Search (59 results, page 1 of 3)

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Abstract

The unprecedented growth of the Internet has given rise to the Dark Web, the problematic facet of the Web associated with cybercrime, hate, and extremism. Despite the need for tools to collect and analyze Dark Web forums, the covert nature of this part of the Internet makes traditional Web crawling techniques insufficient for capturing such content. In this study, we propose a novel crawling system designed to collect Dark Web forum content. The system uses a human-assisted accessibility approach to gain access to Dark Web forums. Several URL ordering features and techniques enable efficient extraction of forum postings. The system also includes an incremental crawler coupled with a recall-improvement mechanism intended to facilitate enhanced retrieval and updating of collected content. Experiments conducted to evaluate the effectiveness of the human-assisted accessibility approach and the recall-improvement-based, incremental-update procedure yielded favorable results. The human-assisted approach significantly improved access to Dark Web forums while the incremental crawler with recall improvement also outperformed standard periodic- and incremental-update approaches. Using the system, we were able to collect over 100 Dark Web forums from three regions. A case study encompassing link and content analysis of collected forums was used to illustrate the value and importance of gathering and analyzing content from such online communities.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1213-1231

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Abstract

With more than two billion pages created by millions of Web page authors and organizations, the World Wide Web is a tremendously rich knowledge base. The knowledge comes not only from the content of the pages themselves, but also from the unique characteristics of the Web, such as its hyperlink structure and its diversity of content and languages. Analysis of these characteristics often reveals interesting patterns and new knowledge. Such knowledge can be used to improve users' efficiency and effectiveness in searching for information an the Web, and also for applications unrelated to the Web, such as support for decision making or business management. The Web's size and its unstructured and dynamic content, as well as its multilingual nature, make the extraction of useful knowledge a challenging research problem. Furthermore, the Web generates a large amount of data in other formats that contain valuable information. For example, Web server logs' information about user access patterns can be used for information personalization or improving Web page design.

Source

Annual review of information science and technology. 38(2004), S.289-330

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Abstract

While the Web has grown significantly in recent years, some portions of the Web remain largely underdeveloped, as shown in a lack of high-quality content and functionality. An example is the Arabic Web, in which a lack of well-structured Web directories limits users' ability to browse for Arabic resources. In this research, we proposed an approach to building Web directories for the underdeveloped Web and developed a proof-of-concept prototype called the Arabic Medical Web Directory (AMedDir) that supports browsing of over 5,000 Arabic medical Web sites and pages organized in a hierarchical structure. We conducted an experiment involving Arab participants and found that the AMedDir significantly outperformed two benchmark Arabic Web directories in terms of browsing effectiveness, efficiency, information quality, and user satisfaction. Participants expressed strong preference for the AMedDir and provided many positive comments. This research thus contributes to developing a useful Web directory for organizing the information in the Arabic medical domain and to a better understanding of how to support browsing on the underdeveloped Web.

Date

22. 3.2009 17:57:50

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.3, S.595-607

Theme

Information Gateway

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Abstract

Relation extraction is the process of scanning text for relationships between named entities. Recently, significant studies have focused on automatically extracting relations from biomedical corpora. Most existing biomedical relation extractors require manual creation of biomedical lexicons or parsing templates based on domain knowledge. In this study, we propose to use kernel-based learning methods to automatically extract biomedical relations from literature text. We develop a framework of kernel-based learning for biomedical relation extraction. In particular, we modified the standard tree kernel function by incorporating a trace kernel to capture richer contextual information. In our experiments on a biomedical corpus, we compare different kernel functions for biomedical relation detection and classification. The experimental results show that a tree kernel outperforms word and sequence kernels for relation detection, our trace-tree kernel outperforms the standard tree kernel, and a composite kernel outperforms individual kernels for relation extraction.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.5, S.756-769

