Search

This paper deals with the specific features of historical papers relevant for information retrieval and bibliometrics. The analysis is based mainly on the citation indexes accessible under the Web of Science (WoS) but also on field-specific databases: the Chemical Abstracts Service (CAS) literature database and the INSPEC database. First, the journal coverage of the WoS (in particular of the WoS Century of Science archive), the limitations of specific search fields as well as several database errors are discussed. Then, the problem of misspelled citations and their 'mutations' is demonstrated by a few typical examples. Complex author names, complicated journal names, and other sources of errors that result from prior citation practice are further issues. Finally, some basic phenomena limiting the meaning of citation counts of historical papers are presented and explained. [ABSTRACT FROM AUTHOR]

Social tagging and controlled indexing both facilitate access to information resources. Given the increasing popularity of social tagging and the limitations of controlled indexing (primarily cost and scalability), it is reasonable to investigate to what degree social tagging could substitute for controlled indexing. In this study, we compared CiteULike tags to Medical Subject Headings ( MeSH) terms for 231,388 citations indexed in MEDLINE. In addition to descriptive analyses of the data sets, we present a paper-by-paper analysis of tags and MeSH terms: the number of common annotations, Jaccard similarity, and coverage ratio. In the analysis, we apply three increasingly progressive levels of text processing, ranging from normalization to stemming, to reduce the impact of lexical differences. Annotations of our corpus consisted of over 76,968 distinct tags and 21,129 distinct MeSH terms. The top 20 tags/MeSH terms showed little direct overlap. On a paper-by-paper basis, the number of common annotations ranged from 0.29 to 0.5 and the Jaccard similarity from 2.12% to 3.3% using increased levels of text processing. At most, 77,834 citations (33.6%) shared at least one annotation. Our results show that CiteULike tags and MeSH terms are quite distinct lexically, reflecting different viewpoints/processes between social tagging and controlled indexing. [ABSTRACT FROM AUTHOR]

Modern information retrieval systems use keywords within documents as indexing terms for search of relevant documents. As Chinese is an ideographic character-based language, the words in the texts are not delimited by white spaces. Indexing of Chinese documents is impossible without a proper segmentation algorithm. Many Chinese segmentation algorithms have been proposed in the past. Traditional segmentation algorithms cannot operate without a large dictionary or a large corpus of training data. Nowadays, the Web has become the largest corpus that is ideal for Chinese segmentation. Although most search engines have problems in segmenting texts into proper words, they maintain huge databases of documents and frequencies of character sequences in the documents. Their databases are important potential resources for segmentation. In this paper, we propose a segmentation algorithm by mining Web data with the help of search engines. On the other hand, the Romanized pinyin of Chinese language indicates boundaries of words in the text. Our algorithm is the first to utilize the Romanized pinyin to segmentation. It is the first unified segmentation algorithm for the Chinese language from different geographical areas, and it is also domain independent because of the nature of the Web. Experiments have been conducted on the datasets of a recent Chinese segmentation competition. The results show that our algorithm outperforms the traditional algorithms in terms of precision and recall. Moreover, our algorithm can effectively deal with the problems of segmentation ambiguity, new word (unknown word) detection, and stop words. [ABSTRACT FROM AUTHOR]

This paper reports on an investigation into the scholarly impact of the TRECVid (Text Retrieval and Evaluation Conference, Video Retrieval Evaluation) benchmarking conferences between 2003 and 2009. The contribution of TRECVid to research in video retrieval is assessed by analyzing publication content to show the development of techniques and approaches over time and by analyzing publication impact through publication numbers and citation analysis. Popular conference and journal venues for TRECVid publications are identified in terms of number of citations received. For a selection of participants at different career stages, the relative importance of TRECVid publications in terms of citations vis à vis their other publications is investigated. TRECVid, as an evaluation conference, provides data on which research teams 'scored' highly against the evaluation criteria and the relationship between 'top scoring' teams at TRECVid and the 'top scoring' papers in terms of citations is analyzed. A strong relationship was found between 'success' at TRECVid and 'success' at citations both for high scoring and low scoring teams. The implications of the study in terms of the value of TRECVid as a research activity, and the value of bibliometric analysis as a research evaluation tool, are discussed. [ABSTRACT FROM AUTHOR]

One of the most significant inaccuracies of bibliometric databases is that of omitted citations, namely, missing electronic links between a paper of interest and some citing papers, which are (or should be) covered by the database. This paper proposes a novel approach for estimating a database's omitted-citation rate, based on the combined use of 2 or more bibliometric databases. A statistical model is also presented for (a) estimating the 'true' number of citations received by individual papers or sets of papers, and (b) defining an appropriate confidence interval. The proposed approach could represent a first step towards the definition of a standard for evaluating the accuracy level of databases. [ABSTRACT FROM AUTHOR]

Previous analyses identified research on environmental tobacco smoke to be subject to strong fluctuations as measured by both quantitative and qualitative indicators. The evolution of search algorithms (based on the Web of Science and Web of Knowledge database platforms) was used to show the impact of errors of omission and commission in the outcomes of scientometric research. Optimization of the search algorithm led to the complete reassessment of previously published findings on the performance of environmental tobacco smoke research. Instead of strong continuous growth, the field of environmental tobacco smoke research was shown to experience stagnation or slow growth since mid-1990s when evaluated quantitatively. Qualitative analysis revealed steady but slow increase in the citation rate and decrease in uncitedness. Country analysis revealed the North- European countries as leaders in environmental tobacco smoke research (when the normalized results were evaluated both quantitatively and qualitatively), whereas the United States ranked first only when assessing the total number of papers produced. Scientometric research artifacts, including both errors of omission and commission, were shown to be capable of completely obscuring the real output of the chosen research field. [ABSTRACT FROM AUTHOR]

