Search

Death is an inevitable part of life and highly relevant to information management: its approach often requires preparation, and its occurrence often demands a response. Many works in information science have acknowledged so much, and yet death is rarely a focused topic, appearing instead sporadically and disconnected across research. As a result there is no introduction to, overview of, or synthesis across studies on death and information. We therefore conducted an extensive literature search and reviewed nearly 300 scholarly publications at the intersection of death and information (and data) management. Covering seven topics in total, we review two groups of work directly engaging information management in relation to death (digital possessions, inheritance, and legacy; information behavior, needs, and practices around death), three engaging death and technology that require information and its management (death and the Internet, thanatosensitive design and technology‐augmented death practices, and the digital afterlife and digital immortality), and two reflecting the ethical and legal dimensions unique to death and information. We then integrate the collective findings to summarize the landscape of death‐related information research, outline remaining challenges for individuals, families, institutions, and society, and identify promising directions for future information science research. [ABSTRACT FROM AUTHOR]

Users of research databases, such as CiteSeerX, Google Scholar, and Microsoft Academic, often search for papers using a set of keywords. Unfortunately, many authors avoid listing sufficient keywords for their papers. As such, these applications may need to automatically associate good descriptive keywords with papers. When the full text of the paper is available this problem has been thoroughly studied. In many cases, however, due to copyright limitations, research databases do not have access to the full text. On the other hand, such databases typically maintain metadata, such as the title and abstract and the citation network of each paper. In this paper we study the problem of predicting which keywords are appropriate for a research paper, using different methods based on the citation network and available metadata. Our main goal is in providing search engines with the ability to extract keywords from the available metadata. However, our system can also be used for other applications, such as for recommending keywords for the authors of new papers. We create a data set of research papers, and their citation network, keywords, and other metadata, containing over 470 K papers with and more than 2 million keywords. We compare our methods with predicting keywords using the title and abstract, in offline experiments and in a user study, concluding that the citation network provides much better predictions. [ABSTRACT FROM AUTHOR]

The amount of scientific papers is growing together with the development of science itself; but, although there is an unprecedented availability of large citation indexes, some daily activities of researchers remain time‐consuming and poorly supported. In this paper, we present Visual Bibliographies (VisualBib), a real‐time visual platform, designed using a zz‐structure‐based model for linking metadata and a narrative, visual approach for showing bibliographies. VisualBib represents a usable, advanced, and visual tool, which simplifies the management of bibliographies, supports a core set of bibliographic tasks, and helps researchers during complex analyses on scientific bibliographies. We present the variety of metadata formats and visualization methods, proposing two use case scenarios. The maturity of the system implementation allowed us two studies, for evaluating both the effectiveness of VisualBib in providing answers to specific data analysis tasks and to support experienced users during real‐life uses. The results of the evaluation are positive and describe an effective and usable platform. [ABSTRACT FROM AUTHOR]

With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end‐to‐end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro‐avgs of P@5, R@5, and F1@5 on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models. [ABSTRACT FROM AUTHOR]

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

In this article, we propose a graph-based interactive bibliographic information retrieval system-GIBIR. GIBIR provides an effective way to retrieve bibliographic information. The system represents bibliographic information as networks and provides a form-based query interface. Users can develop their queries interactively by referencing the system-generated graph queries. Complex queries such as 'papers on information retrieval, which were cited by John's papers that had been presented in SIGIR' can be effectively answered by the system. We evaluate the proposed system by developing another relational database-based bibliographic information retrieval system with the same interface and functions. Experiment results show that the proposed system executes the same queries much faster than the relational database-based system, and on average, our system reduced the execution time by 72% (for 3-node query), 89% (for 4-node query), and 99% (for 5-node query). [ABSTRACT FROM AUTHOR]

The article introduces various topics covered in the issue including benefits of using sequence representations in medical field, the effectiveness of the retrieval-given queries, and major improvements in template extraction post temporality classifications.

How to collect relevance judgment has long been an important problem in Information Retrieval (IR). A popular method is to collect relevance judgment in a point‐wise manner, in which assessors examine and give an absolute relevance score for each item independently of the others. As an alternative, pair‐wise relevance judgment, also named preference judgment, allows an assessor to compare two items side‐by‐side and express their preference for one over the other. Previous work has explored the differences between these two paradigms of relevance judgments from many different aspects. Most of these works are conducted through explicit/implicit feedback. However, few works investigate the underlying neurological mechanisms of the two paradigms. In this paper, we conduct a lab study to investigate and compare point‐wise and pair‐wise relevance judgment in image search scenarios. We study the neurological mechanisms of the two paradigms through an event‐related potential (ERP) analysis of the users' brain signals while viewing images during a search process. We have obtained several observations, such as search engine users tend to pay more attention to preferred items in the point‐wise paradigm but unpreferred items in the pair‐wise paradigm. Furthermore, we test the adoption of brain signals as implicit feedback for predicting pair‐wise relevance judgment, highlighting the feasibility of leveraging brain signals to understand users' relevance judgments. [ABSTRACT FROM AUTHOR]

