Search

Clinicians’ information needs are frequent and, all too often, unresolved. Online information resources are available to address many of these needs, but they are underutilized for a variety of reasons. The information needs that arise while clinicians use clinical information systems are particularly disposed to resolution with such resources. Many researchers have investigated approaches to integrating these systems with each other. One method that has been investigated and shows promise to being widely disseminated is called “infobuttons”. We describe work to develop infobutton managers that provide a flexible, generic method for integrating infobuttons with clinical systems. These infobutton managers have been successfully integrated with several different clinical information systems in an institution-independent manner. We also describe a standard developed by Health Level Seven International (HL7) to enable a standards-based approach to implementing infobuttons. Finally, we discuss ongoing research related to this kind of clinical decision support capability, including question and answering systems, as well as potential future directions.

The dramatic increase in the use of knowledge discovery applications requires end users to write complex database search requests to retrieve information. Such users are not only expected to grasp the structural complexity of complex databases but also the semantic relationships between data stored in databases. In order to overcome such difficulties, researchers have been focusing on knowledge representation and interactive query generation through ontologies, with particular emphasis on improving the interface between data and search requests in order to bring the result sets closer to users research requirements. This paper discusses ontology-based information retrieval approaches and techniques by taking into consideration the aspects of ontology modelling, processing and the translation of ontological knowledge into database search requests. It also extensively compares the existing ontology-to-database transformation and mapping approaches in terms of loss of data and semantics, structural mapping and domain knowledge applicability. The research outcomes, recommendations and future challenges presented in this paper can bridge the gap between ontology and relational models to generate precise search requests using ontologies. Moreover, the comparison presented between various ontology-based information retrieval, database-to-ontology transformations and ontology-to-database mappings approaches provides a reference for enhancing the searching capabilities of massively loaded information management systems. Keywords: Information systems, Ontology, Domain knowledge, Database, Information retrieval, Knowledge management

Even though retrieval practice typically has a robust, positive influence on memory, response format (overt vs. covert retrieval) may moderate its effect when students learn complex material. Overt retrieval is likely to promote exhaustive retrieval, whereas covert retrieval may not be exhaustive for familiar key terms. In two experiments, students were instructed to study key-term definitions and were asked to practice retrieval overtly, to practice retrieval covertly, or to restudy the definitions. Students also made metacognitive judgments. A final criterion test was administered two days later. Students’ final recall was greater after overt retrieval practice than after covert retrieval practice or restudy, with a continuously cumulating meta-analysis establishing the effect as moderate in size (pooled d = 0.43). Thus, response format does matter for learning definitions of key terms, supporting the recommendation that students use overt retrieval when using retrieval practice as a strategy to learn complex materials.

Although there has been a great deal of research into Collaborative Information Retrieval (CIR) and Collaborative Information Seeking (CIS), the majority has assumed that team members have the same level of unrestricted access to underlying information. However, observations from different domains (e.g. healthcare, business, etc.) have suggested that collaboration sometimes involves people with differing levels of access to underlying information. This type of scenario has been referred to as Multi-Level Collaborative Information Retrieval (MLCIR). To the best of our knowledge, no studies have been conducted to investigate the effect of awareness, an existing CIR/CIS concept, on MLCIR. To address this gap in current knowledge, we conducted two separate user studies using a total of 5 different collaborative search interfaces and 3 information access scenarios. A number of Information Retrieval (IR), CIS and CIR evaluation metrics, as well as questionnaires were used to compare the interfaces. Design interviews were also conducted after evaluations to obtain qualitative feedback from participants. Results suggested that query properties such as time spent on query, query popularity and query effectiveness could allow users to obtain information about team's search performance and implicitly suggest better queries without disclosing sensitive data. Besides, having access to a history of intersecting viewed, relevant and bookmarked documents could provide similar positive effect as query properties. Also, it was found that being able to easily identify different team members and their actions is important for users in MLCIR. Based on our findings, we provide important design recommendations to help develop new CIR and MLCIR interfaces.