Research patterns could enhance understanding of the Information Systems (IS) field. Citation analysis is the methodology commonly used to determine such research patterns. In this study, the citation methodology is applied to one of the top-ranked Information Systems conferences—International Conference on Information Systems (ICIS). Information is extracted from papers in the proceedings of ICIS 2000 to 2002. A total of 145 base articles and 4,226 citations are used. Research patterns are obtained using total citations, citations per journal or conference, and overlapping citations. We then provide the citation ranking of journals and conferences. We also examine the difference between the citation ranking in this study and the ranking of IS journals and IS conferences in other studies. Based on the comparison, we confirm that IS research is a multidisciplinary research area. We also identify the most cited papers and authors in the IS research area, and the organizations most active in producing papers in the top-rated IS conference. We discuss the findings and implications of the study. [ABSTRACT FROM AUTHOR]

A brief communication appearing in this journal ranked UK-based LIS and (some) IR academics by their h-index using data derived from the Thomson ISI Web of Science™ (WoS). In this brief communication, the same academics were re-ranked, using other popular citation databases. It was found that for academics who publish more in computer science forums, their h was significantly different due to highly cited papers missed by WoS; consequently, their rank changed substantially. The study was widened to a broader set of UK-based LIS and IR academics in which results showed similar statistically significant differences. A variant of h, hmx, was introduced that allowed a ranking of the academics using all citation databases together. [ABSTRACT FROM AUTHOR]

Web citations have been proposed as comparable to, even replacements for, bibliographic citations, notably in assessing the academic impact of work in promotion and tenure decisions. We compared bibliographic and Web citations to articles in 46 journals in library and information science. For most journals (57%), Web citations correlated significantly with both bibliographic citations listed in the Social Sciences Citation Index and the ISI's Journal Impact Factor. Many of the Web citations represented intellectual impact, coming from other papers posted on the Web (30%) or from class readings lists (12%). Web citation counts were typically higher than bibliographic citation counts for the same article. Journals with more Web citations tended to have Web sites that provided tables of contents on the Web, while less cited journals did not have such publicity. The number of Web citations to journal articles increased from 1992 to 1997. [ABSTRACT FROM AUTHOR]

Young undergraduate college students are often described as 'digital natives,' presumed to prefer living and working in completely digital information environments. In reality, their world is part-paper/part-digital, in constant transition among successive forms of digital storage and communication devices. Studying for a degree is the daily work of these young people, and effective management of paper and digital academic materials and resources contributes crucially to their success in life. Students must also constantly manage their work against deadlines to meet their course and university requirements. This study, following the ' Personal Information Management' ( PIM) paradigm, examines student academic information management under these various constraints and pressures. A total of 41 18- to 22-year-old students were interviewed and observed regarding the content, structure, and uses of their immediate working environment within their dormitory rooms. Students exhibited remarkable creativity and variety in the mixture of automated and manual resources and devices used to support their academic work. The demands of a yearlong procession of assignments, papers, projects, and examinations increase the importance of time management activities and influence much of their behavior. Results provide insights on student use of various kinds of information technology and their overall planning and management of information associated with their studies. [ABSTRACT FROM AUTHOR]

Interdisciplinary research has been attracting more attention in recent decades. In this article, we compare the similarity between scientific research domains and quantifying the temporal similarities of domains. We narrowed our study to three research domains: information retrieval ( IR), database ( DB), and World Wide Web ( W3), because the rapid development of the W3 domain substantially attracted research efforts from both IR and DB domains and introduced new research questions to these two areas. Most existing approaches either employed a content-based technique or a cocitation or coauthorship network-based technique to study the development trend of a research area. In this work, we proposed an effective way to quantify the similarities among different research domains by incorporating content similarity and coauthorship network similarity. Experimental results on DBLP (DataBase systems and Logic Programming) data related to IR, DB, and W3 domains showed that the W3 domain was getting closer to both IR and DB whereas the distance between IR and DB remained relatively constant. In addition, comparing to IR and W3 with the DB domain, the DB domain was more conservative and evolved relatively slower. [ABSTRACT FROM AUTHOR]

This article aims to identify whether different weighted PageRank algorithms can be applied to author citation networks to measure the popularity and prestige of a scholar from a citation perspective. Information retrieval (IR) was selected as a test field and data from 1956-2008 were collected from Web of Science. Weighted PageRank with citation and publication as weighted vectors were calculated on author citation networks. The results indicate that both popularity rank and prestige rank were highly correlated with the weighted PageRank. Principal component analysis was conducted to detect relationships among these different measures. For capturing prize winners within the IR field, prestige rank outperformed all the other measures. [ABSTRACT FROM AUTHOR]

In the past several years studies have started to appear comparing the accuracies of various science mapping approaches. These studies primarily compare the cluster solutions resulting from different similarity approaches, and give varying results. In this study we compare the accuracies of cluster solutions of a large corpus of 2,153,769 recent articles from the biomedical literature (2004-2008) using four similarity approaches: co-citation analysis, bibliographic coupling, direct citation, and a bibliographic coupling-based citation-text hybrid approach. Each of the four approaches can be considered a way to represent the research front in biomedicine, and each is able to successfully cluster over 92% of the corpus. Accuracies are compared using two metrics-within-cluster textual coherence as defined by the Jensen-Shannon divergence, and a concentration measure based on the grant-to-article linkages indexed in MEDLINE. Of the three pure citation-based approaches, bibliographic coupling slightly outperforms co-citation analysis using both accuracy measures; direct citation is the least accurate mapping approach by far. The hybrid approach improves upon the bibliographic coupling results in all respects. We consider the results of this study to be robust given the very large size of the corpus, and the specificity of the accuracy measures used. [ABSTRACT FROM AUTHOR]