The importance of involving humans in the data science process has been widely discussed in the literature. However, studies lack details on how to involve humans in the process. Using a design science approach, this paper proposes and evaluates a human‐supervised data science framework in the context of local governments. Our findings suggest that the involvement of a stakeholder group, public managers in this case, in the process of data science project enhanced quality of data science outcomes. Public managers' detailed knowledge on both the data and context was beneficial for improving future data science infrastructure. In addition, the study suggests that local governments can harness the value of data‐driven approaches to policy and decision making through focalized investments in improving data and data science infrastructure, which includes culture and processes necessary to incorporate data science and analytics into the decision‐making process. [ABSTRACT FROM AUTHOR]

The paper reports a longitudinal analysis of the topical and methodological development of Library and Information Science (LIS). Its focus is on the effects of researchers' disciplines on these developments. The study extends an earlier cross‐sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706–1722) by a coordinated dataset representing a content analysis of articles published in 31 scholarly LIS journals in 1995, 2005, and 2015. It is novel in its coverage of authors' disciplines, topical and methodological aspects in a coordinated dataset spanning two decades thus allowing trend analysis. The findings include a shrinking trend in the share of LIS from 67 to 36% while Computer Science, and Business and Economics increase their share from 9 and 6% to 21 and 16%, respectively. The earlier cross‐sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706–1722) for the year 2015 identified three topical clusters of LIS research, focusing on topical subfields, methodologies, and contributing disciplines. Correspondence analysis confirms their existence already in 1995 and traces their development through the decades. The contributing disciplines infuse their concepts, research questions, and approaches to LIS and may also subsume vital parts of LIS in their own structures of knowledge production. [ABSTRACT FROM AUTHOR]

As part of the effort to digitally transform, organizations are seeking more and better solutions to long‐standing enterprise content management challenges. Such solutions rarely investigate the relationship between knowledge workers' daily work to capture information and the perceived or actual value of that information to the enterprise per established content management strategy. The study described in this paper seeks to identify gaps in content management practices versus policy by modeling the conventions by which one organization's knowledge workers typically generate, store, and later recover their daily work products. Thirty‐five interviews with knowledge workers at the Jet Propulsion Laboratory were conducted on this subject. The results of these interviews provide an insight as to how knowledge workers interact with enterprise content in their dual roles as both the primary creators and primary consumers of enterprise content. This paper, which outlines various permutations of the digital object creation, description, and storage (CDS) model, provides basic strategies for bringing the value perceptions of knowledge workers into alignment with institutional directives related to improving content findability and reuse in the enterprise. [ABSTRACT FROM AUTHOR]

This article considers the interdisciplinary opportunities and challenges of working with digital cultural heritage, such as digitized historical newspapers, and proposes an integrated digital hermeneutics workflow to combine purely disciplinary research approaches from computer science, humanities, and library work. Common interests and motivations of the above‐mentioned disciplines have resulted in interdisciplinary projects and collaborations such as the NewsEye project, which is working on novel solutions on how digital heritage data is (re)searched, accessed, used, and analyzed. We argue that collaborations of different disciplines can benefit from a good understanding of the workflows and traditions of each of the disciplines involved but must find integrated approaches to successfully exploit the full potential of digitized sources. The paper is furthermore providing an insight into digital tools, methods, and hermeneutics in action, showing that integrated interdisciplinary research needs to build something in between the disciplines while respecting and understanding each other's expertise and expectations. [ABSTRACT FROM AUTHOR]

Term position feature is widely and successfully used in IR and Web search engines, to enhance the retrieval effectiveness. This feature is essentially used for two purposes: to capture query terms proximity or to boost the weight of terms appearing in some parts of a document. In this paper, we are interested in this second category. We propose two novel query‐independent techniques based on absolute term positions in a document, whose goal is to boost the weight of terms appearing in the beginning of a document. The first one considers only the earliest occurrence of a term in a document. The second one takes into account all term positions in a document. We formalize each of these two techniques as a document model based on term position, and then we incorporate it into a basic language model (LM). Two smoothing techniques, Dirichlet and Jelinek‐Mercer, are considered in the basic LM. Experiments conducted on three TREC test collections show that our model, especially the version based on all term positions, achieves significant improvements over the baseline LMs, and it also often performs better than two state‐of‐the‐art baseline models, the chronological term rank model and the Markov random field model. [ABSTRACT FROM AUTHOR]

This study examined how researchers used research information systems (RIMSs) and the relationships among researchers' seniority, discipline, and types and extent of RIMS use. Most researchers used RIMSs to discover research content. Fewer used RIMSs for sharing and promoting their research. Early career researchers were more frequent users of RIMSs than were associate and full professors. Likewise, assistant professors and postdocs exhibited a higher probability of using RIMSs to promote their research than did students and full professors. Humanities researchers were the least frequent users of RIMSs. Moreover, humanities scholars used RIMSs to evaluate research less than did scholars in other disciplines. The tasks of discovering papers, monitoring the literature, identifying potential collaborators, and promoting research were predictors of higher RIMS use. Researchers who engaged in promoting their research, evaluating research, or monitoring the literature showed a greater propensity to have a public RIMS profile. Furthermore, researchers mostly agreed that not being required, having no effect on their status, not being useful, or not being a norm were reasons for not having a public RIMS profile. Humanities scholars were also more likely than social scientists to agree that having a RIMS profile was not a norm in their fields. [ABSTRACT FROM AUTHOR]

