at ITC-IRST and the University of Trento, has been used to re-classify into the Universal Standard Products and Services Classification (unspsc) the catalogue of office equipment and accessories used by a worldwide telecommunication company to classify its suppliers. On the basis of this experience we are envisaging new applications of the algorithm in the area of demand aggregation. We conclude the paper by briefly describing a future application in this area.

Improving Web searches: case study of quit-smoking Web sites for teenagers.

BackgroundThe Web has become an important and influential source of health information. With the vast number of Web sites on the Internet, users often resort to popular search sites when searching for information. However, little is known about the characteristics of Web sites returned by simple Web searches for information about smoking cessation for teenagers.ObjectiveTo determine the characteristics of Web sites retrieved by search engines about smoking cessation for teenagers and how information quality correlates with the search ranking.MethodsThe top 30 sites returned by 4 popular search sites in response to the search terms "teen quit smoking" were examined. The information relevance and quality characteristics of these sites were evaluated by 2 raters. Objective site characteristics were obtained using a page-analysis Web site.ResultsOnly 14 of the 30 Web sites are of direct relevance to smoking cessation for teenagers. The readability of about two-thirds of the 14 sites is below an eighth-grade school level and they ranked significantly higher (Kendall rank correlation, tau = -0.39, P= .05) in search-site results than sites with readability above or equal to that grade level. Sites that ranked higher were significantly associated with the presence of e-mail address for contact (tau = -0.46, P= .01), annotated hyperlinks to external sites (tau = -0.39, P= .04), and the presence of meta description tag (tau = -0.48, P= .002). The median link density (number of external sites that have a link to that site) of the Web pages was 6 and the maximum was 735. A higher link density was significantly associated with a higher rank (tau = -0.58, P= .02).ConclusionsUsing simple search terms on popular search sites to look for information on smoking cessation for teenagers resulted in less than half of the sites being of direct relevance. To improve search efficiency, users could supplement results obtained from simple Web searches with human-maintained Web directories and learn to refine their searches with more advanced search syntax.

Testing a Cancer Meta Spider

As in many other applications, the rapid proliferation and unrestricted Web-based publishing of health-related content have made finding pertinent and useful healthcare information increasingly difficult. Although the development of healthcare information retrieval systems such as medical search engines and peer-reviewed medical Web directories has helped alleviate this information and cognitive overload problem, the effectiveness of these systems has been limited by low search precision, poor presentation of search results, and the required user search effort. To address these challenges, we have developed a domain-specific meta-search tool called Cancer Spider. By leveraging post-retrieval document clustering techniques, this system aids users in querying multiple medical data sources to gain an overview of the retrieved documents and locating answers of high quality to a wide spectrum of health questions. The system presents the retrieved documents to users in two different views: (1) Web pages organized by a list of key phrases, and (2) Web pages clustered into regions discussing different topics on a two-dimensional map (self-organizing map). In this paper, we present the major components of the Cancer Spider system and a user evaluation study designed to evaluate the effectiveness and efficiency of our approach. Initial results comparing Cancer Spider with NLM Gateway, a premium medical search site, have shown that they achieved comparable performances measured by precision, recall, and F-measure. Cancer Spider required less user searching time, fewer documents that need to be browsed, and less user effort.

Automatic Association of Web Directories with Word Senses

We describe an algorithm that combines lexical information (from WordNet 1.7) with Web directories (from the Open Directory Project) to associate word senses with such directories. Such associations can be used as rich characterizations to acquire sense-tagged corpora automatically, cluster topically related senses, and detect sense specializations. The algorithm is evaluated for the 29 nouns (147 senses) used in the Senseval 2 competition, obtaining 148 (word sense, Web directory) associations covering 88% of the domain-specific word senses in the test data with 86% accuracy. The richness of Web directories as sense characterizations is evaluated in a supervised word sense disambiguation task using the Senseval 2 test suite. The results indicate that, when the directory/word sense association is correct, the samples automatically acquired from the Web directories are nearly as valid for training as the original Senseval 2 training instances. The results support our hypothesis that Web directories are a rich source of lexical information: cleaner, more reliable, and more structured than the full Web as a corpus.