This paper studies how varied damping factors in the PageRank algorithm influence the ranking of authors and proposes weighted PageRank algorithms. We selected the 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculated the ranks of these 108 authors based on PageRank with the damping factor ranging from 0.05 to 0.95. In order to test the relationship between different measures, we compared PageRank and weighted PageRank results with the citation ranking, h-index, and centrality measures. We found that in our author co-citation network, citation rank is highly correlated with PageRank with different damping factors and also with different weighted PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index rank does not significantly correlate with centrality measures but does significantly correlate with other measures. The key factors that have impact on the PageRank of authors in the author co-citation network are being co-cited with important authors. [ABSTRACT FROM AUTHOR]

This paper provides empirical support for some of the key assumptions guiding the design of data fusion methods. It computes and analyzes the overlap structures between the search results of retrieval systems that participated in the short, long, and manual tracks in TREC 3, 6, 7, and 8 to examine what can be learned to infer a document's probability of being relevant. This paper shows that the potential relevance of a document increases exponentially as the number of systems retrieving it increases—called the Authority Effect. It also shows that documents higher up in ranked lists and found by more systems are more likely to be relevant—called the Ranking Effect. A contribution of this paper is that it shows that the Authority and Ranking Effects can be observed regardless of whether a query is generated manually or automatically and short or long queries are used. Further, it is illustrated that the Authority and Ranking Effects can be observed if the result sets of random groupings of five retrieval systems are compared and only the top 50 results are used in the overlap computation. Also discussed is how the Authority and Ranking Effects can help explain why major data fusion methods perform well. [ABSTRACT FROM AUTHOR]

The goal of this study was to propose novel cyberlearning resource-based scientific referential metadata for an assortment of publications and scientific topics, in order to enhance the learning experiences of students and scholars in a cyberinfrastructure-enabled learning environment. By using information retrieval and meta-search approaches, different types of referential metadata, such as related Wikipedia pages, data sets, source code, video lectures, presentation slides, and (online) tutorials for scientific publications and scientific topics will be automatically retrieved, associated, and ranked. In order to test our method of automatic cyberlearning referential metadata generation, we designed a user experiment to validate the quality of the metadata for each scientific keyword and publication and resource-ranking algorithm. Evaluation results show that the cyberlearning referential metadata retrieved via meta-search and statistical relevance ranking can help students better understand the essence of scientific keywords and publications. [ABSTRACT FROM AUTHOR]

The paper discusses and analyzes the notion of information quality in terms of a pragmatic philosophy of language. It is argued that the notion of information quality is of great importance, and needs to be situated better within a sound philosophy of information to help frame information quality in a broader conceptual light. It is found that much research on information quality conceptualizes information quality as either an inherent property of the information itself, or as an individual mental construct of the users. The notion of information quality is often not situated within a philosophy of information. This paper outlines a conceptual framework in which information is regarded as a semiotic sign, and extends that notion with Paul Grice's pragmatic philosophy of language to provide a conversational notion of information quality that is contextual and tied to the notion of meaning. [ABSTRACT FROM AUTHOR]

A recent study in information science (IS), Lykke and Eslau (; hereafter L&E), raises important issues concerning the value of human indexing and basic theories of indexing and information retrieval, as well as the use of quantitative and qualitative approaches in IS and the underlying theories of knowledge informing the field. The present article uses L&E as the point of departure for demonstrating in what way more social and interpretative understandings may provide fruitful improvements for research in indexing, knowledge organization, and information retrieval. The artcle is motivated by the observation that philosophical contributions tend to be ignored in IS if they are not directly formed as criticisms or invitations to dialogs. It is part of the author's ongoing publication of articles about philosophical issues in IS and it is intended to be followed by analyzes of other examples of contributions to core issues in IS. Although it is formulated as a criticism of a specific paper, it should be seen as part of a general discussion of the philosophical foundation of IS and as a support to the emerging social paradigm in this field. [ABSTRACT FROM AUTHOR]

Following the transition from print journals to electronic (hybrid) journals in the past decade, usage metrics have become an interesting complement to citation metrics. In this article we investigate the similarities of and differences between usage and citation indicators for pharmacy and pharmacology journals and relate the results to a previous study on oncology journals. For the comparison at journal level we use the classical citation indicators as defined in the Journal Citation Reports and compute the corresponding usage indicators. At the article level we not only relate download and citation counts to each other but also try to identify the possible effect of citations upon subsequent downloads. Usage data were provided by ScienceDirect both at the journal level and, for a few selected journals, on a paper-by-paper basis. The corresponding citation data were retrieved from the Web of Science and Journal Citation Reports. Our analyses show that electronic journals have become generally accepted over the last decade. While the supply of ScienceDirect pharma journals rose by 50% between 2001 and 2006, the total number of article downloads (full-text articles [FTAs]) multiplied more than 5-fold in the same period. This also impacted the pattern of scholarly communication (strong increase in the immediacy index) in the past few years. Our results further reveal a close relation between citation and download frequencies. We computed a high correlation at the journal level when using absolute values and a moderate to high correlation when relating usage and citation impact factors. At the article level the rank correlation between downloads and citations was only medium-sized. Differences between downloads and citations exist in terms of obsolescence characteristics. While more than half of the articles are downloaded in the publication year or 1 year later, the median cited half-life was nearly 6 years for our journal sample. Our attempt to reveal a direct influence of citations upon downloads proved not to be feasible. [ABSTRACT FROM AUTHOR]