This paper addresses the importance of improving the effectiveness of voice search systems through partial query modification. A user‐centered experiment was designed to compare the effectiveness of an experimental system using partial query modification feature to a baseline system in which users could issue complete queries only, with 32 participants each searching on eight different tasks. The results indicate that the participants spent significantly more time preparing the modification but significantly less time speaking the modification by using the experimental system than by using the baseline system. The participants found that the experimental system (a) was more effective, (b) gave them more control, (c) was easier for the search tasks, and (d) saved them time than the baseline system. The results contribute to improving future voice search system design and benefiting the research community in general. System implications and future work were discussed. [ABSTRACT FROM AUTHOR]

Deep learning technologies have brought us many models that outperform human beings on a few benchmarks. An interesting question is: can these models well solve real‐world problems with similar settings (e.g., identical input/output) to the benchmark datasets? We argue that a model is trained to answer the same information need in a similar context (e.g., the information available), for which the training dataset is created. The trained model may be used to solve real‐world problems for a similar information need in a similar context. However, information need is independent of the format of dataset input/output. Although some datasets may share high structural similarities, they may represent different research tasks aiming for answering different information needs. Examples are question–answer pairs for the question answering (QA) task, and image‐caption pairs for the image captioning (IC) task. In this paper, we use the QA task and IC task as two case studies and compare their widely used benchmark datasets. From the perspective of information need in the context of information retrieval, we show the differences in the dataset creation processes and the differences in morphosyntactic properties between datasets. The differences in these datasets can be attributed to the different information needs and contexts of the specific research tasks. We encourage all researchers to consider the information need perspective of a research task when selecting the appropriate datasets to train a model. Likewise, while creating a dataset, researchers may also incorporate the information need perspective as a factor to determine the degree to which the dataset accurately reflects the real‐world problem or the research task they intend to tackle. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet and its applications, growing volumes of documents increasingly become interconnected to form large-scale document networks. Accordingly, topic modeling in a network of documents has been attracting continuous research attention. Most of the existing network-based topic models assume that topics in a document are influenced by its directly linked neighbouring documents in a document network and overlook the potential influence from indirectly linked ones. The existing work also has not carefully modeled variations of such influence among neighboring documents. Recognizing these modeling limitations, this paper introduces a novel Local Context-Aware LDA Model (LC-LDA), which is capable of observing a local context comprising a rich collection of documents that may directly or indirectly influence the topic distributions of a target document. The proposed model can also differentiate the respective influence of each document in the local context on the target document according to both structural and temporal relationships between the two documents. The proposed model is extensively evaluated through multiple document clustering and classification tasks conducted over several large-scale document sets. Evaluation results clearly and consistently demonstrate the effectiveness and superiority of the new model with respect to several state-of-the-art peer models. [ABSTRACT FROM AUTHOR]

Data reuse has recently become significant in academia and is providing new impetus for academic research. This prompts two questions: What precisely is the data reuse process? What is the connection between each participating element? To address these issues, 42 studies were reviewed to identify the stages and primary data reuse elements. A meta‐synthesis was used to locate and analyze the studies, and inductive coding was used to organize the analytical process. We identified three stages of data reuse—initiation, exploration and collection, and repurposing—and explored how they interact and form iterative characteristics. The results illuminated the data reuse at each stage, including issues of data trust, data sources, scaffolds, and barriers. The results indicated that multisource data and human scaffolds promote reuse behavior effectively. Further, two data and information search patterns were extracted: reticular centripetal patterns and decentralized centripetal patterns. Three paths with elements cooperating through flexible functions and motivated by different action items were identified: data centers, human scaffolds, and publications. This study supports improvements for data infrastructure construction, data reuse, and data reuse research by providing a new perspective on the effect of information behavior and clarifying the stages and contextual relationships between various elements. [ABSTRACT FROM AUTHOR]

This is the first study that compares types of orientation tactics that blind and sighted users applied in their initial interactions with a digital library (DL) and the associated factors. Multiple methods were employed for data collection: questionnaires, think-aloud protocols, and transaction logs. The paper identifies seven types of orientation tactics applied by the two groups of users. While sighted users focused on skimming DL content, blind users concentrated on exploring DL structure. Moreover, the authors discovered 13 types of system, user, and interaction factors that led to the use of orientation tactics. More system factors than user factors affect blind users' tactics in browsing DL structures. The findings of this study support the social model that the sightcentered design of DLs, rather than blind users' disability, prohibits them from effectively interacting with a DL. Simultaneously, the results reveal the limitation of existing interactive information retrieval models that do not take people with disabilities into consideration. DL design implications are discussed based on the identified factors. [ABSTRACT FROM AUTHOR]