Although the literature on information representation in decision support has argued for a long time that the way in which information is presented to decision makers should fit both task characteristics and the cognitive style of decision makers, the latter aspect has received much less attention in empirical research. Most studies that took into account cognitive style used rather general instruments to measure it, which do not focus on the specifics of managerial decision making. In this paper, we describe an experiment that uses an instrument specifically developed for a managerial context to study the relationship between cognitive style and decision performance when using tabular or graphical representations. We also take into account that having to deal with a misfitting information representation depletes cognitive resources, and thus might not only impede the solution of the current problem, but also impact subsequent problems. Our results confirm that a mismatch between information representation and cognitive style indeed has effects that last beyond the solution of the current decision problem. The fit between information representation and cognitive style impacts decisions.Cognitive style is measured by a scale specific to a managerial context.Depletion of cognitive resources due to misfit impedes subsequent decisions.

Information Retrieval is a complex task, which deals with both the subjectivity related to the user's needs and the uncertainty and vagueness that characterize the retrieval process. For this reason, deciding to which extent a document is relevant to a user's needs is not easy, and it strongly depends on several dimensions such as topicality, novelty, user's context, and so on. One of the most straightforward ways to interpret this activity is as a Multi-Criteria Decision Making (MCDM) problem, in which the choice of appropriate aggregation operators can play an important role in various tasks related to, or characterizing the IR process. This article aims to provide a presentation of the main approaches that in the literature have made use of aggregation operators in Information Retrieval.

Language processing is not an isolated capacity, but is embedded in other aspects of our cognition. However, it is still largely unexplored to what extent and how language processing interacts with general cognitive resources. This question can be investigated with cognitively constrained computational models, which simulate the cognitive processes involved in language processing. The theoretical claims implemented in cognitive models interact with general architectural constraints such as memory limitations. This way, it generates new predictions that can be tested in experiments, thus generating new data that can give rise to new theoretical insights. This theory-model-experiment cycle is a promising method for investigating aspects of language processing that are difficult to investigate with more traditional experimental techniques. This review specifically examines the language processing models of Lewis and Vasishth (2005), Reitter, Keller, and Moore (2011), and Van Rij, Van Rijn, and Hendriks (2010), all implemented in the cognitive architecture ACT-R (Anderson, Bothell, Byrne, Douglass, Lebiere, & Qin, 2004). These models are all limited by the assumptions about cognitive capacities provided by the cognitive architecture, but use different linguistic approaches. Because of this, their comparison provides insight into the extent to which assumptions about general cognitive resources influence concretely implemented models of linguistic competence. For example, the sheer speed and accuracy of human language processing is a current challenge in the field of cognitive modeling, as it does not seem to adhere to the same memory and processing capacities that have been found in other cognitive processes. Architecture-based cognitive models of language processing may be able to make explicit which language-specific resources are needed to acquire and process natural language. The review sheds light on cognitively constrained models of language processing from two angles: we discuss 1) whether currently adopted cognitive assumptions meet the requirements for language processing, and 2) how validated cognitive architectures can constrain linguistically motivated models, which, all other things being equal, will increase the cognitive plausibility of these models. Overall, the evaluation of cognitively constrained models of language processing will allow for a better understanding of the relation between data, linguistic theory, cognitive assumptions, and explanation.

Temporal features, such as an explicit date and time or a time-specific event, employ concise semantics for any kind of information retrieval. Therefore, temporal features should be suitable for linked data information retrieval. However, we have found that most linked data information retrieval techniques pay little attention to the power of temporal feature inclusion. We propose a keyword-based linked data information retrieval framework ` that can incorporate temporal features and give more concise results. The evaluation of our system performance indicates that it is promising.

The advent of modem information technology is significantly affecting many aspects of the system design process as well as the resulting user products. One tendency seems to be in the direction of integrated work stations which aim at supporting the broad diversification of tasks which professional users have to cope with. Therefore earlier efforts originating within the fields of “ergonomics” and “human factors” are undergoing a radical development in the direction of a serious concern with understanding and dealing with the basic interactions between people and their actual work situation. Major areas include user modelling, task performance, decision making and user-system communications. This concern has spawned an activity called cognitive engineering having the overall goal of providing good human-work interfaces and incorporating as a principle ingredient a cognitive task analysis (Pejtersen and Rasmussen 1986).