In this article a set of requirements for the design of a personal document management system is presented, based on the results of three research studies (Bondarenko, 2006; Bondarenko & Janssen, 2005; Bondarenko & Janssen, 2009). We propose a framework, based on layers of task decomposition, that helps to understand the needs of information workers with regard to personal document and task management. Relevant user processes are described and requirements for a document-management system are derived for each layer. The derived requirements are compared to related studies, and implications for system design are discussed. [ABSTRACT FROM AUTHOR]

In 1975 Tefko Saracevic declared “the subject knowledge view” to be the most fundamental perspective of relevance. This paper examines the assumptions in different views of relevance, including “the system's view” and “the user's view” and offers a reinterpretation of these views. The paper finds that what was regarded as the most fundamental view by Saracevic in 1975 has not since been considered (with very few exceptions). Other views, which are based on less fruitful assumptions, have dominated the discourse on relevance in information retrieval and information science. Many authors have reexamined the concept of relevance in information science, but have neglected the subject knowledge view, hence basic theoretical assumptions seem not to have been properly addressed. It is as urgent now as it was in 1975 seriously to consider “the subject knowledge view” of relevance (which may also be termed “the epistemological view”). The concept of relevance, like other basic concepts, is influenced by overall approaches to information science, such as the cognitive view and the domain-analytic view. There is today a trend toward a social paradigm for information science. This paper offers an understanding of relevance from such a social point of view. [ABSTRACT FROM AUTHOR]

Many writers of structured abstracts spend a good deal of time revising and polishing their texts—but is it worth it? Do readers notice the difference? In this paper we report three studies of readers using rating scales to judge (electronically) the clarity of an original and a revised abstract, both as a whole and in its constituent parts. In Study 1, with approximately 250 academics and research workers, we found some significant differences in favor of the revised abstract, but in Study 2, with approximately 210 information scientists, we found no significant effects. Pooling the data from Studies 1 and 2, however, in Study 3, led to significant differences at a higher probability level between the perception of the original and revised abstract as a whole and between the same components as found in Study 1. These results thus indicate that the revised abstract as a whole, as well as certain specific components of it, were judged significantly clearer than the original one. In short, the results of these experiments show that readers can and do perceive differences between original and revised texts—sometimes—and that therefore these efforts are worth the time and effort. [ABSTRACT FROM AUTHOR]

The amount of knowledge accumulated in published scientific papers has increased due to the continuing progress being made in scientific research. Since numerous papers have only reported fragments of scientific facts, there are possibilities for discovering new knowledge by connecting these facts. We therefore developed a system called BioTermNet to draft a conceptual network with hybrid methods of information extraction and information retrieval. Two concepts are regarded as related in this system if (a) their relationship is clearly described in MEDLINE abstracts or (b) they have distinctively co-occurred in abstracts. PRIME data, including protein interactions and functions extracted by NLP techniques, are used in the former, and the Singhal-measure for information retrieval is used in the latter. Relationships that are not clearly or directly described in an abstract can be extracted by connecting multiple concepts. To evaluate how well this system performs, Swanson's association between Raynaud's disease and fish oil and that between migraine and magnesium were tested with abstracts that had been published before the discovery of these associations. The result was that when start and end concepts were given, plausible and understandable intermediate concepts connecting them could be detected. When only the start concept was given, not only the focused concept (magnesium and fish oil) but also other probable concepts could be detected as related concept candidates. Finally, this system was applied to find diseases related to the BRCA1 gene. Some other new potentially related diseases were detected along with diseases whose relations to BRCA1 were already known. The BioTermNet is available at http://btn.ontology.ims.u-tokyo.ac.jp. [ABSTRACT FROM AUTHOR]

The article cites a study that compared accessibility and use of information sources among more than two hundred Israeli computer scientists and software engineers, employed in industry and academy. Scientists need and use information to create new scientific information, claiming that their research is new, different and enhances existing knowledge. Engineers need and consume information to make decisions, primarily aiming to create and produce new products based upon designs and programs and using various tools and techniques, rather than to contribute original research to professional literature. They are less interested in theories. For most information sources, two groups differed significantly and consistently in the two phases checked and in accessibility, which may he partially explained by differences in workplace, role, and derived goals, as indicated in former studies. Printed professional journals as well as printed and electronic conference meeting papers were reported as consistently more accessible to and used by the academy group. While the industry group reported greater access to and use of electronic textbooks and trade/promotional literature.

Wikipedia is characterized by its dense link structure and a large number of articles in different languages, which make it a notable Web corpus for knowledge extraction and mining, in particular for mining the multilingual associations. In this paper, motivated by a psychological theory of word meaning, we propose a graph-based approach to constructing a cross-language association dictionary ( CLAD) from Wikipedia, which can be used in a variety of cross-language accessing and processing applications. In order to evaluate the quality of the mined CLAD, and to demonstrate how the mined CLAD can be used in practice, we explore two different applications of the mined CLAD to cross-language information retrieval ( CLIR). First, we use the mined CLAD to conduct cross-language query expansion; and, second, we use it to filter out translation candidates with low translation probabilities. Experimental results on a variety of standard CLIR test collections show that the CLIR retrieval performance can be substantially improved with the above two applications of CLAD, which indicates that the mined CLAD is of sound quality. [ABSTRACT FROM AUTHOR]