In this paper, we perform an in-depth study into how clinicians represent their information needs and the influence this has on information retrieval (IR) effectiveness. While much research in IR has considered the effectiveness of IR systems, there is still a significant gap in the understanding of how users contribute to the effectiveness of these systems. The paper aims to contribute to this by studying how clinicians search for information. Multiple representations of an information need-from verbose patient case descriptions to ad-hoc queries-were considered in order to understand their effect on retrieval. Four clinicians provided queries and performed relevance assessment to form a test collection used in this study. The different query formulation strategies of each clinician, and their effectiveness, were investigated. The results show that query formulation had more impact on retrieval effectiveness than the particular retrieval systems used. The most effective queries were short, ad-hoc keyword queries. Different clinicians were observed to consistently adopt specific query formulation strategies. The most effective queriers were those who, given their information need, inferred novel keywords most likely to appear in relevant documents. This study reveals aspects of how people search within the clinical domain. This can help inform the development of new models and methods that specifically focus on the query formulation process to improve retrieval effectiveness. [ABSTRACT FROM AUTHOR]

This study presents a large-scale analysis of the distribution and presence of Mendeley readership scores over time and across disciplines. We study whether Mendeley readership scores (RS) can identify highly cited publications more effectively than journal citation scores (JCS). Web of Science (WoS) publications with digital object identifiers (DOIs) published during the period 2004-2013 and across five major scientific fields were analyzed. The main result of this study shows that RS are more effective (in terms of precision/recall values) than JCS to identify highly cited publications across all fields of science and publication years. The findings also show that 86.5% of all the publications are covered by Mendeley and have at least one reader. Also, the share of publications with Mendeley RS is increasing from 84% in 2004 to 89% in 2009, and decreasing from 88% in 2010 to 82% in 2013. However, it is noted that publications from 2010 onwards exhibit on average a higher density of readership versus citation scores. This indicates that compared to citation scores, RS are more prevalent for recent publications and hence they could work as an early indicator of research impact. These findings highlight the potential and value of Mendeley as a tool for scientometric purposes and particularly as a relevant tool to identify highly cited publications. [ABSTRACT FROM AUTHOR]

With the development of Web‐based social networks, many personalized tag recommendation approaches based on multi‐information have been proposed. Due to the differences in users' preferences, different users care about different kinds of information. In the meantime, different elements within each kind of information are differentially informative for user tagging behaviors. In this context, how to effectively integrate different elements and different information separately becomes a key part of tag recommendation. However, the existing methods ignore this key part. In order to address this problem, we propose a deep neural network for tag recommendation. Specifically, we model two important attentive aspects with a hierarchical attention model. For different user‐item pairs, the bottom layered attention network models the influence of different elements on the features representation of the information while the top layered attention network models the attentive scores of different information. To verify the effectiveness of the proposed method, we conduct extensive experiments on two real‐world data sets. The results show that using attention network and different kinds of information can significantly improve the performance of the recommendation model, and verify the effectiveness and superiority of our proposed model. [ABSTRACT FROM AUTHOR]

While the search interfaces used by common academic digital libraries provide easy access to a wealth of peer‐reviewed literature, their interfaces provide little support for exploratory browsing. When faced with a complex search task (such as one that requires knowledge discovery), exploratory browsing is an important first step in an exploratory search process. To more effectively support exploratory browsing, we have designed and implemented a novel academic digital library search interface (KLink Search) with two new features: visually linked keywords and an interactive workspace. To study the potential value of these features, we have conducted a controlled laboratory study with 32 participants, comparing KLink Search to a baseline digital library search interface modeled after that used by IEEE Xplore. Based on subjective opinions, objective performance, and behavioral data, we show the value of adding lightweight visual and interactive features to academic digital library search interfaces to support exploratory browsing. [ABSTRACT FROM AUTHOR]

This study applies Kuhlthau's Information Search Process stage (ISP) model to understand bilingual users' Internet search experience. We conduct a quasi‐field experiment with 30 bilingual searchers and the results suggested that the ISP model was applicable in studying searchers' information retrieval behavior in search tasks. The ISP model was applicable in studying searchers' information retrieval behavior in simple tasks. However, searchers' emotional responses differed from those of the ISP model for a complex task. By testing searchers using different search strategies, the results suggested that search engines with multilanguage search functions provide an advantage for bilingual searchers in the Internet's multilingual environment. The findings showed that when searchers used a search engine as a tool for problem solving, they might experience different feelings in each ISP stage than in searching for information for a term paper using a library. The results echo other research findings that indicate that information seeking is a multifaceted phenomenon. [ABSTRACT FROM AUTHOR]

