Search (59 results, page 1 of 3)

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Date

10. 8.2001 21:23:46

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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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.

Date

23. 2.2009 18:36:23

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Abstract

The World Wide Web presents significant opportunities for business intelligence analysis as it can provide information about a company's external environment and its stakeholders. Traditional business intelligence analysis on the Web has focused on simple keyword searching. Recently, it has been suggested that the incoming links, or backlinks, of a company's Web site (i.e., other Web pages that have a hyperlink pointing to the company of Interest) can provide important insights about the company's "online communities." Although analysis of these communities can provide useful signals for a company and information about its stakeholder groups, the manual analysis process can be very time-consuming for business analysts and consultants. In this article, we present a tool called Redips that automatically integrates backlink meta-searching and text-mining techniques to facilitate users in performing such business intelligence analysis on the Web. The architectural design and implementation of the tool are presented in the article. To evaluate the effectiveness, efficiency, and user satisfaction of Redips, an experiment was conducted to compare the tool with two popular business Intelligence analysis methods-using backlink search engines and manual browsing. The experiment results showed that Redips was statistically more effective than both benchmark methods (in terms of Recall and F-measure) but required more time in search tasks. In terms of user satisfaction, Redips scored statistically higher than backlink search engines in all five measures used, and also statistically higher than manual browsing in three measures.

Date

7. 3.2007 16:19:23

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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.

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Abstract

The purpose of this paper is to identify and explain the role of individual learning and development in acquiring tacit knowledge in the context of the inexorable and intense continuous change (technological and otherwise) that characterizes our society today, and also to investigate the software (SW) sector, which is at the core of contemporary continuous change and is a paradigm of effective and intrinsic knowledge sharing (KS). This makes the SW sector unique and different from others where KS is so hard to implement. Design/methodology/approach The study employed an inductive qualitative approach based on a multi-case study approach, composed of three successful SW companies in China. These companies are representative of the fabric of the sector, namely a small- and medium-sized enterprise, a large private company and a large state-owned enterprise. The fieldwork included 44 participants who were interviewed using a semi-structured script. The interview data were coded and interpreted following the Straussian grounded theory pattern of open coding, axial coding and selective coding. The process of interviewing was stopped when theoretical saturation was achieved after a careful process of theoretical sampling.
Findings The findings of this research suggest that individual learning and development are deemed to be the fundamental feature for professional success and survival in the continuously changing environment of the SW industry today. However, individual learning was described by the participants as much more than a mere individual process. It involves a collective and participatory effort within the organization and the sector as a whole, and a KS process that transcends organizational, cultural and national borders. Individuals in particular are mostly motivated by the pressing need to face and adapt to the dynamic and changeable environments of today's digital society that is led by the sector. Software practitioners are continuously in need of learning, refreshing and accumulating tacit knowledge, partly because it is required by their companies, but also due to a sound awareness of continuous technical and technological changes that seem only to increase with the advances of information technology. This led to a clear theoretical understanding that the continuous change that faces the sector has led to individual acquisition of culture and somatic knowledge that in turn lay the foundation for not only the awareness of the need for continuous individual professional development but also for the creation of habitus related to KS and continuous learning. Originality/value The study reported in this paper shows that there is a theoretical link between the existence of conducive organizational and sector-wide somatic and cultural knowledge, and the success of KS practices that lead to individual learning and development. Therefore, the theory proposed suggests that somatic and cultural knowledge are crucial drivers for the creation of habitus of individual tacit knowledge acquisition. The paper further proposes a habitus-driven individual development (HDID) Theoretical Model that can be of use to both academics and practitioners interested in fostering and developing processes of KS and individual development in knowledge-intensive organizations.