Climate change has become a major area of concern over the past few years and consequently many governments, international bodies, businesses, and institutions are taking measures to reduce their carbon footprint. However, to date very little research has taken place on information and sustainable development in general, and on the environmental impact of information services in particular. Based on the data collected from various research papers and reports, this review article shows that information systems and services for the higher education and research sector currently generate massive greenhouse gas (GHG) emissions, and it is argued that there is an urgent need for developing a green information service, or green IS in short, that should be based on minimum GHG emissions throughout its lifecycle, from content creation to distribution, access, use, and disposal. Based on an analysis of the current research on green information technology (IT), it is proposed that a green IS should be based on the model of cloud computing. Finally, a research agenda is proposed that will pave the way for building and managing green ISs to support education and research/scholarly activities. [ABSTRACT FROM AUTHOR]

This paper presents and compares three feature reduction techniques that were applied to Arabic text. The techniques include stemming, light stemming, and word clusters. The effects of the aforementioned techniques were studied and analyzed on the K-nearest-neighbor classifier. Stemming reduces words to their stems. Light stemming, by comparison, removes common affixes from words without reducing them to their stems. Word clusters group synonymous words into clusters and each cluster is represented by a single word. The purpose of employing the previous methods is to reduce the size of document vectors without affecting the accuracy of the classifiers. The comparison metric includes size of document vectors, classification time, and accuracy (in terms of precision and recall). Several experiments were carried out using four different representations of the same corpus: the first version uses stem-vectors, the second uses light stem-vectors, the third uses word clusters, and the fourth uses the original words (without any transformation) as representatives of documents. The corpus consists of 15,000 documents that fall into three categories: sports, economics, and politics. In terms of vector sizes and classification time, the stemmed vectors consumed the smallest size and the least time necessary to classify a testing dataset that consists of 6,000 documents. The light stemmed vectors superseded the other three representations in terms of classification accuracy. [ABSTRACT FROM AUTHOR]

Although the analysis of citations in the scholarly literature is now an established and relatively well understood part of information science, not enough is known about citations that can be found on the Web. In particular, are there new Web types, and if so, are these trivial or potentially useful for studying or evaluating research communication? We sought evidence based upon a sample of 1,577 Web citations of the URLs or titles of research articles in 64 open-access journals from biology, physics, chemistry, and computing. Only 25% represented intellectual impact, from references of Web documents (23%) and other informal scholarly sources (2%). Many of the Web/URL citations were created for general or subject-specific navigation (45%) or for self-publicity (22%). Additional analyses revealed significant disciplinary differences in the types of Google unique Web/URL citations as well as some characteristics of scientific open-access publishing on the Web. We conclude that the Web provides access to a new and different type of citation information, one that may therefore enable us to measure different aspects of research, and the research process in particular; but to obtain good information, the different types should be separated. [ABSTRACT FROM AUTHOR]

Document keyphrases provide a concise summary of a document's content, offering semantic metadata summarizing a document. They can be used in many applications related to knowledge management and text mining, such as automatic text summarization, development of search engines, document clustering, document classification, thesaurus construction, and browsing interfaces. Because only a small portion of documents have keyphrases assigned by authors, and it is time-consuming and costly to manually assign keyphrases to documents, it is necessary to develop an algorithm to automatically generate keyphrases for documents. This paper describes a Keyphrase Identification Program (KIP), which extracts document keyphrases by using prior positive samples of human identified phrases to assign weights to the candidate keyphrases. The logic of our algorithm is: The more keywords a candidate keyphrase contains and the more significant these keywords are, the more likely this candidate phrase is a keyphrase. KIP's learning function can enrich the glossary database by automatically adding new identified keyphrases to the database. KIP's personalization feature will let the user build a glossary database specifically suitable for the area of his/her interest. The evaluation results show that KIP's performance is better than the systems we compared to and that the learning function is effective. [ABSTRACT FROM AUTHOR]

A new document representation model is presented in this paper. This model is based on the idea of representing a document by two or more pictures of the document taken from different perspectives. It is shown that by applying the stereo representation model, enhanced textual retrieval performance is achieved because the new model improves the capability of capturing individual features of the document. Experiments have been conducted on two standard corpora, TIME and ADI, using the standard term vector method and the latent semantic indexing (LSI) method based upon both the stereo representation model and the traditional representation model. Statistical t-tests on the experimental results have convincingly illustrated that these methods achieve significant improvements in retrieval performances with the stereo representation model over those with the traditional representation model. [ABSTRACT FROM AUTHOR]

As retrieval set size in information retrieval (IR) becomes larger, users may need greater interactive opportunities to determine for themselves potential relevance of the resources offered by a given collection. A parts-of-document approach, coupled with an interactive graphic interface and control panel, permits end users to tailor the information seeking (IS) session. Applying the model described by the author in a previous paper in this journal, this paper explores two issues: whether a group of information seekers in the same research domain will want to use this type of IR interaction, and whether such interaction is more successful than relevancy ranked lists, based on the general vector model. In addition, the paper proposes the use of gradient space as a means of capturing end users' cognitive states-decision-making points- during a parts-of-document-based lR session. It concludes that, for a group of biomedical researchers, a parts-of-document approach is preferred for certain IR situations and that gradient space provides designers of systems with empirical evidence suited for systems analysis.