Conventional approaches to relevance ranking typically optimize ranking models by each query separately. The traditional cluster hypothesis also does not consider the dependency between related queries. The goal of this paper is to leverage similar search intents to perform ranking consistency so that the search performance can be improved accordingly. Different from the previous supervised approach, which learns relevance by click‐through data, we propose a novel cocluster hypothesis to bridge the gap between relevance ranking and ranking consistency. A nearest‐neighbors test is also designed to measure the extent to which the cocluster hypothesis holds. Based on the hypothesis, we further propose a two‐stage unsupervised approach, in which two ranking heuristics and a cost function are developed to optimize the combination of consistency and uniqueness (or inconsistency). Extensive experiments have been conducted on a real and large‐scale search engine log. The experimental results not only verify the applicability of the proposed cocluster hypothesis but also show that our approach is effective in boosting the retrieval performance of the commercial search engine and reaches a comparable performance to the supervised approach. [ABSTRACT FROM AUTHOR]

Community Question‐Answering (CQA) sites have become popular venues where people can ask questions, seek information, or share knowledge with a user community. Although responses on CQA sites are obviously slower than information retrieved by a search engine, one of the most frustrating aspects of CQAs occurs when an asker's posted question does not receive a reasonable answer or remains unanswered. CQA sites could improve users' experience by identifying potential answerers and routing appropriate questions to them. In this paper, we predict the potential answerers based on question content and user profiles. Our approach builds user profiles based on past activity. When a new question is posted, the proposed method computes scores between the question and all user profiles to find the potential answerers. We conduct extensive experimental evaluations on two popular CQA sites ‐ Yahoo! Answers and Stack Overflow ‐ to show the effectiveness of our algorithm. The results show that our technique is able to predict a small group of 1000 users from which at least one user will answer the question with a probability higher than 50% in both CQA sites. Further analysis indicates that topic interest and activity level can improve the correctness of our approach. [ABSTRACT FROM AUTHOR]

Recommender systems are changing the way that people find information, products, and even other people. This paper studies the problem of leveraging the context of the items presented to the user in a user/system interaction session to improve the recommender system's ranking prediction. We propose a novel model that incorporates the opportunity cost of giving up the other items in the session and computes session‐specific relevance values for items for context‐aware recommendation. The model can work on a variety of different problems settings with emphasis on implicit user feedback as it supports varying levels of ordinal relevance. Experimental evaluation demonstrates the advantages of our new model with respect to the ranking quality. [ABSTRACT FROM AUTHOR]

This brief communication builds on the application of content-based image retrieval (CBIR) and reverse image lookup (RIL), a graduated form of CBIR, as assessment tools for digital library image reuse. It combines literature on the definition, history, usefulness, and limitations of RIL and includes a brief analysis of the 4 published digital library image reuse assessment case studies. In its conclusion, the communication paper proposes that RIL offers benefits for digital library managers in the assessment of their collections. [ABSTRACT FROM AUTHOR]

This paper explores the usefulness of OpenAIRE, BASE, Recolecta, and Google Scholar (GS) for evaluating open access (OA) policies that demand a deposit in a repository. A case study was designed focusing on 762 financed articles with a project of FIS‐2012 of the Instituto de Salud Carlos III, the Spanish national health service's main management body for health research. Its finance is therefore subject to the Spanish Government OA mandate. A search was carried out for full‐text OA copies of the 762 articles using the four tools being evaluated and with identification of the repository housing these items. Of the 762 articles concerned, 510 OA copies were found of 353 unique articles (46.3%) in 68 repositories. OA copies were found of 81.9% of the articles in PubMed Central and copies of 49.5% of the articles in an institutional repository (IR). BASE and GS identified 93.5% of the articles and OpenAIRE 86.7%. Recolecta identified just 62.2% of the articles deposited in a Spanish IR. BASE achieved the greatest success, by locating copies deposited in IR, while GS found those deposited in disciplinary repositories. None of the tools identified copies of all the articles, so they need to be used in a complementary way when evaluating OA policies. [ABSTRACT FROM AUTHOR]

Information retrieval (IR) systems are the prominent means for searching and accessing huge amounts of unstructured information on the web and elsewhere. They are complex systems, constituted by many different components interacting together, and evaluation is crucial to both tune and improve them. Nevertheless, in the current evaluation methodology, there is still no way to determine how much each component contributes to the overall performances and how the components interact together. This hampers the possibility of a deep understanding of IR system behavior and, in turn, prevents us from designing ahead which components are best suited to work together for a specific search task. In this paper, we move the evaluation methodology one step forward by overcoming these barriers and beginning to devise an “anatomy” of IR systems and their internals. In particular, we propose a methodology based on the General Linear Mixed Model (GLMM) and analysis of variance (ANOVA) to develop statistical models able to isolate system variance and component effects as well as their interaction, by relying on a grid of points (GoP) containing all the combinations of the analyzed components. We apply the proposed methodology to the analysis of two relevant search tasks—news search and web search—by using standard TREC collections. We analyze the basic set of components typically part of an IR system, namely, stop lists, stemmers, and n‐grams, and IR models. In this way, we derive insights about English text retrieval. [ABSTRACT FROM AUTHOR]

