Search (13355 results, page 12 of 668)

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Abstract

The concepts of Shannon information and entropy have been applied to a number of information retrieval tasks such as to formalize the probabilistic model, to design practical retrieval systems, to cluster documents, and to model texture in image retrieval. In this report, the concept of entropy is used for a different purpose. It is shown that any positive Retrieval Status Value (RSV)based retrieval system may be conceived as a special probability space in which the amount of the associated Shannon information is being reduced; in this view, the retrieval system is referred to as Uncertainty Decreasing Operation (UDO). The concept of UDO is then proposed as a theoretical background for term and query discrimination Power, and it is applied to the computation of term and query discrimination values in the vector space retrieval model. Experimental evidence is given as regards such computation; the results obtained compare weIl to those obtained using vector-based calculation of term discrimination values. The UDO-based computation, however, presents advantages over the vectorbased calculation: It is faster, easier to assess and handle in practice, and its application is not restricted to the vector space model. Based an the ADI test collection, it is shown that the UDO-based Term Discrimination Value (TDV) weighting scheme yields better retrieval effectiveness than using the vector-based TDV weighting scheme. Also, experimental evidence is given to the intuition that the choice of an appropriate weighting scheure and similarity measure depends an collection properties, and thus the UDO approach may be used as a theoretical basis for this intuition.

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Content

A short discussion of using classification systems to organize the web, one of many such. The authors are both involved with BC2 and naturally think it is the best system for organizing information online. They list reasons why faceted classifications are best (e.g. no theoretical limits to specificity or exhaustivity; easier to handle complex subjects; flexible enough to accommodate different user needs) and take a brief look at how BC2 works. They conclude with a discussion of how and why it should be applied to online resources, and a plea for recognition of the importance of classification and subject analysis skills, even when full-text searching is available and databases respond instantly.

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Abstract

In recent years there has been an explosion of biological data stored in large central databases, tools to handle the data, and educational programs to train scientists in using bioinformatics resources. Still, the diffusion of bioinformatics within the biological cornmunity has yet to be extensively studied. In this study, the diffusion of two bioinformatics-related practices-using genomic databases and analyzing DNA and protein sequences-was investigated by analyzing MEDLINE records of 12 journals, representing various fields of biology. The diffusion of these practices between 1970 and 2003 follows an S-shaped curve typical of many innovations, beginning with slow growth, followed by a period of rapid linear growth, and finally reaching saturation. Similar diffusion patterns were found for both the use of genomic databases and biological sequence analysis, indicating the strong relationship between these practices. This study presents the surge in the use of genomic databases and analysis of biological sequences and proposes that these practices are fully diffused within the biological community. Extrapolating from these results, it suggests that taking a diffusion of innovations approach may be useful for researchers as well as for providers of bioinformatics applications and support services.

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Abstract

Khoo, Dai, and Loh examine new statistical methods for the identification of two and three character words in Chinese text. Some meaningful Chinese words are simple (independent units of one or more characters in a sentence that have independent meaning) but others are compounds of two or more simple words. In their segmentation they utilize the Modern Chinese Word Segmentation for Application of Information Processing, with some modifications to focus on meaningful words to do manual segmentation. About 37% of meaningful words are longer than 2 characters indicating a need to handle three and four character words. Four hundred sentences from news articles were manually broken into overlapping bi-grams and tri-grams. Using logistic regression, the log of the odds that such bi/tri-grams were meaningful words was calculated. Variables like relative frequency, document frequency, local frequency, and contextual and positional information, were incorporated in the model only if the concordance measure improved by at least 2% with their addition. For two- and three-character words relative frequency of adjacent characters and document frequency of overlapping bi-grams were found to be significant. Using measures of recall and precision where correct automatic segmentation is normalized either by manual segmentation or by automatic segmentation, the contextual information formula for 2 character words provides significantly better results than previous formulations and using both the 2 and 3 character formulations in combination significantly improves the 2 character results.

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Abstract

Ia McIlwaine discussed the importance of addressing the issue of lack of user-friendly access to systems for users located in many parts of the world. The diversity of the European classification systems is a case in point. A good example of how to handle this diversity, in her opinion, is the system at the Federal Technical University in Zurich. It has an especially user-friendly French and German interface which, along with UDC numbers, provides captions helpful for the average user. Having examined the problems associated with transnational copy cataloging she emphasized the consideration of cultural constructs in transnational cataloging. For example, the Islamic countries tend to adapt translations quite well in their classification schemes due to the fact that they possess greater literary warrant in Islam. China appears to have solved difficulties concerning transnational copy cataloging by incorporating Chinese materials into specialized classification schemes while utilizing MARC records in the national library for cataloging Western materials. Philip Bryant called for the balance of "utopian vision" with practicality. He stressed that existing bibliographic notations must be pushed to the limit in an attempt to function with the network He applauded the continuous work of Stephen Walker, Stephen Robertson and Jill Venner for developing an online catalog (OKAPI) which allows the average user to obtain help existing in the database by using the classification system already established in the data. He emphasized the significance of the BUBL project at the University of Strathclyde, where UDC subject divisions are employed as a means of dividing subjects into fairly large groupings.