In today's world, there is no shortage of information. However, for a specific information need, only a small subset of all of the available information will be useful. The field of information retrieval (IR) is the study of methods to provide users with that small subset of information relevant to their needs and to do so in a timely fashion. Information sources can take many forms, but this thesis will focus on text based information systems and investigate problems germane to the retrieval of written natural language documents. Central to these problems is the notion of "topic." In other words, what are documents about? However, topics depend on the semantics of documents and retrieval systems are not endowed with knowledge of the semantics of natural language. The approach taken in this thesis will be to make use of probabilistic language models to investigate text based information retrieval and related problems. One such problem is the prediction of topic shifts in text, the topic segmentation problem. It will be shown that probabilistic methods can be used to predict topic changes in the context of the task of new event detection. Two complementary sets of features are studied individually and then combined into a single language model. The language modeling approach allows this problem to be approached in a principled way without complex semantic modeling. Next, the problem of document retrieval in response to a user query will be investigated. Models of document indexing and document retrieval have been extensively studied over the past three decades. The integration of these two classes of models has been the goal of several researchers but it is a very difficult problem. Much of the reason for this is that the indexing component requires inferences as to the semantics of documents. Instead, an approach to retrieval based on probabilistic language modeling will be presented. Models are estimated for each document individually. The approach to modeling is non-parametric and integrates the entire retrieval process into a single model. One advantage of this approach is that collection statistics, which are used heuristically for the assignment of concept probabilities in other probabilistic models, are used directly in the estimation of language model probabilities in this approach. The language modeling approach has been implemented and tested empirically and performs very well on standard test collections and query sets. In order to improve retrieval effectiveness, IR systems use additional techniques such as relevance feedback, unsupervised query expansion and structured queries. These and other techniques are discussed in terms of the language modeling approach and empirical results are given for several of the techniques developed. These results provide further proof of concept for the use of language models for retrieval tasks.

Cognitive informatics (CI) is an interdisciplinary study on modelling of the brain in terms of knowledge and information processing. In CI, objects/attributes are considered as neurons connected to each other via synapse. The relation represents the synapse in CI. In order to represent new information the brain generates new synapse or relation between the existing neurons. Therefore, the establishment of cognitive relations is essential to represent new information. In order to represent new information, we propose an algorithm which creates cognitive relation between the pair of objects and attributes by using the relational attribute and object method. Further, the cognitive relations between the pair of objects or attributes within the context could be checked with newly defined conditions, i.e. the necessary and sufficient condition. These conditions will evaluate whether the relational object and attribute is adequate to have relations between the pair of objects and attributes. The new information is obtained without increasing the number of neurons in brain. It is achieved by creating cognitive relations between the pair of objects and attributes. The obtained results are beneficial to simulate the intelligence behaviour of brain such as learning and memorizing. Integrating the idea of CI into cognitive relations is a promising and challenging research direction. In this paper, we have discussed it from the aspects of cognitive mechanism, cognitive computing and cognitive process.

Planning and decision-making are two of the cognitive functions involved in the solution of problems. These functions, among others, have been studied from the point of view of a new field known as cognitive informatics focused on the development of cognitive architectures, autonomous agents, and human robots that are capable of showing human-like behavior. We present an exhaustive study of current biological and computational models proposed in the fields of neuroscience, psychology, and cognitive informatics. Also, we present a deep review of the brain areas involved in planning, decision-making, and affection. However, the majority of the proposed computational models are seeking to mimic human external behavior. This paper aims to contribute to the cognitive informatics field with an innovative cognitive computational model of planning and decision-making. The two main differences of our model with respect to the current models in the literature are: (i) our model considers affective and motivational information as a basic and essential trigger in planning and decision-making processes; (ii) our model attempts to mimic both the internal human brain as well as the external human behavior. We developed a computational model capable of offering a direct mapping from human brain areas to computational modules of our model. Thus, in this paper we present our model from a conceptual, formal, and computational approach in order to show how our proposal must be implemented. Finally, a set of tests were conducted in order to validate our proposal. These tests show an interesting comparison between the behavior of our prototype and the behavior exhibited by some people involved in a case study.

Purpose This paper aims to understand user information behaviours when they perform multilingual information retrieval. It also offers reference for the development of multilingual information retrieval systems and relevant service platforms. Design/methodology/approach The authors designed an experiment on multilingual information retrieval with WorldWideScience, utilized Camtasia studio7 (a screen capturing and recording tool) to record overall operational processes of subjects and collected participants’ thought processes with think-aloud protocols. Meanwhile, a questionnaire survey and interviews were used to examine the subjects’ background information, their feelings for the experiment and their ideas about the experimental platform, respectively. Thirty-two valid data points were obtained by 41 subjects. Findings The users preferred their own language for retrieval. Most users from social science chose general search or advanced search freely according to the tasks. The majority of the participants selected key words directly from the tasks as search terms. Doctoral candidates were more likely to construct a search query with logic symbols. Translation tools were utilized for assisting retrieval and solving doubts of translation. When facing obstacles, users stayed on the original web page to explore continually, followed by back to homepage. Originality/value This paper provides a study of user behaviour through investigating how users behave on the whole process of retrieving multilingual information. The findings offer advice for optimizing the function of multilingual information retrieval systems and service platforms.