This article demonstrates the benefits of using sequence representations based on word embeddings to inform the seed selection and sample selection processes in an active learning pipeline for clinical information extraction. Seed selection refers to choosing an initial sample set to label to form an initial learning model. Sample selection refers to selecting informative samples to update the model at each iteration of the active learning process. Compared to supervised machine learning approaches, active learning offers the opportunity to build statistical classifiers with a reduced amount of training samples that require manual annotation. Reducing the manual annotation effort can support automating the clinical information extraction process. This is particularly beneficial in the clinical domain, where manual annotation is a time-consuming and costly task, as it requires extensive labor from clinical experts. Our empirical findings demonstrate that (a) using sequence representations along with the length of sequence for seed selection shows potential towards more effective initial models, and (b) using sequence representations for sample selection leads to significantly lower manual annotation efforts, with up to 3% and 6% fewer tokens and concepts requiring annotation, respectively, compared to state-of-the-art query strategies. [ABSTRACT FROM AUTHOR]

This paper explores the relationships between natural language lexicons in lexical semantics and thesauri in information retrieval research. These different areas of knowledge have different restrictions on use of vocabulary; thesauri are used only in information search and retrieval contexts, whereas lexicons are mental systems and generally applicable in all domains of life. A set of vocabulary requirements that defines the more concrete characteristics of vocabulary items in the 2 contexts can be derived from this framework: lexicon items have to be learnable, complex, transparent, etc., whereas thesaurus terms must be effective, current and relevant, searchable, etc. The differences in vocabulary properties correlate with 2 other factors, the well-known dimension of Control (deliberate, social activities of building and maintaining vocabularies), and Syntagmatization, which is less known and describes vocabulary items' varying formal preparedness to exit the thesaurus/lexicon, enter into linear syntactic constructions, and, finally, acquire communicative functionality. It is proposed that there is an inverse relationship between Control and Syntagmatization. [ABSTRACT FROM AUTHOR]

Recent, rapid changes in technology have resulted in a proliferation of choices for music storage and access. Portable, web-enabled music devices are widespread, and listeners now enjoy a plethora of options regarding formats, devices, and access methods. Yet in this mobile music environment, listeners' access and management strategies for music collections are poorly understood, because behaviors surrounding the organization and retrieval of music collections have received little formal study. Our current research seeks to enrich our knowledge of people's music listening and collecting behavior through a series of systematic user studies. In this paper we present our findings from interviews involving 20 adult and 20 teen users of commercial cloud music services. Our results contribute to theoretical understandings of users' music information behavior in a time of upheaval in music usage patterns, and more generally, the purposes and meanings users ascribe to personal media collections in cloud-based systems. The findings suggest improvements to the future design of cloud-based music services, as well as to any information systems and services designed for personal media collections, benefiting both commercial entities and listeners. [ABSTRACT FROM AUTHOR]

Information searching in practice seldom is an end in itself. In work, work task (WT) performance forms the context, which information searching should serve. Therefore, information retrieval (IR) systems development/evaluation should take the WT context into account. The present paper analyzes how WT features: task complexity and task types, affect information searching in authentic work: the types of information needs, search processes, and search media. We collected data on 22 information professionals in authentic work situations in three organization types: city administration, universities, and companies. The data comprise 286 WTs and 420 search tasks (STs). The data include transaction logs, video recordings, daily questionnaires, interviews. and observation. The data were analyzed quantitatively. Even if the participants used a range of search media, most STs were simple throughout the data, and up to 42% of WTs did not include searching. WT's effects on STs are not straightforward: different WT types react differently to WT complexity. Due to the simplicity of authentic searching, the WT/ST types in interactive IR experiments should be reconsidered. [ABSTRACT FROM AUTHOR]

Modern search systems often meet their users' information needs, but when the system fails, searchers struggle to formulate effective queries. Query suggestions may help, but research suggests these often go unused. Although much is known about how searchers scan results pages when assessing relevance, little is known about the processes searchers use when struggling to reformulate queries. Investigating how searchers overcome query difficulties, and how search systems help and hinder that process, requires enquiry into the cognitive procedures searchers use to select words for queries. The purpose of this paper is to investigate one cognitive process involved: semantic priming of words in memory. A framework for conceptualizing the role of semantic priming in search interaction is presented, along with results from two experiments that applied research methods from cognitive psychology, in an investigation of word selection and subsequent search for selected words. The results show that word selection activates related words in memory and that looking for a selected word among related words is effortful. The finding suggests that semantic priming may play a role in the difficulties people experience when reformulating queries. Ideas for continued development of semantic priming methods and their use in future research are also presented. [ABSTRACT FROM AUTHOR]

The segment of companies providing storage services and hardware for end users and small businesses has been growing in the past few years. Cloud storage, personal network-attached storage ( NAS), and external hard drives are more affordable than ever before and one would think that backing up personal digital information is a straightforward process nowadays. Despite this, small group studies and corporate surveys show the opposite. In this paper we present the results from a quantitative and qualitative survey of 319 participants about how they back up their personal computers and restore personal information in case of computer failures. The results show that the majority of users do manual, selective, and noncontinuous backups, rely on a set of planned and unplanned backups (as a consequence of other activities), have inadequate knowledge about possible solutions and implications of using known solutions, and so on. The study also reveals that around a fifth of all computers are not backed up, and a quarter of most important files and a third of most important folders at the time of the survey could not be (fully) restored in the event of computer failure. Based on the results, several implications for practice and research are presented. [ABSTRACT FROM AUTHOR]