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Abstract

As the Web is used increasingly to share and disseminate information, business analysts and managers are challenged to understand stakeholder relationships. Traditional stakeholder theories and frameworks employ a manual approach to analysis and do not scale up to accommodate the rapid growth of the Web. Unfortunately, existing business intelligence (BI) tools lack analysis capability, and research on BI systems is sparse. This research proposes a framework for designing BI systems to identify and to classify stakeholders on the Web, incorporating human knowledge and machine-learned information from Web pages. Based on the framework, we have developed a prototype called Business Stakeholder Analyzer (BSA) that helps managers and analysts to identify and to classify their stakeholders on the Web. Results from our experiment involving algorithm comparison, feature comparison, and a user study showed that the system achieved better within-class accuracies in widespread stakeholder types such as partner/sponsor/supplier and media/reviewer, and was more efficient than human classification. The student and practitioner subjects in our user study strongly agreed that such a system would save analysts' time and help to identify and classify stakeholders. This research contributes to a better understanding of how to integrate information technology with stakeholder theory, and enriches the knowledge base of BI system design.

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.1, S.59-74

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Abstract

As part of the ongoing Illinois Digital Library Initiative project, this research proposes an intelligent agent approach to Web searching. In this experiment, we developed 2 Web personal spiders based on best first search and genetic algorithm techniques, respectively. These personal spiders can dynamically take a user's selected starting homepages and search for the most closely related homepages in the Web, based on the links and keyword indexing. A graphical, dynamic, Jav-based interface was developed and is available for Web access. A system architecture for implementing such an agent-spider is presented, followed by deteiled discussions of benchmark testing and user evaluation results. In benchmark testing, although the genetic algorithm spider did not outperform the best first search spider, we found both results to be comparable and complementary. In user evaluation, the genetic algorithm spider obtained significantly higher recall value than that of the best first search spider. However, their precision values were not statistically different. The mutation process introduced in genetic algorithms allows users to find other potential relevant homepages that cannot be explored via a conventional local search process. In addition, we found the Java-based interface to be a necessary component for design of a truly interactive and dynamic Web agent

Source

Journal of the American Society for Information Science. 49(1998) no.7, S.604-618

Theme

Web-Agenten

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Abstract

To make good decisions, businesses try to gather good intelligence information. Yet managing and processing a large amount of unstructured information and data stand in the way of greater business knowledge. An effective business intelligence tool must be able to access quality information from a variety of sources in a variety of forms, and it must support people as they search for and analyze that information. The EBizPort system was designed to address information needs for the business/IT community. EBizPort's collection-building process is designed to acquire credible, timely, and relevant information. The user interface provides access to collected and metasearched resources using innovative tools for summarization, categorization, and visualization. The effectiveness, efficiency, usability, and information quality of the EBizPort system were measured. EBizPort significantly outperformed Brint, a business search portal, in search effectiveness, information quality, user satisfaction, and usability. Users particularly liked EBizPort's clean and user-friendly interface. Results from our evaluation study suggest that the visualization function added value to the search and analysis process, that the generalizable collection-building technique can be useful for domain-specific information searching an the Web, and that the search interface was important for Web search and browse support.

Source

Journal of the American Society for Information Science and Technology. 55(2004) no.10, S.873-891

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Abstract

Despite the rapid growth of text-based computer-mediated communication (CMC), its limitations have rendered the media highly incoherent. This poses problems for content analysis of online discourse archives. Interactional coherence analysis (ICA) attempts to accurately identify and construct CMC interaction networks. In this study, we propose the Hybrid Interactional Coherence (HIC) algorithm for identification of web forum interaction. HIC utilizes a bevy of system and linguistic features, including message header information, quotations, direct address, and lexical relations. Furthermore, several similarity-based methods including a Lexical Match Algorithm (LMA) and a sliding window method are utilized to account for interactional idiosyncrasies. Experiments results on two web forums revealed that the proposed HIC algorithm significantly outperformed comparison techniques in terms of precision, recall, and F-measure at both the forum and thread levels. Additionally, an example was used to illustrate how the improved ICA results can facilitate enhanced social network and role analysis capabilities.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.8, S.1195-1209