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Abstract

In this article, we report research on an algorithmic approach to alleviating search uncertainty in a large information space. Grounded on object filtering, automatic indexing, and co-occurence analysis, we performed a large-scale experiment using a parallel supercomputer (SGI Power Challenge) to analyze 400.000+ abstracts in an INSPEC computer engineering collection. Two system-generated thesauri, one based on a combined object filtering and automatic indexing method, and the other based on automatic indexing only, were compaed with the human-generated INSPEC subject thesaurus. Our user evaluation revealed that the system-generated thesauri were better than the INSPEC thesaurus in 'concept recall', but in 'concept precision' the 3 thesauri were comparable. Our analysis also revealed that the terms suggested by the 3 thesauri were complementary and could be used to significantly increase 'variety' in search terms the thereby reduce search uncertainty

Theme

Konzeption und Anwendung des Prinzips Thesaurus
Semantisches Umfeld in Indexierung u. Retrieval

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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

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Abstract

Presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge based systems and to alleviate the limitation of the manual browsing approach, develops 2 spreading activation based algorithms for concept exploration in large, heterogeneous networks of concepts (eg multiple thesauri). One algorithm, which is based on the symbolic AI paradigma, performs a conventional branch-and-bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The 2nd algorithm, which is absed on the neural network approach, executes the Hopfield net parallel relaxation and convergence process to identify 'convergent' concepts for some initial queries (a parallel, heuristic search process). Tests these 2 algorithms on a large text-based knowledge network of about 13.000 nodes (terms) and 80.000 directed links in the area of computing technologies

Theme

Konzeption und Anwendung des Prinzips Thesaurus

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Abstract

Social networks evolve over time with the addition and removal of nodes and links to survive and thrive in their environments. Previous studies have shown that the link-formation process in such networks is influenced by a set of facilitators. However, there have been few empirical evaluations to determine the important facilitators. In a research partnership with law enforcement agencies, we used dynamic social-network analysis methods to examine several plausible facilitators of co-offending relationships in a large-scale narcotics network consisting of individuals and vehicles. Multivariate Cox regression and a two-proportion z-test on cyclic and focal closures of the network showed that mutual acquaintance and vehicle affiliations were significant facilitators for the network under study. We also found that homophily with respect to age, race, and gender were not good predictors of future link formation in these networks. Moreover, we examined the social causes and policy implications for the significance and insignificance of various facilitators including common jails on future co-offending. These findings provide important insights into the link-formation processes and the resilience of social networks. In addition, they can be used to aid in the prediction of future links. The methods described can also help in understanding the driving forces behind the formation and evolution of social networks facilitated by mobile and Web technologies.

Date

22. 3.2009 18:50:30

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Abstract

Reports an algorithmic approach to the automatic generation of thesauri for electronic community systems. The techniques used included terms filtering, automatic indexing, and cluster analysis. The testbed for the research was the Worm Community System, which contains a comprehensive library of specialized community data and literature, currently in use by molecular biologists who study the nematode worm. The resulting worm thesaurus included 2709 researchers' names, 798 gene names, 20 experimental methods, and 4302 subject descriptors. On average, each term had about 90 weighted neighbouring terms indicating relevant concepts. The thesaurus was developed as an online search aide. Tests the worm thesaurus in an experiment with 6 worm researchers of varying degrees of expertise and background. The experiment showed that the thesaurus was an excellent 'memory jogging' device and that it supported learning and serendipitous browsing. Despite some occurrences of obvious noise, the system was useful in suggesting relevant concepts for the researchers' queries and it helped improve concept recall. With a simple browsing interface, an automatic thesaurus can become a useful tool for online search and can assist researchers in exploring and traversing a dynamic and complex electronic community system

Theme

Konzeption und Anwendung des Prinzips Thesaurus

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Abstract

The increasing amount of publicly available literature and experimental data in biomedicine makes it hard for biomedical researchers to stay up-to-date. Genescene is a toolkit that will help alleviate this problem by providing an overview of published literature content. We combined a linguistic parser with Concept Space, a co-occurrence based semantic net. Both techniques extract complementary biomedical relations between noun phrases from MEDLINE abstracts. The parser extracts precise and semantically rich relations from individual abstracts. Concept Space extracts relations that hold true for the collection of abstracts. The Gene Ontology, the Human Genome Nomenclature, and the Unified Medical Language System, are also integrated in Genescene. Currently, they are used to facilitate the integration of the two relation types, and to select the more interesting and high-quality relations for presentation. A user study focusing on p53 literature is discussed. All MEDLINE abstracts discussing p53 were processed in Genescene. Two researchers evaluated the terms and relations from several abstracts of interest to them. The results show that the terms were precise (precision 93%) and relevant, as were the parser relations (precision 95%). The Concept Space relations were more precise when selected with ontological knowledge (precision 78%) than without (60%).