PurposeThe purpose of this paper is to demonstrate how to apply traditional information retrieval (IR) evaluation methods based on standards from the Text REtrieval Conference and web search evaluation to all types of modern library information systems (LISs) including online public access catalogues, discovery systems, and digital libraries that provide web search features to gather information from heterogeneous sources.Design/methodology/approachThe authors apply conventional procedures from IR evaluation to the LIS context considering the specific characteristics of modern library materials.FindingsThe authors introduce a framework consisting of five parts: search queries, search results, assessors, testing, and data analysis. The authors show how to deal with comparability problems resulting from diverse document types, e.g., electronic articles vs printed monographs and what issues need to be considered for retrieval tests in the library context.Practical implicationsThe framework can be used as a guideline for conducting retrieval effectiveness studies in the library context.Originality/valueAlthough a considerable amount of research has been done on IR evaluation, and standards for conducting retrieval effectiveness studies do exist, to the authors’ knowledge this is the first attempt to provide a systematic framework for evaluating the retrieval effectiveness of twenty-first-century LISs. The authors demonstrate which issues must be considered and what decisions must be made by researchers prior to a retrieval test.

Information is an important concept for the “information age”, the “information society” and the discipline of Information Systems (IS). However, different conceptions of information often make incommensurable assumptions about what information is. This essay introduces a ‘consequential framework’ revealing different assumptions made about the nature of information and the consequences following from these assumptions. According to this consequential framework four stances on the existence of information can be distinguished: (1) A first stance assumes information to exist independently of humans as part of the physical world, for instance, in the structure of the universe or the transmission of signals; (2) a second stance assumes that information exists in signs but in a observer independent way, such as in objective facts about things; (3) a third stance assumes that information exists only in relation to a subject, so that the same document, report or data will convey different information to different individuals; (4) a fourth stance assumes information to exist within a sociocultural setting, as lawyers, doctors or accountants differ in what is information to them. Each of these four stances makes vastly different assumptions about how information can be accessed and used by humans. This has further consequences for how information can be researched and how related concepts, such as data, signs, technology, or social context can be related to the study of information. The consequential framework introduced offers conceptual clarity regarding a central but largely ignored concept for IS and its reference disciplines.

Cognitive engineering is an applied field with roots in both cognitive science and engineering that has been used to support design of information displays, decision support, human-automation interaction, and training in numerous high risk domains ranging from nuclear power plant control to transportation and defense systems. Cognitive engineering provides a set of structured, analytic methods for data collection and analysis that intersect with and complement methods of Cognitive Informatics. These methods support discovery of aspects of the work that make performance challenging, as well as the knowledge, skills, and strategies that experts use to meet those challenges. Importantly, cognitive engineering methods provide novel representations that highlight the inherent complexities of the work domain and traceable links between the results of cognitive analyses and actionable design requirements. This article provides an overview of relevant cognitive engineering methods, and illustrates how they have been applied to the design of health information technology (HIT) systems. Additionally, although cognitive engineering methods have been applied in the design of user-centered informatics systems, methods drawn from informatics are not typically incorporated into a cognitive engineering analysis. This article presents a discussion regarding ways in which data-rich methods can inform cognitive engineering.

Research on how clinicians search shows that they pose queries according to three common clinical tasks: searching for diagnoses, searching for treatments and searching for tests. We hypothesise, therefore, that structuring an information retrieval system around these three tasks would be beneficial when searching for evidence-based medicine (EBM) resources in medical digital libraries. Task-oriented (diagnosis, test and treatment) information was extracted from free-text medical articles using a natural language processing pipeline. This information was integrated into a retrieval and visualisation system for EBM search that allowed searchers to interact with the system via task-oriented filters. The effectiveness of the system was empirically evaluated using TREC CDS—a gold standard of medical articles and queries designed for EBM search. Task-oriented information was successfully extracted from 733,138 articles taken from a medical digital library. Task-oriented search led to improvements in the quality of search results and savings in searcher workload. An analysis of how different tasks affected retrieval showed that searching for treatments was the most challenging and that the task-oriented approach improved search for treatments. The most savings in terms of workload were observed when searching for treatments and tests. Overall, taking into account different clinical tasks can improve search according to these tasks. Each task displayed different results, making systems that are more adaptive to the clinical task type desirable. A future user study would help quantify the actual cost-saving estimates.