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Abstract

A recent development in biological research is the emergence of bioinformatics, which employs novel informatics techniques to handle biological data. Although the importance of bioinformatics training is widely recognized, little attention has been paid to its effect on the acceptance of bioinformatics by biologists. In this study, the effect of training on biologists' acceptance of bioinformatics tools was tested using the technology acceptance model (TAM) as a theoretical framework. Ninety individuals participated in a field experiment during seven bioinformatics workshops. Pre- and post-intervention tests were used to measure perceived usefulness, perceived ease of use, and intended use of bioinformatics tools for primer design and microarray analysis - a simple versus a complex tool that is used for a simple and a complex task, respectively. Perceived usefulness and ease of use were both significant predictors of intended use of bioinformatics tools. After hands-on experience, intention to use both tools decreased. The perceived ease of use of the primer design tool increased but that of the microarray analysis tool decreased. It is suggested that hands-on training helps researchers to form realistic perceptions of bioinformatics tools, thereby enabling them to make informed decisions about whether and how to use them.

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Abstract

Text categorization is an important research area and has been receiving much attention due to the growth of the on-line information and of Internet. Automated text categorization is generally cast as a multi-class classification problem. Much of previous work focused on binary document classification problems. Support vector machines (SVMs) excel in binary classification, but the elegant theory behind large-margin hyperplane cannot be easily extended to multi-class text classification. In addition, the training time and scaling are also important concerns. On the other hand, other techniques naturally extensible to handle multi-class classification are generally not as accurate as SVM. This paper presents a simple and efficient solution to multi-class text categorization. Classification problems are first formulated as optimization via discriminant analysis. Text categorization is then cast as the problem of finding coordinate transformations that reflects the inherent similarity from the data. While most of the previous approaches decompose a multi-class classification problem into multiple independent binary classification tasks, the proposed approach enables direct multi-class classification. By using generalized singular value decomposition (GSVD), a coordinate transformation that reflects the inherent class structure indicated by the generalized singular values is identified. Extensive experiments demonstrate the efficiency and effectiveness of the proposed approach.

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Abstract

This article shows how a fuzzy ontology-based approach can improve semantic documents retrieval. After formally defining a fuzzy ontology and a fuzzy knowledge base, a special type of new fuzzy relationship called (semantic) correlation, which links the concepts or entities in a fuzzy ontology, is discussed. These correlations, first assigned by experts, are updated after querying or when a document has been inserted into a database. Moreover, in order to define a dynamic knowledge of a domain adapting itself to the context, it is shown how to handle a tradeoff between the correct definition of an object, taken in the ontology structure, and the actual meaning assigned by individuals. The notion of a fuzzy concept network is extended, incorporating database objects so that entities and documents can similarly be represented in the network. Information retrieval (IR) algorithm, using an object-fuzzy concept network (O-FCN), is introduced and described. This algorithm allows us to derive a unique path among the entities involved in the query to obtain maxima semantic associations in the knowledge domain. Finally, the study has been validated by querying a database using fuzzy recall, fuzzy precision, and coefficient variant measures in the crisp and fuzzy cases.

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Abstract

Statistical authorship attribution has a long history, culminating in the use of modern machine learning classification methods. Nevertheless, most of this work suffers from the limitation of assuming a small closed set of candidate authors and essentially unlimited training text for each. Real-life authorship attribution problems, however, typically fall short of this ideal. Thus, following detailed discussion of previous work, three scenarios are considered here for which solutions to the basic attribution problem are inadequate. In the first variant, the profiling problem, there is no candidate set at all; in this case, the challenge is to provide as much demographic or psychological information as possible about the author. In the second variant, the needle-in-a-haystack problem, there are many thousands of candidates for each of whom we might have a very limited writing sample. In the third variant, the verification problem, there is no closed candidate set but there is one suspect; in this case, the challenge is to determine if the suspect is or is not the author. For each variant, it is shown how machine learning methods can be adapted to handle the special challenges of that variant.