Traditionally, many information retrieval models assume that terms occur in documents independently. Although these models have already shown good performance, the word independency assumption seems to be unrealistic from a natural language point of view, which considers that terms are related to each other. Therefore, such an assumption leads to two well-known problems in information retrieval ( IR), namely, polysemy, or term mismatch, and synonymy. In language models, these issues have been addressed by considering dependencies such as bigrams, phrasal-concepts, or word relationships, but such models are estimated using simple n-grams or concept counting. In this paper, we address polysemy and synonymy mismatch with a concept-based language modeling approach that combines ontological concepts from external resources with frequently found collocations from the document collection. In addition, the concept-based model is enriched with subconcepts and semantic relationships through a semantic smoothing technique so as to perform semantic matching. Experiments carried out on TREC collections show that our model achieves significant improvements over a single word-based model and the Markov Random Field model (using a Markov classifier). [ABSTRACT FROM AUTHOR]

Finding software for reuse is a problem that programmers face. To reuse code that has been proven to work can increase any programmer's productivity, benefit corporate productivity, and also increase the stability of software programs. This paper shows that fuzzy retrieval has an improved retrieval performance over typical Boolean retrieval. Various methods of fuzzy information retrieval implementation and their use for software reuse will be examined. A deeper explanation of the fundamentals of designing a fuzzy information retrieval system for software reuse is presented. Future research options and necessary data storage systems are explored. [ABSTRACT FROM AUTHOR]

In several author name disambiguation studies, some ethnic name groups such as East Asian names are reported to be more difficult to disambiguate than others. This implies that disambiguation approaches might be improved if ethnic name groups are distinguished before disambiguation. We explore the potential of ethnic name partitioning by comparing performance of four machine learning algorithms trained and tested on the entire data or specifically on individual name groups. Results show that ethnicity-based name partitioning can substantially improve disambiguation performance because the individual models are better suited for their respective name group. The improvements occur across all ethnic name groups with different magnitudes. Performance gains in predicting matched name pairs outweigh losses in predicting nonmatched pairs. Feature (e.g., coauthor name) similarities of name pairs vary across ethnic name groups. Such differencesmay enable the development of ethnicity-specific feature weights to improve prediction for specific ethic name categories. These findings are observed for three labeled data with a natural distribution of problem sizes as well as one in which all ethnic name groups are controlled for the same sizes of ambiguous names. This study is expected to motive scholars to group author names based on ethnicity prior to disambiguation. [ABSTRACT FROM AUTHOR]

User studies in the music information retrieval ( MIR) domain tend to be exploratory and qualitative in nature, involving a small number of users, which makes it difficult to derive broader implications for system design. In order to fill this gap, we conducted a large-scale user survey questioning various aspects of people's music information needs and behaviors. In particular, we investigated if general music users' needs and behaviors have significantly changed over time by comparing our current survey results with a similar survey conducted in 2004. In this paper, we present the key findings from the survey data and discuss 4 emergent themes-(a) the shift in access and use of personal music collections; (b) the growing need for tools to support collaborative music seeking, listening, and sharing; (c) the importance of 'visual' music experiences; and (d) the need for ontologies for providing rich contextual information. We conclude by making specific recommendations for improving the design of MIR systems and services. [ABSTRACT FROM AUTHOR]

This paper focuses on exploring the usage patterns and regularities of co-employment of various popular tags and their relationships with the activeness of users and the interest level of resources in social tagging. A hypernetwork for social tagging is constructed in which a tagging action is expressed as a hyperedge and the user, resource, and tag are expressed as nodes. Quantitative measures for the constructed hypernetwork are defined, including the hyperdegree and its distribution, the excess average hyperdegree, and the hyperdegree conditional probability distribution. Using the data set from Delicious, an empirical study was conducted. The empirical results show that multiple individual tags and one or very few popular tags are generally employed together in one tagging action, and the usage patterns and regularities of tags with varying popularity are correlated to both user activity and resource interest. The empirical results are further discussed and explained from the perspectives of tag functions and motivations. Finally, suggestions regarding the usage of various popular tags for both tagging users and service providers of social tagging are given. [ABSTRACT FROM AUTHOR]

Analyzing hyperlink patterns has been a major research topic since the early days of the web. Numerous studies reported uncovering rich information and methodological advances. However, very few studies thus far examined hyperlinks in the rapidly developing sphere of social media. This paper reports a study that helps fill this gap. The study analyzed links originating from tweets to the websites of 3 types of organizations (government, education, and business). Data were collected over an 8-month period to observe the fluctuation and reliability of the individual data set. Hyperlink data from the general web (not social media sites) were also collected and compared with social media data. The study found that the 2 types of hyperlink data correlated significantly and that analyzing the 2 together can help organizations see their relative strength or weakness in the two platforms. The study also found that both types of inlink data correlated with offline measures of organizations' performance. Twitter data from a relatively short period were fairly reliable in estimating performance measures. The timelier nature of social media data as well as the date/time stamps on tweets make this type of data potentially more valuable than that from the general web. [ABSTRACT FROM AUTHOR]