Academic advisors assist students in academic, professional, and personal matters. Successful advising increases applicant yield and student retention, improves graduation rates, and helps students meet educational goals. This work presents a cognitive advising system that answers questions related to a university and its programs. This system uses IBM Watson’s cognitive intelligence to identify the question category and then answer the question accordingly. To implement the system, the authors created a four-step systems development process. Their approach will be used as part of a Cognitive Computing in Business course that exposes student teams with diverse skills and backgrounds to both developing and deploying cognitive systems.

The amount of information in hospitals and the medical field is consistently increasing. Traditional information retrieval systems in hospitals are lacking, and the medical industry has been slow to adopt technology because it requires accurate and effective information. In order to achieve a solution, Statistics and Collaborative Knowledge Exchange (SCKE) is a multi-agent system that allows the retrieval and exchange of information between hospitals. The proposed system uses semantic search techniques to provide results that are more accurate. We examined the system accuracy, and the experiment results show a high accuracy rate, regardless of the number of queries.

This publication presents new paradigms of cognitive management in information systems. The cognitive management aspects dedicated to support management processes are based on interpretations of the meaning of the analyzed data/situation, etc. The management processes are shown as it is applied to different subjects because of the diverse possibilities of executing management processes. This publication shows the versatility of the presented approaches. Cognitive management processes were defined on the basis of methods for the cognitive interpretation of information applied to semantic information contained in data sets. Semantic data interpretation used to determine the meaning of the analyzed information, and semantic layers contained in data sets. Also is dedicated to significance for the whole-process of cognitive management. Cognitive management processes were discussed as they are applied to various subjects, i.e. data, information, structures, processes or situations. Cognitive management processes were defined for the purpose of supporting existing data management solutions.

A factographic information retrieval with human involvement which consists of two stages is given detailed consideration in this paper. In the first stage, the retrieval without direct human involvement is implemented. In the second stage, the retrieval assumes the human involvement. This retrieval includes a pattern recognition algorithm, and they are implemented for retrieval only one document among the variety of similar documents. An analytical model of the retrieval block is developed. This model is presented by effectiveness indicator: average length of the recommendatory list provided by the retrieval block enabling the human operator to take the final decision.

This study investigated the effects of cognitive style and emotional trade-off difficulty (ETOD) on information processing in decision-making. Eighty undergraduates (73.75% female, M = 21.90), grouped according to their cognitive style (field-dependent or field-independent), conducted an Information Display Board (IDB) task, through which search time, search depth and search pattern were measured. Participants' emotional states were assessed both before and after the IDB task. The results showed that participants experienced significantly more negative emotion under high ETOD compared to those under low ETOD. While both cognitive style and ETOD had significant effects on search time and search depth, only ETOD significantly influenced search pattern; individuals in both cognitive style groups tended to use attribute-based processing under high ETOD and to use alternative-based processing under low ETOD. There was also a significant interaction between cognitive style and ETOD for search time and search depth. We propose that these results are best accounted for by the coping behaviour framework under high ETOD, and by the negative emotion hypothesis under low ETOD.