After several decades of heavy research activity on English stemmers, Arabic morphological analysis techniques have become a popular area of research. The Arabic language is one of the Semitic languages; it exhibits a very systematic but complex morphological structure based on root-pattern schemes. As a consequence, survey of such techniques proves to be more necessary. The aim of this paper is to summarize and organize the information available in the literature in an attempt to motivate researchers to look into these techniques and try to develop more advanced ones. This paper introduces, classifies, and surveys Arabic morphological analysis techniques. Furthermore, conclusions, open areas, and future directions are provided at the end. [ABSTRACT FROM AUTHOR]

This paper proposes an effective term suggestion approach to interactive Web search. Conventional approaches to making term suggestions involve extracting co-occurring keyterms from highly ranked retrieved documents. Such approaches must deal with term extraction difficulties and interference from irrelevant documents, and, more importantly, have difficulty extracting terms that are conceptually related but do not frequently co-occur in documents. In this paper, we present a new, effective log-based approach to relevant term extraction and term suggestion. Using this approach, the relevant terms suggested for a user query are those that cooccur in similar query sessions from search engine logs, rather than in the retrieved documents. In addition, the suggested terms in each interactive search step can be organized according to its relevance to the entire .query session, rather than to the most recent single query as in conventional approaches. The proposed approach was tested using a proxy server log containing about two million query transactions submitted to search engines in Taiwan. The obtained experimental results show that the proposed approach can provide organized and highly relevant terms, and can exploit the contextual information in a user's query session to make more effective suggestions. [ABSTRACT FROM AUTHOR]

Automated information retrieval relies heavily on statistical regularities that emerge as terms are deposited to produce text. This paper examines statistical patterns expected of a pair of terms that are semantically related to each other. Guided by a conceptualization of the text generation process, we derive measures of how tightly two terms are semantically associated. Our main objective is to probe whether such measures yield reasonable results. Specifically, we examine how the tendency of a content bearing term to clump, as quantified by previously developed measures of term clumping, is influenced by the presence of other terms. This approach allows us to present a toolkit from which a range of measures can be constructed. As an illustration, one of several suggested measures is evaluated on a large text corpus built from an on-line encyclopedia. [ABSTRACT FROM AUTHOR]

This paper revises David Ellis's information-seeking behavior model of social scientists, which includes six generic features: starting, chaining, browsing, differentiating, monitoring, and extracting. The paper uses social science faculty researching stateless nations as the study population. The description and analysis of the information-seeking behavior of this group of scholars is based on data collected through structured and semistructured electronic mail interviews. Sixty faculty members from 14 different countries were interviewed by e-mail. For reality check purposes, face-to-face interviews with five faculty members were also conducted. Although the study confirmed Ellis's model, it found that a fuller description of the information-seeking process of social scientists studying stateless nations should include four additional features besides those identified by Ellis. These new features are: accessing, networking, verifying, and information managing. In view of that, the study develops a new model, which, unlike Ellis's, groups all the features into four interrelated stages: searching, accessing, processing, and ending. This new model is fully described and its implications on research and practice are discussed. How and why scholars studied here are different than other academic social scientists is also discussed. [ABSTRACT FROM AUTHOR]

As a part of a research project aiming to connect library data to the unfamiliar data sets available in the Linked Data ( LD) community's CKAN Data Hub (thedatahub.org), this project collected, analyzed, and mapped properties used in describing and accessing music recordings, scores, and music-related information used by selected music LD data sets, library catalogs, and various digital collections created by libraries and other cultural institutions. This article reviews current efforts to connect music data through the Semantic Web, with an emphasis on the Music Ontology ( MO) and ontology alignment approaches; it also presents a framework for understanding the life cycle of a musical work, focusing on the central activities of composition, performance, and use. The project studied metadata structures and properties of 11 music-related LD data sets and mapped them to the descriptions commonly used in the library cataloging records for sound recordings and musical scores (including MARC records and their extended schema.org markup), and records from 20 collections of digitized music recordings and scores (featuring a variety of metadata structures). The analysis resulted in a set of crosswalks and a unified crosswalk that aligns these properties. The paper reports on detailed methodologies used and discusses research findings and issues. Topics of particular concern include (a) the challenges of mapping between the overgeneralized descriptions found in library data and the specialized, music-oriented properties present in the LD data sets; (b) the hidden information and access points in library data; and (c) the potential benefits of enriching library data through the mapping of properties found in library catalogs to similar properties used by LD data sets. [ABSTRACT FROM AUTHOR]

In this study, we design an innovative method for answering students' or scholars' academic questions (for a specific scientific publication) by automatically recommending e-learning resources in a cyber-infrastructure-enabled learning environment to enhance the learning experiences of students and scholars. By using information retrieval and metasearch methodologies, different types of referential metadata (related Wikipedia pages, data sets, source code, video lectures, presentation slides, and online tutorials) for an assortment of publications and scientific topics will be automatically retrieved, associated, and ranked (via the language model and the inference network model) to provide easily understandable cyberlearning resources to answer students' questions. We also designed an experimental system to automatically answer students' questions for a specific academic publication and then evaluated the quality of the answers (the recommended resources) using mean reciprocal rank and normalized discounted cumulative gain. After examining preliminary evaluation results and student feedback, we found that cyberlearning resources can provide high-quality and straightforward answers for students' and scholars' questions concerning the content of academic publications. [ABSTRACT FROM AUTHOR]

Looking for ontology in a search engine, one can find so many different approaches that it can be difficult to understand which field of research the subject belongs to and how it can be useful. The term ontology is employed within philosophy, computer science, and information science with different meanings. To take advantage of what ontology theories have to offer, one should understand what they address and where they come from. In information science, except for a few papers, there is no initiative toward clarifying what ontology really is and the connections that it fosters among different research fields. This article provides such a clarification. We begin by revisiting the meaning of the term in its original field, philosophy, to reach its current use in other research fields. We advocate that ontology is a genuine and relevant subject of research in information science. Finally, we conclude by offering our view of the opportunities for interdisciplinary research. [ABSTRACT FROM AUTHOR]