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Abstract

Information extraction is an important text-mining task that aims at extracting prespecified types of information from large text collections and making them available in structured representations such as databases. In the biomedical domain, information extraction can be applied to help biologists make the most use of their digital-literature archives. Currently, there are large amounts of biomedical literature that contain rich information about biomedical substances. Extracting such knowledge requires a good named entity recognition technique. In this article, we combine conditional random fields (CRFs), a state-of-the-art sequence-labeling algorithm, with two semisupervised learning techniques, bootstrapping and feature sampling, to recognize disease names from biomedical literature. Two data-processing strategies for each technique also were analyzed: one sequentially processing unlabeled data partitions and another one processing unlabeled data partitions in a round-robin fashion. The experimental results showed the advantage of semisupervised learning techniques given limited labeled training data. Specifically, CRFs with bootstrapping implemented in sequential fashion outperformed strictly supervised CRFs for disease name recognition. The project was supported by NIH/NLM Grant R33 LM07299-01, 2002-2005.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.4, S.727-737

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Abstract

Social media is frequently used as a platform for the exchange of information and opinions as well as propaganda dissemination. But online content can be misused for the distribution of illicit information, such as violent postings in web forums. Illicit content is highly distributed in social media, while non-illicit content is unspecific and topically diverse. It is costly and time consuming to label a large amount of illicit content (positive examples) and non-illicit content (negative examples) to train classification systems. Nevertheless, it is relatively easy to obtain large volumes of unlabeled content in social media. In this article, an artificial immune system-based technique is presented to address the difficulties in the illicit content identification in social media. Inspired by the positive selection principle in the immune system, we designed a novel labeling heuristic based on partially supervised learning to extract high-quality positive and negative examples from unlabeled datasets. The empirical evaluation results from two large hate group web forums suggest that our proposed approach generally outperforms the benchmark techniques and exhibits more stable performance.

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.256-269

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Abstract

While the Web has become a worldwide platform for communication, terrorists share their ideology and communicate with members on the Dark Web - the reverse side of the Web used by terrorists. Currently, the problems of information overload and difficulty to obtain a comprehensive picture of terrorist activities hinder effective and efficient analysis of terrorist information on the Web. To improve understanding of terrorist activities, we have developed a novel methodology for collecting and analyzing Dark Web information. The methodology incorporates information collection, analysis, and visualization techniques, and exploits various Web information sources. We applied it to collecting and analyzing information of 39 Jihad Web sites and developed visualization of their site contents, relationships, and activity levels. An expert evaluation showed that the methodology is very useful and promising, having a high potential to assist in investigation and understanding of terrorist activities by producing results that could potentially help guide both policymaking and intelligence research.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.8, S.1347-1359

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Abstract

Biomedical research is critical to biodefense, which is drawing increasing attention from governments globally as well as from various research communities. The U.S. government has been closely monitoring and regulating biomedical research activities, particularly those studying or involving bioterrorism agents or diseases. Effective surveillance requires comprehensive understanding of extant biomedical research and timely detection of new developments or emerging trends. The rapid knowledge expansion, technical breakthroughs, and spiraling collaboration networks demand greater support for literature search and sharing, which cannot be effectively supported by conventional literature search mechanisms or systems. In this study, we propose an integrated approach that integrates advanced techniques for content analysis, network analysis, and information visualization. We design and implement Arizona Literature Mapper, a Web-based portal that allows users to gain timely, comprehensive understanding of bioterrorism research, including leading scientists, research groups, institutions as well as insights about current mainstream interests or emerging trends. We conduct two user studies to evaluate Arizona Literature Mapper and include a well-known system for benchmarking purposes. According to our results, Arizona Literature Mapper is significantly more effective for supporting users' search of bioterrorism publications than PubMed. Users consider Arizona Literature Mapper more useful and easier to use than PubMed. Users are also more satisfied with Arizona Literature Mapper and show stronger intentions to use it in the future. Assessments of Arizona Literature Mapper's analysis functions are also positive, as our subjects consider them useful, easy to use, and satisfactory. Our results have important implications that are also discussed in the article.