Internet searches that occur in learning contexts are very different in nature from traditional "lookup" or "known item" searches: students usually perform searches to gather information about or master a certain topic, and the search engine is used as an aid in the exploration of a domain of knowledge. This paper presents SDE (Search Discover Explore), an exploratory search engine for educational resources that was built on top of the knowledge provided by Wikipedia: the set of its articles provides the search space (the set of topics that users can investigate), and the relationships between Wikipedia articles inform the suggestions that the search engine provides to students to go deeper in the exploration of a certain domain of knowledge. SDE indexes several hundreds of thousands of educational resources from high-quality Web sources, such as Project Gutenberg and Open Education Europe, among many others. This paper also reports the results of the evaluation of SDE by experts in Technology Enhanced Learning in several workshops that took place across Europe in the context of the European FP7 project iTEC. These results enable us to conclude that the exploratory search paradigm, making use of knowledge mined from Wikipedia, is a very promising approach for building information retrieval systems to be used in learning contexts. The research presented in this manuscript stems from the work we have done in the context of the European FP7 project iTEC (Innovative Technologies for an Engaging Classroom). We developed a conceptbased search engine for educational resources that leverages Wikipedia as background knowledge, and the evaluation of the system with end-users in pilots was very positive. We are aware of the huge potential of concept-based document representations for Information Retrieval, Classification, Clustering, and Recommendations, among other areas of application. The following URL provides more information on the conceptbased search engine, it is an iTEC research report: http://files.eun.org/itec/del/ITECD10p4.pdfThe manuscript that we are submitting for review aims at understanding the nature of concept-based representations, as opposed to Word-based ones.

This paper has two distinct but related goals: (1) to identify some of the potential consequences of the Internet for our cognitive abilities and (2) to suggest an approach to evaluate these consequences. I begin by outlining the Google effect, which (allegedly) shows that when we know information is available online, we put less effort into storing that information in the brain. Some argue that this strategy is adaptive because it frees up internal resources which can then be used for other cognitive tasks, whereas others argue that this is maladaptive because it makes us less knowledgeable. I argue that the currently available empirical evidence in cognitive psychology does not support strong conclusions about the negative effects of the Internet on memory. Before we can make value-judgements about the cognitive effects of the Internet, we need more robust and ecologically-valid evidence. Having sketched a more nuanced picture of the Google effect, I then argue that the value of our cognitive abilities is in part intrinsic and in part instrumental, that is, they are both valuable in themselves and determined by the socio-cultural context in which these cognitive abilities are utilised. Focussing on instrumental value, I argue that, in an information society such as ours, having the skills to efficiently navigate, evaluate, compare, and synthesize online information are (under most circumstances) more valuable than having a lot of facts stored in biological memory. This is so, partly because using the Internet as an external memory system has overall benefits for education, navigation, journalism, and academic scholarship.

Searching for information is a ubiquitous activity, performed in a variety of contexts and supported by rapidly evolving technologies. As a process, information search often has a spatial aspect: spatial metaphors help users refer to abstract contents, and geo-referenced information grounds entities in physical space. While information search is a major research topic in computer science, GIScience and cognitive psychology, this intrinsic spatiality has not received enough attention. This article reviews research opportunities at the crossroad of three research strands, which are (1) computational, (2) geospatial, and (3) cognitive. The articles in this special issue focus on interface design for spatio-temporal information, on the search for qualitative spatial configurations, and on a big-data analysis of the spatial relation “near”.

In Internet image retrieval, returned results may fail to satisfy the retrieval intentions of users because of noisy annotations. Solving the ambiguity in image retrieval by combining text features and visual information has been a challenging problem. In this paper, we propose a convenient and precise approach for Internet image retrieval called combined retrieval (CR), which costs minimized extra feedback to retrieve more results reflecting the query intentions of users. CR is used as a plug-in to commercial image search engines, such as Google and Bing, which are defined as host image search engines (HISE). First, in the returned result from HISE, document analysis is utilized to construct the image categories based on the Wikipedia categorical index. Returned images will be automatically categorized, and a convenient interface is provided for user feedback. Second, we describe the re-retrieval algorithm in which image data combined with particular text information will be sent to the HISE for re-retrieval. Finally, a perceptual hash based re-rank algorithm to optimize the returned images is proposed. Experimental results indicate that CR can significantly improve the retrieval performance with minimum effort and can provide a notably convenient user experience.

Search engines are among the most important information technology (IT) applications and platforms on which to conduct information search. This study contributes by investigating whether and how the search engine-enabled information search is related to accounting information effectiveness. We develop the concept of information traffic to conceptualize investor IT-enabled information search activities and to explore whether the searches captured by this concept provide any insights for understanding and enhancing accounting information effectiveness. Building upon the input-process-output model (Maines and McDaniel 2000) and with a sample of 59 accounting information items, we report that information items with higher information traffic have greater ability to explain and predict firm market value (i.e., higher information effectiveness). The impact of information traffic on information effectiveness is higher for economic upturns than for economic downturns and differs among different types of information. We propose a conceptual measure that integrates both information traffic and information effectiveness to capture information relative importance and to suggest empirically an order in importance of the ten types of information we investigate. Our dynamic analysis of information traffic reveals a significant increase of investor IT-enabled information search in the post-financial-crisis period. It also shows higher search increases for accounting items that received previously scant investor attention.