The Open Access ( OA) movement, which postulates gratis and unrestricted online access to publicly funded research findings, has significantly gained momentum in recent years. The two ways of achieving OA are self-archiving of scientific work by the authors ( Green OA) and publishing in OA journals ( Gold OA). But there is still no consensus which model should be supported in particular. The aim of this simulation study is to discover mechanisms and predict developments that may lead to specific outcomes of possible market transformation scenarios. It contributes to theories related to OA by substantiating the argument of a citation advantage of OA articles and by visualizing the mechanisms of a journal system collapsing in the long-term due to the continuation of the serials crisis. The practical contribution of this research stems from the integration of all market players: Decisions regarding potential financial support of OA models can be aligned with our findings, as well as the decision of a publisher to migrate his/her journals to Gold OA. Our results indicate that for scholarly communication in general, a transition to Green OA combined with a certain level of subscription-based publishing and a migration of few top journals is the most beneficial development. [ABSTRACT FROM AUTHOR]

The interactions of users with search engines can be seen as implicit relevance feedback by the user on the results offered to them. In particular, the selection of results by users can be interpreted as a confirmation of the relevance of those results, and used to reorder or prioritize subsequent search results. This collection of search/result pairings is called clickthrough data, and many uses for it have been proposed. However, the reliability of clickthrough data has been challenged and it has been suggested that clickthrough data are not a completely accurate measure of relevance between search term and results. This paper reports on an experiment evaluating the reliability of clickthrough data as a measure of the mutual relevance of search term and result. The experiment comprised a user study involving over 67 participants and determines the reliability of image search clickthrough data, using factors identified in previous similar studies. A major difference in this work to previous work is that the source of clickthrough data comes from image searches, rather than the traditional text page searches. Image search clickthrough data were rarely examined in prior works but has differences that impact the accuracy of clickthrough data. These differences include a more complete representation of the results in image search, allowing users to scrutinize the results more closely before selecting them, as well as presenting the results in a less obviously ordered way. The experiment reported here demonstrates that image clickthrough data can be more reliable as a relevance feedback measure than has been the case with traditional text-based search. There is also evidence that the precision of the search system influences the accuracy of click data when users make searches in an information-seeking capacity. [ABSTRACT FROM AUTHOR]

This paper presents the results of a large-scale, qualitative study conducted in the homes of children aged 7, 9, and 11 investigating internet searching processes on Google. Seven search roles, representing distinct behavior patterns displayed by children when interacting with the Google search engine, are described, including Developing Searchers, Domain-specific Searchers, Power Searchers, Nonmotivated Searchers, Distracted Searchers, Rule-bound Searchers, and Visual Searchers. Other trends are described and selected to present a view of the whole child searcher. These roles and trends are used to make recommendations to designers, researchers, educators, and parents about the directions to take when considering how to best aid children to become search literate. [ABSTRACT FROM AUTHOR]

In this paper a novel method for detecting plagiarized passages in document collections is presented. In contrast to previous work in this field that uses content terms to represent documents, the proposed method is based on a small list of stopwords (i.e., very frequent words). We show that stopword n-grams reveal important information for plagiarism detection since they are able to capture syntactic similarities between suspicious and original documents and they can be used to detect the exact plagiarized passage boundaries. Experimental results on a publicly available corpus demonstrate that the performance of the proposed approach is competitive when compared with the best reported results. More importantly, it achieves significantly better results when dealing with difficult plagiarism cases where the plagiarized passages are highly modified and most of the words or phrases have been replaced with synonyms. [ABSTRACT FROM AUTHOR]

We have developed a system that segments web pages into blocks and predicts those blocks' importance (block importance prediction or BIP). First, we use VIPS to partition a page into a tree composed of blocks and then extracts features from each block and labels all leaf nodes. This paper makes two main contributions. Firstly, we are pioneering the formulation of BIP as a sequence tagging task. We employ DFS, which outputs a single sequence for the whole tree in which related sub-blocks are adjacent. Our second contribution is using the conditional random fields (CRF) model for labeling these sequences. CRF's transition features model correlations between neighboring labels well, and CRF can simultaneously label all blocks in a sequence to find the global optimal solution for the whole sequence, not only the best solution for each block. In our experiments, our CRF-based system achieves an F1-measure of 97.41%, which significantly outperforms our ME-based baseline (95.64%). Lastly, we tested the CRF-based system using sites which were not covered in the training data. On completely novel sites CRF performed slightly worse than ME. However, when given only two training pages from a given site, CRF improved almost three times as much as ME. [ABSTRACT FROM AUTHOR]

Applications that use games to harness human intelligence to perform various computational tasks are increasing in popularity and may be termed human computation games (HCGs). Most HCGs are collaborative in nature, requiring players to cooperate within a game to score points. Competitive versions, where players work against each other, are a more recent entrant, and have been claimed to address shortcomings of collaborative HCGs such as quality of computation. To date, however, little work has been conducted in understanding how different HCG genres influence computational performance and players' perceptions of such. In this paper we study these issues using image tagging HCGs in which users play games to generate keywords for images. Three versions were created: collaborative HCG, competitive HCG, and a control application for manual tagging. The applications were evaluated to uncover the quality of the image tags generated as well as users' perceptions. Results suggest that there is a tension between entertainment and tag quality. While participants reported liking the collaborative and competitive image tagging HCGs over the control application, those using the latter seemed to generate better quality tags. Implications of the work are discussed. [ABSTRACT FROM AUTHOR]