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.7, S.1466-1485

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Abstract

Research in information retrieval (IR) has advanced significantly in the past few decades. Many tasks, such as indexing and text categorization, can be performed automatically with minimal human effort. Machine learning has played an important role in such automation by learning various patterns such as document topics, text structures, and user interests from examples. In recent years, it has become increasingly difficult to search for useful information an the World Wide Web because of its large size and unstructured nature. Useful information and resources are often hidden in the Web. While machine learning has been successfully applied to traditional IR systems, it poses some new challenges to apply these algorithms to the Web due to its large size, link structure, diversity in content and languages, and dynamic nature. On the other hand, such characteristics of the Web also provide interesting patterns and knowledge that do not present in traditional information retrieval systems.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.7, S.621-624

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Abstract

Advanced technology has resulted in the generation of about one million terabytes of information every year. Ninety-reine percent of this is available in digital format (Keim, 2001). More information will be generated in the next three years than was created during all of previous human history (Keim, 2001). Collecting information is no longer a problem, but extracting value from information collections has become progressively more difficult. Various search engines have been developed to make it easier to locate information of interest, but these work well only for a person who has a specific goal and who understands what and how information is stored. This usually is not the Gase. Visualization was commonly thought of in terms of representing human mental processes (MacEachren, 1991; Miller, 1984). The concept is now associated with the amplification of these mental processes (Card, Mackinlay, & Shneiderman, 1999). Human eyes can process visual cues rapidly, whereas advanced information analysis techniques transform the computer into a powerful means of managing digitized information. Visualization offers a link between these two potent systems, the human eye and the computer (Gershon, Eick, & Card, 1998), helping to identify patterns and to extract insights from large amounts of information. The identification of patterns is important because it may lead to a scientific discovery, an interpretation of clues to solve a crime, the prediction of catastrophic weather, a successful financial investment, or a better understanding of human behavior in a computermediated environment. Visualization technology shows considerable promise for increasing the value of large-scale collections of information, as evidenced by several commercial applications of TreeMap (e.g., http://www.smartmoney.com) and Hyperbolic tree (e.g., http://www.inxight.com) to visualize large-scale hierarchical structures. Although the proliferation of visualization technologies dates from the 1990s where sophisticated hardware and software made increasingly faster generation of graphical objects possible, the role of visual aids in facilitating the construction of mental images has a long history. Visualization has been used to communicate ideas, to monitor trends implicit in data, and to explore large volumes of data for hypothesis generation. Imagine traveling to a strange place without a map, having to memorize physical and chemical properties of an element without Mendeleyev's periodic table, trying to understand the stock market without statistical diagrams, or browsing a collection of documents without interactive visual aids. A collection of information can lose its value simply because of the effort required for exhaustive exploration. Such frustrations can be overcome by visualization.
Visualization can be classified as scientific visualization, software visualization, or information visualization. Although the data differ, the underlying techniques have much in common. They use the same elements (visual cues) and follow the same rules of combining visual cues to deliver patterns. They all involve understanding human perception (Encarnacao, Foley, Bryson, & Feiner, 1994) and require domain knowledge (Tufte, 1990). Because most decisions are based an unstructured information, such as text documents, Web pages, or e-mail messages, this chapter focuses an the visualization of unstructured textual documents. The chapter reviews information visualization techniques developed over the last decade and examines how they have been applied in different domains. The first section provides the background by describing visualization history and giving overviews of scientific, software, and information visualization as well as the perceptual aspects of visualization. The next section assesses important visualization techniques that convert abstract information into visual objects and facilitate navigation through displays an a computer screen. It also explores information analysis algorithms that can be applied to identify or extract salient visualizable structures from collections of information. Information visualization systems that integrate different types of technologies to address problems in different domains are then surveyed; and we move an to a survey and critique of visualization system evaluation studies. The chapter concludes with a summary and identification of future research directions.