Open content and value creation

The borderline between production and consumption of media content is not so clear as it used to be. For example on the Internet, many people put a lot of effort into producing personal homepages in the absence of personal compensation. They publish everything from holiday pictures to complete Web directories. Illegal exchange of media material is another important trend that has a negative impact on the media industry. In this paper, I consider open content as an important development track in the media landscape of tomorrow. I define open content as content possible for others to improve and redistribute and/or content that is produced without any consideration of immediate financial reward ? often collectively within a virtual community. The open content phenomenon can to some extent be compared to the phenomenon of open source. Production within a virtual community is one possible source of open content. Another possible source is content in the public domain. This could be sound, pictures, movies or texts that have no copyright, in legal terms. Which are the driving forces for the cooperation between players that work with open content? This knowledge could be essential in order to understand the dynamics of business development, technical design and legal aspects in this field. In this paper I focus on these driving forces and the relationships between these players. I have studied three major open content projects. In my analysis, I have used Gordijnos (2002) value modeling method "e3value", modified for open content value creation and value chains. Open content value chains look much the same as commercial value chains, but there are also some major differences. In a commercial value chain, the consumerso needs trigger the entire chain of value creation. My studies indicate that an open content value chain is often triggered by what the creators and producers wish to make available as open content. Motivations in non-monetary forms play a crucial role in the creation of open content value chains and value. My study of these aspects is based on Feller and Fitzgeraldos (2002) three perspectives on motivations underlying participation in the creation of open source software.

A layered approach for investigating the topological structure of communities in the Web

A layered approach for identifying communities in the Web is presented and explored by applying the flake exact community identification algorithm to the UK academic Web. Although community or topic identification is a common task in information retrieval, a new perspective is developed by: the application of alternative document models, shifting the focus from individual pages to aggregated collections based upon Web directories, domains and entire sites; the removal of internal site links; and the adaptation of a new fast algorithm to allow fully‐automated community identification using all possible single starting points. The overall topology of the graphs in the three least‐aggregated layers was first investigated and found to include a large number of isolated points but, surprisingly, with most of the remainder being in one huge connected component, exact proportions varying by layer. The community identification process then found that the number of communities far exceeded the number of topological components, indicating that community identification is a potentially useful technique, even with random starting points. Both the number and size of communities identified was dependent on the parameter of the algorithm, with very different results being obtained in each case. In conclusion, the UK academic Web is embedded with layers of non‐trivial communities and, if it is not unique in this, then there is the promise of improved results for information retrieval algorithms that can exploit this additional structure, and the application of the technique directly to partially automate Web metrics tasks such as that of finding all pages related to a given subject hosted by a single country's universities.

The Scottish Web Directory: Over 10,000 Household Names & Official Web Sites

The Scottish Web Directory: Over 10,000 Household Names & Official Web Sites

Fast and accurate text classification via multiple linear discriminant projections

Abstract.Support vector machines (SVMs) have shown superb performance for text classification tasks. They are accurate, robust, and quick to apply to test instances. Their only potential drawback is their training time and memory requirement. For n training instances held in memory, the best-known SVM implementations take time proportional to n a, where a is typically between 1.8 and 2.1. SVMs have been trained on data sets with several thousand instances, but Web directories today contain millions of instances that are valuable for mapping billions of Web pages into Yahoo!-like directories. We present SIMPL, a nearly linear-time classification algorithm that mimics the strengths of SVMs while avoiding the training bottleneck. It uses Fisher's linear discriminant, a classical tool from statistical pattern recognition, to project training instances to a carefully selected low-dimensional subspace before inducing a decision tree on the projected instances. SIMPL uses efficient sequential scans and sorts and is comparable in speed and memory scalability to widely used naive Bayes (NB) classifiers, but it beats NB accuracy decisively. It not only approaches and sometimes exceeds SVM accuracy, but also beats the running time of a popular SVM implementation by orders of magnitude. While describing SIMPL, we make a detailed experimental comparison of SVM-generated discriminants with Fisher's discriminants, and we also report on an analysis of the cache performance of a popular SVM implementation. Our analysis shows that SIMPL has the potential to be the method of choice for practitioners who want the accuracy of SVMs and the simplicity and speed of naive Bayes classifiers.

Category cluster discovery from distributed WWW directories

Due to the inherently distributed nature of many networks, including the Internet, information and knowledge are generated and organized independently by different groups of people. To discover and exploit all the knowledge from different sources, a method of knowledge integration is usually required. Considering the document category sets as information sources, we define a problem of information integration called category merging. The purpose of category merging is to automatically construct a unified category set which represents and exploits document information from several different sources. This merging process is based on the clustering concept where categories with similar characteristics are merged into the same cluster under certain distributed constraints. To evaluate the quality of the merged category set, we measure the precision and recall values under three classification methods, Naive Bayes, Vector Space Model, and K-Nearest Neighbor. In addition, we propose a performance measure called cluster entropy, which determines how well the categories from different sources are distributed over the resulting clusters. We perform the merging process by using the real data sets collected from three different Web directories. The results show that our merging process improves the classification performance over the non-merged approach and also provides a better representation for all categories from distributed directories.