Purpose – Successful information retrieval is determined by library patrons retrieving accurate, relevant, and up-to-date information stored in documents; this affects their self-efficacy, emotions, and behavior. The purpose of this paper is to examine the impact of mastery experiences, self-evaluation, vicarious observation of others’ experiences, social feedback, and physiological state of library patrons’ emotions after information retrieval. Design/methodology/approach – A structured close-ended questionnaire survey was utilized of which 200 responses were valid and usable for data analysis. Multiple regression analysis was executed to assess the influence of personal self-evaluation, comparisons with others, physiological state, and social feedback, on the library patrons’ emotions after information retrieval. Findings – The findings of this study indicated that social feedback had the greatest influence on the library patrons’ emotions after information retrieval, followed by personal self-evaluation and physiological state. Originality/value – This study employed quantitative research design utilizing multiple regression analysis which provides useful insight for university librarians on the specific factors that have significant effects on library patrons’ emotions after information retrieval. The outcomes add a new perspective to preceding studies on library patrons’ emotions after information retrieval, which has previously been inadequately researched in the Malaysian setting.

The study investigates whether information-seeking behavior models and theories obtained in previous research are applicable to more complex tasks. It also aims to gather students’ opinions on the importance and helpfulness of various traditional and online information sources in their thesis-writing process. This study would help to develop a better understanding of the roles and impacts of these information sources in the current networked academic infrastructure. Inspired by the Information Search Process model, we divided the process into six stages and conducted three separate surveys that covered students’ feelings, thoughts and actions, as well as other important factors that might affect their behaviors in each of the stages. Our study shows that both the feelings and thoughts of students changed during the different stages of the process, and that they were generally consistent with the descriptions in the Information Search Process model. The study indicates that it is beneficial to use the Information Search Process model as the starting point for studying the student thesis-writing processes. As the outcome of the study, we ultimately proposed a multi-stage model for Chinese undergraduate students’ thesis-writing process.

Cognitive concept learning is to learn concepts from a given clue by simulating human thought processes including perception, attention and thinking. In recent years, it has attracted much attention from the communities of formal concept analysis, cognitive computing and granular computing. However, the classical cognitive concept learning approaches are not suitable for incomplete information. Motivated by this problem, this study mainly focuses on cognitive concept learning from incomplete information. Specifically, we put forward a pair of approximate cognitive operators to derive concepts from incomplete information. Then, we propose an approximate cognitive computing system to perform the transformation between granular concepts as incomplete information is updated periodically. Moreover, cognitive processes are simulated based on three types of similarities. Finally, numerical experiments are conducted to evaluate the proposed cognitive concept learning methods.

This work presents two studies that aim to discover whether age can be used as a suitable metric for distinguishing performance between individuals or if other factors can provide greater insight. Information retrieval tasks are used to test the performance of these factors. First, a study is introduced that examines the effect that fluid intelligence and Internet usage has on individuals. Second, a larger study is reported on that examines a collection of Internet and cognitive factors in order to determine to what extent each of these metrics can account for disorientation in users. This work adds to growing evidence showing that age is not a suitable metric to distinguish between individuals within the field of human-computer interaction. It shows that factors such as previous Internet experience and fluid-based cognitive abilities can be used to gain better insight into users’ reported browsing experience during information retrieval tasks.

This paper explores the major challenges of access to information that face the consumers of various databases of the Institute of Scientific Information on Social Sciences (INION) of the Russian Academy of Sciences. The key features of different types of information barriers are investigated. The possibilities and means to overcome these barriers are explored in the current context.

In this paper we present a new personalized approach, which integrates a social profile into a distributed search system. Most previous studies on distributed information retrieval are based on textual information, and rarely consider any social information. Based on this observation, we propose an approach which exploits the social profile and the different relations between social entities. We believe that this method can: (i) enhance a query expansion, (ii) personalize and improve both the source selection and the result merging process in distributed information retrieval systems.