Source

Annual review of information science and technology. 39(2005), S.139-177

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Abstract

2 studies were conducted to investigate the cognitive processes involved in online document-based information retrieval. These studies led to the development of 5 computerised models of online document retrieval. These models were incorporated into a design of an 'intelligent' document-based retrieval system. Following a discussion of this system, discusses the broader implications of the research for the design of information retrieval sysems

Source

Information processing and management. 27(1991) no.5, S.405-432

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Abstract

With the emergence of Web 2.0, sharing personal content, communicating ideas, and interacting with other online users in Web 2.0 communities have become daily routines for online users. User-generated data from Web 2.0 sites provide rich personal information (e.g., personal preferences and interests) and can be utilized to obtain insight about cyber communities and their social networks. Many studies have focused on leveraging user-generated information to analyze blogs and forums, but few studies have applied this approach to video-sharing Web sites. In this study, we propose a text-based framework for video content classification of online-video sharing Web sites. Different types of user-generated data (e.g., titles, descriptions, and comments) were used as proxies for online videos, and three types of text features (lexical, syntactic, and content-specific features) were extracted. Three feature-based classification techniques (C4.5, Naïve Bayes, and Support Vector Machine) were used to classify videos. To evaluate the proposed framework, user-generated data from candidate videos, which were identified by searching user-given keywords on YouTube, were first collected. Then, a subset of the collected data was randomly selected and manually tagged by users as our experiment data. The experimental results showed that the proposed approach was able to classify online videos based on users' interests with accuracy rates up to 87.2%, and all three types of text features contributed to discriminating videos. Support Vector Machine outperformed C4.5 and Naïve Bayes techniques in our experiments. In addition, our case study further demonstrated that accurate video-classification results are very useful for identifying implicit cyber communities on video-sharing Web sites.

Object

Web 2.0

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.891-906

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Abstract

Attempts to identify the nature and causes of information management indeterminism in an online research environment and proposes solutions for alleviating this indeterminism. Conducts two empirical studies of information management activities. The first identified the types and nature of information management indeterminism by evaluating archived text. The second focused on four sources of indeterminism: subject area knowledge, classification knowledge, system knowledge, and collaboration knowledge. Proposes a knowledge based design for alleviating indeterminism, which contains a system generated thesaurus and an inferencing engine

Source

Information processing and management. 30(1994) no.4, S.557-577

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Abstract

This paper summarizes a prototype geographical image retrieval system that demonstrates how to integrate image processing and information analysis techniques to support large-scale content-based image retrieval. By using an image as its interface, the prototype system addresses a troublesome aspect of traditional retrieval models, which require users to have complete knowledge of the low-level features of an image. In addition we describe an experiment to validate against that of human subjects in an effort to address the scarcity of research evaluating performance of an algorithm against that of human beings. The results of the experiment indicate that the system could do as well as human subjects in accomplishing the tasks of similarity analysis and image categorization. We also found that under some circumstances texture features of an image are insufficient to represent an geographic image. We believe, however, that our image retrieval system provides a promising approach to integrating image processing techniques and information retrieval algorithms

Source

Journal of the American Society for Information Science. 51(2000) no.7, S.625-634

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Abstract

As increasing numbers of non-English resources have become available on the Web, the interesting and important issue of how Web users can retrieve documents in different languages has arisen. Cross-language information retrieval (CLIP), the study of retrieving information in one language by queries expressed in another language, is a promising approach to the problem. Cross-language information retrieval has attracted much attention in recent years. Most research systems have achieved satisfactory performance on standard Text REtrieval Conference (TREC) collections such as news articles, but CLIR techniques have not been widely studied and evaluated for applications such as Web portals. In this article, the authors present their research in developing and evaluating a multilingual English-Chinese Web portal that incorporates various CLIP techniques for use in the business domain. A dictionary-based approach was adopted and combines phrasal translation, co-occurrence analysis, and pre- and posttranslation query expansion. The portal was evaluated by domain experts, using a set of queries in both English and Chinese. The experimental results showed that co-occurrence-based phrasal translation achieved a 74.6% improvement in precision over simple word-byword translation. When used together, pre- and posttranslation query expansion improved the performance slightly, achieving a 78.0% improvement over the baseline word-by-word translation approach. In general, applying CLIR techniques in Web applications shows promise.

Footnote

Beitrag einer special topic section on multilingual information systems

Source

Journal of the American Society for Information Science and Technology. 57(2006) no.5, S.671-683

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          1.4142135 = tf(freq=2.0), with freq of:
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Source

Proceedings of ACM SIGIR 23rd International Conference on Research and Development in Information Retrieval. Ed. by N.J. Belkin, P. Ingwersen u. M.K. Leong