In both the popular press and scholarly research, digital information is persistently discussed in terms that imply its immateriality. In this characterization, the digital derives its power from its nature as a mere collection of 0s and 1s wholly independent from the particular media on which it is stored-hard drive, network wires, optical disk, etc.-and the particular signal carrier which encodes bits-variations of magnetic field, voltages, or pulses of light. This purported immateriality endows bits with considerable advantages: they are immune from the economics and logistics of analog media, and from the corruption, degradation, and decay that necessarily result from the handling of material carriers of information, resulting in a worldwide shift 'from atom to bits' as captured by Negroponte. This is problematic: however immaterial it might appear, information cannot exist outside of given instantiations in material forms. But what might it mean to talk of bits as material objects? In this paper I argue that bits cannot escape the material constraints of the physical devices that manipulate, store, and exchange them. Such an analysis reveals a surprising picture of computing as a material process through and through. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology which discriminates the articles by the target authors ('true' articles) from those by other homonymous authors ('false' articles). Author name searches for 2,595 'source' authors in six subject fields retrieved about 629,000 articles. In order to extract true articles from the large amount of the retrieved articles, including many false ones, two filtering stages were applied. At the first stage any retrieved article was eliminated as false if either its affiliation addresses had little similarity to those of its source article or there was no citation relationship between the journal of the retrieved article and that of its source article. At the second stage, a sample of retrieved articles was subjected to manual judgment, and utilizing the judgment results, discrimination functions based on logistic regression were defined. These discrimination functions demonstrated both the recall ratio and the precision of about 95% and the accuracy (correct answer ratio) of 90-95%. Existence of common coauthor(s), address similarity, title words similarity, and interjournal citation relationships between the retrieved and source articles were found to be the effective discrimination predictors. Whether or not the source author was from a specific country was also one of the important predictors. Furthermore, it was shown that a retrieved article is almost certainly true if it was cited by, or cocited with, its source article. The method proposed in this study would be effective when dealing with a large number of articles whose subject fields and affiliation addresses vary widely. [ABSTRACT FROM AUTHOR]

This paper aims to review the fiercely discussed question of whether the ranking of Wikipedia articles in search engines is justified by the quality of the articles. After an overview of current research on information quality in Wikipedia, a summary of the extended discussion on the quality of encyclopedic entries in general is given. On this basis, a heuristic method for evaluating Wikipedia entries is developed and applied to Wikipedia articles that scored highly in a search engine retrieval effectiveness test and compared with the relevance judgment of jurors. In all search engines tested, Wikipedia results are unanimously judged better by the jurors than other results on the corresponding results position. Relevance judgments often roughly correspond with the results from the heuristic evaluation. Cases in which high relevance judgments are not in accordance with the comparatively low score from the heuristic evaluation are interpreted as an indicator of a high degree of trust in Wikipedia. One of the systemic shortcomings of Wikipedia lies in its necessarily incoherent user model. A further tuning of the suggested criteria catalog, for instance, the different weighing of the supplied criteria, could serve as a starting point for a user model differentiated evaluation of Wikipedia articles. Approved methods of quality evaluation of reference works are applied to Wikipedia articles and integrated with the question of search engine evaluation. [ABSTRACT FROM AUTHOR]

We suggest partial logarithmic binning as the method of choice for uncovering the nature of many distributions encountered in information science (IS). Logarithmic binning retrieves information and trends 'not visible' in noisy power law tails. We also argue that obtaining the exponent from logarithmically binned data using a simple least square method is in some cases warranted in addition to methods such as the maximum likelihood. We also show why often-used cumulative distributions can make it difficult to distinguish noise from genuine features and to obtain an accurate power law exponent of the underlying distribution. The treatment is nontechnical, aimed at IS researchers with little or no background in mathematics. [ABSTRACT FROM AUTHOR]

Thomson Reuters' Web of Science (WoS) is undoubtedly a great tool for scientiometrics purposes. It allows one to retrieve and compute different measures such as the total number of papers that satisfy a particular condition; however, it also is well known that this tool imposes several different restrictions that make obtaining certain results difficult. One of those constraints is that the tool does not offer the total count of documents in a dataset if it is larger than 100,000 items. In this article, we propose and analyze different approaches that involve partitioning the search space (using the Source field) to retrieve item counts for very large datasets from the WoS. The proposed techniques improve previous approaches: They do not need any extra information about the retrieved dataset (thus allowing completely automatic procedures to retrieve the results), they are designed to avoid many of the restrictions imposed by the WoS, and they can be easily applied to almost any query. Finally, a description of WoS Query Partitioner, a freely available and online interactive tool that implements those techniques, is presented. [ABSTRACT FROM AUTHOR]

Automatic document categorization is an important research problem in Information Science and Natural Language Processing. Many applications, including, Word Sense Disambiguation and Information Retrieval in large collections, can benefit from such categorization. This paper focuses on automatic categorization of documents from the biomedical literature into broad discipline-based categories. Two different systems are described and contrasted: CISMeF, which uses rules based on human indexing of the documents by the Medical Subject Headings (MeSH) controlled vocabulary in order to assign metaterms (MTs), and Journal Descriptor Indexing (JDI), based on human categorization of about 4,000 journals and statistical associations between journal descriptors (JDs) and textwords in the documents. We evaluate and compare the performance of these systems against a gold standard of humanly assigned categories for 100 MEDLINE documents, using six measures selected from trec_eval. The results show that for five of the measures performance is comparable, and for one measure JDI is superior. We conclude that these results favor JDI, given the significantly greater intellectual overhead involved in human indexing and maintaining a rule base for mapping MeSH terms to MTs. We also note a JDI method that associates JDs with MeSH indexing rather than textwords, and it may be worthwhile to investigate whether this JDI method (statistical) and CISMeF (rule-based) might be combined and then evaluated showing they are complementary to one another. [ABSTRACT FROM AUTHOR]