Date

22. 7.2006 14:26:01

Footnote

Beitrag in einem special issue on bioinformatics

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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.

Footnote

Beitrag innerhalb der special topic section des Heftes: "Document search interface design"

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Theme

Konzeption und Anwendung des Prinzips Thesaurus

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Abstract

With the growth of hypertext and multimedia applications that support and encourage browsing it is time to take a penetrating look at browsing behaviour. Several dimensions of browsing are exemined, to find out: first, what is browsing and what cognitive processes are associated with it: second, is there a browsing strategy, and if so, are there any differences between how subject-area experts and novices browse; and finally, how can this knowledge be applied to improve the design of hypertext systems. Two groups of students, subject-area experts and novices, were studied while browsing a Macintosh HyperCard application on the subject The Vietnam War. A protocol analysis technique was used to gather and analyze data. Components of the GOMS model were used to describe the goals, operators, methods, and selection rules observed: Three browsing strategies were identified: (1) search-oriented browse, scanning and and reviewing information relevant to a fixed task; (2) review-browse, scanning and reviewing intersting information in the presence of transient browse goals that represent changing tasks, and (3) scan-browse, scanning for interesting information (without review). Most subjects primarily used review-browse interspersed with search-oriented browse. Within this strategy, comparisons between subject-area experts and novices revealed differences in tactics: experts browsed in more depth, seldom used referential links, selected different kinds of topics, and viewed information differently thatn did novices. Based on these findings, suggestions are made to hypertext developers

Source

IEEE transactions on systems, man and cybernetics. 22(1992) no.5, S.865-884

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Abstract

With the rapid proliferation of Internet technologies and applications, misuse of online messages for inappropriate or illegal purposes has become a major concern for society. The anonymous nature of online-message distribution makes identity tracing a critical problem. We developed a framework for authorship identification of online messages to address the identity-tracing problem. In this framework, four types of writing-style features (lexical, syntactic, structural, and content-specific features) are extracted and inductive learning algorithms are used to build feature-based classification models to identify authorship of online messages. To examine this framework, we conducted experiments on English and Chinese online-newsgroup messages. We compared the discriminating power of the four types of features and of three classification techniques: decision trees, backpropagation neural networks, and support vector machines. The experimental results showed that the proposed approach was able to identify authors of online messages with satisfactory accuracy of 70 to 95%. All four types of message features contributed to discriminating authors of online messages. Support vector machines outperformed the other two classification techniques in our experiments. The high performance we achieved for both the English and Chinese datasets showed the potential of this approach in a multiple-language context.

Date

22. 7.2006 16:14:37

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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.

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Abstract

Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to 'intelligent' information retrieval and indexing. More recently, information science researchers have tfurned to other newer inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information systems. In this article, we first provide an overview of these newer techniques and their use in information retrieval research. In order to femiliarize readers with the techniques, we present 3 promising methods: the symbolic ID3 algorithm, evolution-based genetic algorithms, and simulated annealing. We discuss their knowledge representations and algorithms in the unique context of information retrieval

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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.

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Abstract

It is important for education in computer science and information systems to keep up to date with the latest development in technology. With the rapid development of the Internet and the Web, many schools have included Internet-related technologies, such as Web search engines and e-commerce, as part of their curricula. Previous research has shown that it is effective to use search engine development tools to facilitate students' learning. However, the effectiveness of these tools in the classroom has not been evaluated. In this article, we review the design of three search engine development tools, SpidersRUs, Greenstone, and Alkaline, followed by an evaluation study that compared the three tools in the classroom. In the study, 33 students were divided into 13 groups and each group used the three tools to develop three independent search engines in a class project. Our evaluation results showed that SpidersRUs performed better than the two other tools in overall satisfaction and the level of knowledge gained in their learning experience when using the tools for a class project on Internet applications development.

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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.