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Abstract

A significant fraction of queries in PubMed(TM) are multiterm queries without parsing instructions. Generally, search engines interpret such queries as collections of terms, and handle them as a Boolean conjunction of these terms. However, analysis of queries in PubMed(TM) indicates that many such queries are meaningful phrases, rather than simple collections of terms. In this study, we examine whether or not it makes a difference, in terms of retrieval quality, if such queries are interpreted as a phrase or as a conjunction of query terms. And, if it does, what is the optimal way of searching with such queries. To address the question, we developed an automated retrieval evaluation method, based on machine learning techniques, that enables us to evaluate and compare various retrieval outcomes. We show that the class of records that contain all the search terms, but not the phrase, qualitatively differs from the class of records containing the phrase. We also show that the difference is systematic, depending on the proximity of query terms to each other within the record. Based on these results, one can establish the best retrieval order for the records. Our findings are consistent with studies in proximity searching.

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Abstract

This article presents a bilingual video question answering (QA) system, namely BVideoQA, which allows users to retrieve Chinese videos through English or Chinese natural language questions. Our method first extracts an optimal one-to-one string pattern matching according to the proposed dense and long N-gram match. On the basis of the matched string patterns, it gives a passage score based on our term-weighting scheme. The main contributions of this approach to multimedia information retrieval literatures include: (a) development of a truly bilingual video QA system, (b) presentation of a robust bilingual passage retrieval algorithm to handle no-word-boundary languages such as Chinese and Japanese, (c) development of a large-scale bilingual video QA corpus for system evaluation, and (d) comparisons of seven top-performing retrieval methods under the fair conditions. The experimental studies indicate that our method is superior to other existing approaches in terms of precision and main rank reciprocal rates. When ported to English, encouraging empirical results also are obtained. Our method is very important to Asian-like languages since the development of a word tokenizer is optional.

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Abstract

A modern information retrieval system must have the capability to find, organize and present very different manifestations of information - such as text, pictures, videos or database records - any of which may be of relevance to the user. However, the concept of relevance, while seemingly intuitive, is actually hard to define, and it's even harder to model in a formal way. Lavrenko does not attempt to bring forth a new definition of relevance, nor provide arguments as to why any particular definition might be theoretically superior or more complete. Instead, he takes a widely accepted, albeit somewhat conservative definition, makes several assumptions, and from them develops a new probabilistic model that explicitly captures that notion of relevance. With this book, he makes two major contributions to the field of information retrieval: first, a new way to look at topical relevance, complementing the two dominant models, i.e., the classical probabilistic model and the language modeling approach, and which explicitly combines documents, queries, and relevance in a single formalism; second, a new method for modeling exchangeable sequences of discrete random variables which does not make any structural assumptions about the data and which can also handle rare events. Thus his book is of major interest to researchers and graduate students in information retrieval who specialize in relevance modeling, ranking algorithms, and language modeling.

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Abstract

Purpose - Throughout its history, information retrieval has struggled to handle contradictory needs of system oriented and user-oriented research. Information retrieval has gradually, starting in the 1960s, moved toward handling the needs of the user. This paper aims to consider the way boundaries toward the user and user-oriented perspectives are drawn, renegotiated and re-drawn. Design/methodology/approach - The central concept of relevance is seen as a boundary concept, complex and flexible, that is continuously redefined in order to manage boundaries. Five influential research papers from the 1960s and early 1970s are analysed in order to understand usage of the concept during a period when psychological and cognitive research tools began to be discussed as a possibility. Findings - Relevance does not only carry an explanatory function, but also serves a purpose relating to the identity of the field. Key contributions on research on relevance seems to, as a by-product, draw a boundary giving legitimacy to certain theoretical resources while demarcating against others. The strategies that are identified in the key texts are intent on finding, representing, justifying and strengthening a boundary that includes and excludes a reasonable amount of complexity associated with the user. Originality/value - The paper explores a central concept within information retrieval and information science in a new way. It also supplies a fresh perspective on the development of information retrieval during the 1960s and 1970s.

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Abstract

Information extraction (IE) and text summarization (TS) are powerful technologies for finding relevant pieces of information in text and presenting them to the user in condensed form. The ongoing information explosion makes IE and TS critical for successful functioning within the information society. These technologies face particular challenges due to the inherent multi-source nature of the information explosion. The technologies must now handle not isolated texts or individual narratives, but rather large-scale repositories and streams---in general, in multiple languages---containing a multiplicity of perspectives, opinions, or commentaries on particular topics, entities or events. There is thus a need to adapt existing techniques and develop new ones to deal with these challenges. This volume contains a selection of papers that present a variety of methodologies for content identification and extraction, as well as for content fusion and regeneration. The chapters cover various aspects of the challenges, depending on the nature of the information sought---names vs. events,--- and the nature of the sources---news streams vs. image captions vs. scientific research papers, etc. This volume aims to offer a broad and representative sample of studies from this very active research field.