The music information retrieval ( MIR) community has long understood the role of evaluation as a critical component for successful information retrieval systems. Over the past several years, it has also become evident that user-centered evaluation based on realistic tasks is essential for creating systems that are commercially marketable. Although user-oriented research has been increasing, the MIR field is still lacking in holistic, user-centered approaches to evaluating music services beyond measuring the performance of search or classification algorithms. In light of this need, we conducted a user study exploring how users evaluate their overall experience with existing popular commercial music services, asking about their interactions with the system as well as situational and personal characteristics. In this paper, we present a qualitative heuristic evaluation of commercial music services based on Jakob Nielsen's 10 usability heuristics for user interface design, and also discuss 8 additional criteria that may be used for the holistic evaluation of user experience in MIR systems. Finally, we recommend areas of future user research raised by trends and patterns that surfaced from this user study. [ABSTRACT FROM AUTHOR]

Normalization of citation scores using reference sets based on Web of Science subject categories ( WCs) has become an established ('best') practice in evaluative bibliometrics. For example, the Times Higher Education World University Rankings are, among other things, based on this operationalization. However, WCs were developed decades ago for the purpose of information retrieval and evolved incrementally with the database; the classification is machine-based and partially manually corrected. Using the WC 'information science & library science' and the WCs attributed to journals in the field of 'science and technology studies,' we show that WCs do not provide sufficient analytical clarity to carry bibliometric normalization in evaluation practices because of 'indexer effects.' Can the compliance with 'best practices' be replaced with an ambition to develop 'best possible practices'? New research questions can then be envisaged. [ABSTRACT FROM AUTHOR]

A large body of research work examined, from both the query side and the user behavior side, the characteristics of medical- and health-related searches. One of the core issues in medical information retrieval (IR) is diversity of tasks that lead to diversity of categories of information needs and queries. From the evaluation perspective, another related and challenging issue is the limited availability of appropriate test collections allowing the experimental validation of medically task oriented IR techniques and systems. In this paper, we explore the peculiarities of TREC and CLEF medically oriented tasks and queries through the analysis of the differences and the similarities between queries across tasks, with respect to length, specificity, and clarity features and then study their effect on retrieval performance. We show that, even for expert oriented queries, language specificity level varies significantly across tasks as well as search difficulty. Additional findings highlight that query clarity factors are task dependent and that query terms specificity based on domain-specific terminology resources is not significantly linked to term rareness in the document collection. The lessons learned from our study could serve as starting points for the design of future task-based medical information retrieval frameworks. [ABSTRACT FROM AUTHOR]

File sharing is an integral component of modern work. Files can be shared either using Group Information Management (GIM), where collaborators exploit a common repository (e.g., the cloud), or Personal Information Management (PIM), where files are sent via email attachments, and collaborators store files individually in personal collections. Given the recent prevalence of GIM, we compare the effects on retrieval for PIM versus GIM collections. We examine the effects of various theoretically motivated factors relating to collection size, properties of the target file, and user workload. In our study, 289 participants accessed 1,557 of their own shared files in a naturalistic setting. Results indicate that factors relating to collection size, file versions, and user workload negatively affect the retrieval of GIM more than PIM files, indicating that PIM is more scalable than GIM. Testing a very different population, we confirm previous findings that failure percentages of GIM are approximately double those of PIM. We discuss possible theoretical explanations, specifically how factors that hinder retrieval exacerbate the general GIM problem of retrieving files organized by other people. Overall, PIM's greater scalability has practical implications for fast‐growing organizations such as startups when choosing their sharing policies. [ABSTRACT FROM AUTHOR]

Recent efforts in biomedical visual question answering (VQA) research rely on combined information gathered from the image content and surrounding text supporting the figure. Biomedical journals are a rich source of information for such multimodal content indexing. For multipanel figures in these journals, it is critical to develop automatic figure panel splitting and label recognition algorithms to associate individual panels with text metadata in the figure caption and the body of the article. Challenges in this task include large variations in figure panel layout, label location, size, contrast to background, and so on. In this work, we propose a deep convolutional neural network, which splits the panels and recognizes the panel labels in a single step. Visual features are extracted from several layers at various depths of the backbone neural network and organized to form a feature pyramid. These features are fed into classification and regression networks to generate candidates of panels and their labels. These candidates are merged to create the final panel segmentation result through a beam search algorithm. We evaluated the proposed algorithm on the ImageCLEF data set and achieved better performance than the results reported in the literature. In order to thoroughly investigate the proposed algorithm, we also collected and annotated our own data set of 10,642 figures. The experiments, trained on 9,642 figures and evaluated on the remaining 1,000 figures, show that combining panel splitting and panel label recognition mutually benefit each other. [ABSTRACT FROM AUTHOR]