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Abstract

In the late 1970s libraries suddenly recognized the importance of online catalogues. Advanced computer technology can handle massive bibliographic records and direct user inquiries (cataloguing and circulation) and online access is much more adequate than a cara and COM catalogue. There are several problems associated woth online public access catalogues as they are designed primarily for direct use by library patrons without knowledge of library cataloguing rules, yet the introduction of online catalogues extends the services that a library offers in the sense of efficiency, productivity and cooperation with other libraries, i. e. users and staff.

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Abstract

The paper introduces TinREAD (The Information Navigator for Readers), an integrated library system produced by IME Romania. The main feature of interest is the way TinREAD can handle a classification-based thesaurus in which verbal index terms are mapped to classification notations. It supports subject authority control interlinking the authority files (subject headings and UDC system). Authority files are used for indexing consistency. Although it is said that intellectual indexing is, unlike automated indexing, both subjective and inconsistent, TinREAD is using intellectual indexing as input (the UDC notations assigned to documents) for the automated indexing resulting from the implementation of a thesaurus structure based on UDC. Each UDC notation is represented by a UNIMARC subject heading record as authority data. One classification notation can be used to search simultaneously into more than one corresponding thesaurus. This way natural language terms are used in indexing and, at the same time, the link with the corresponding classification notation is kept. Additionally, the system can also manage multilingual data for the authority files. This, together with other characteristics of TinREAD are largely discussed and illustrated in the paper. Problems encountered and possible solutions to tackle them are shown.

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Abstract

Managing the constant flow of incoming messages is a daily challenge faced by knowledge workers who use technologies such as e-mail and other digital communication tools. This study focuses on the most ubiquitous of these technologies, e-mail, and unobtrusively explores the ongoing inbox-management activities of thousands of users worldwide over a period of 8 months. The study describes the dynamics of these inboxes throughout the day and the week as users strive to handle incoming messages, read them, classify them, respond to them in a timely manner, and archive them for future reference, all while carrying out the daily tasks of knowledge workers. It then tests several hypotheses about the influence of specific inbox-management behaviors in mitigating the causes of e-mail overload, and proposes a continuous index that quantifies one of these inbox-management behaviors. This inbox clearing index (ICI) expands on the widely cited trichotomous classification of users into frequent filers, spring cleaners, and no filers, as suggested by Whittaker and Sidner (1996). We propose that the ICI allows shifting the focus, from classifying users to characterizing a diversity of user behaviors and measuring the relationships between these behaviors and desired outcomes.

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Abstract

This paper reports findings from a study designed to gain broader understanding of sensemaking activities using the Data/Frame Theory as the analytical framework. Although this theory is one of the dominant models of sensemaking, it has not been extensively tested with a range of sensemaking tasks. The tasks discussed here focused on making sense of structures rather than processes or narratives. Eleven researchers were asked to construct understanding of how a scientific community in a particular domain is organized (e.g., people, relationships, contributions, factors) by exploring the concept of "influence" in academia. This topic was chosen because, although researchers frequently handle this type of task, it is unlikely that they have explicitly sought this type of information. We conducted a think-aloud study and semistructured interviews with junior and senior researchers from the human-computer interaction (HCI) domain, asking them to identify current leaders and rising stars in both HCI and chemistry. Data were coded and analyzed using the Data/Frame Model to both test and extend the model. Three themes emerged from the analysis: novices and experts' sensemaking activity chains, constructing frames through indicators, and characteristics of structure tasks. We propose extensions to the Data/Frame Model to accommodate structure sensemaking.

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Content

Vgl.: https://dspace.mit.edu/bitstream/handle/1721.1/4821/RLE-TR-247-03150899.pdf?sequence=1.

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Abstract

The Brazilian Government has made a massive volume of structured, semi-structured and non-structured public data available on the web to ensure that the administration is as transparent as possible. Subsequently, providing applications with enough capability to handle this "big data environment" so that vital and decisive information is readily accessible, has become a tremendous challenge. In this environment, data processing is done via new approaches in the area of information and computer science, involving technologies and processes for collecting, representing, storing and disseminating information. Along these lines, this paper presents a conceptual model, the technical architecture and the prototype implementation of a tool, denominated DBgoldbr, designed to classify government public information with the help of ontologies, by transforming open data into open linked data. To achieve this objective, we used "soft system methodology" to identify problems, to collect users needs and to design solutions according to the objectives of specific groups. The DBgoldbr tool was designed to facilitate the search for open data made available by many Brazilian government institutions, so that this data can be reused to support the evaluation and monitoring of social programs, in order to support the design and management of public policies.