Recruitment professionals perform complex search tasks in order to find candidates that match client job briefs. In completing these tasks, recruiters have to contend with many core information retrieval (IR) challenges, such as query formulation, refinement and evaluation. However, despite the similarities with more established communities of information professionals such as patent lawyers and healthcare librarians, this community has been largely overlooked in IR research. This article presents results of a survey of recruitment professionals to understand their information-seeking behaviour and their needs regarding IR systems and applications. The findings are discussed in the context of the recruitment industry as a whole and are moderated by insights from professional recruiters.

Models and algorithms of information retrieval in the global and local computer networks on the basis of thematic and dynamic text corpora are proposed in this article. The developed algorithms provide the effectiveness of documents information retrieval and notable for universality, i. e., for the independence from topics. Information system adjustment to a particular data domain can be fully automated, it adds up to the creation of a respective thematic text corpus and an actualization of dictionaries of the knowledge database.

Introduction This chapter considers the social component of interactive information retrieval: what is the role of other people in searching and browsing? For simplicity we begin by considering situations without computers. After all, you can interactively retrieve information without a computer; you just have to interact with someone or something else. Such an analysis can then help us think about the new forms of collaborative interactions that extend our conceptions of information search, made possible by the growth of networked ubiquitous computing technology. Information searching and browsing have often been conceptualized as solitary activities; however, they always have a social component. We may talk about ‘the’ searcher or ‘the’ user of a database or information resource. Our focus may be on individual uses and our research may look at individual users. Our experiments may be designed to observe the behaviors of individual subjects. Our models and theories derived from our empirical analyses may focus substantially or exclusively on an individual's evolving goals, thoughts, beliefs, emotions and actions. Nevertheless there are always social aspects of information seeking and use present, both implicitly and explicitly. We start by summarizing some of the history of information access with an emphasis on social and collaborative interactions. Then we look at the nature of recommendations, social search and interfaces to support collaboration between information seekers. Following this we consider how the design of interactive information systems is influenced by social elements. Background The history of organized information is, necessarily, one of social relationships. The organizers structured documents to ease retrieval, possibly for themselves in the first instance, but then for users who wished to access the collection. Personal libraries, organizational libraries, and then public libraries provided access to information resources hundreds of years before computers arrived. These libraries contained, or indeed embodied, two main social relationships between the librarians and the users. The simplest relationship was a dialogue between the information seeker and the librarian, which has evolved into the reference interview. The other relationship was indirect, via the structure of the collection; where the librarians arranged the items to facilitate retrieval. These arrangements have evolved into classification systems such as the Dewey Decimal Classification. As the literary record grew through books and journals the connections between published works, authors, readers and information seekers have become ever more complex.

The need of effective and efficient method to retrieving non-Web-enabled and Web-enabled information entities is essential, due to the fact of inaccuracy of the existing search engines that still use traditional term-based indexing for text documents and annotation text for images, audio and video files. Previous works showed that incorporating the knowledge in the form of concepts into an information retrieval system may increase the effectiveness of the retrieving method. Unfortunately, most of the works that implemented the concept-based information retrieval system still focused on one information format. This paper proposes a multi-format (text, image, video and, audio) concept-based information retrieval method for Cloud environment. The proposed method is implemented in a laboratory-scale heterogeneous cloud environment using Eucalyptus middleware. 755 multi-format information is experimented and the performance of the proposed method is measured.

Most information retrieval technologies are designed to facilitate information discovery. However, much knowledge work involves finding and re-using previously seen information. We describe the design and evaluation of a system, called Stuff I've Seen (SIS), that facilitates information re-use. This is accomplished in two ways. First, the system provides a unified index of information that a person has seen, whether it was seen as email, web page, document, appointment, etc. Second, because the information has been seen before, rich contextual cues can be used in the search interface. The system has been used internally by more than 230 employees. We report on both qualitative and quantitative aspects of system use. Initial findings show that time and people are important retrieval cues. Users find information more easily using SIS, and use other search tools less frequently after installation.

Research group of Computer Sciences at DMU, Psychology Research Group at University of Birmingham and Reseach Group of Computer Science at University of Northumbria. With the latest developments in computer technologies and artificial intelligence (AI) techniques, more opportunities of cognitive data acquisition and stimulation via game-based systems have become available for computer scientists and psychologists. This may lead to more efficient cognitive learning model developments to be used in different fields of cognitive psychology than in the past. The increasing popularity of computer games among a broad range of age groups leads scientists and experts to seek game domain solutions to cognitive based learning abnormalities, especially for younger age groups and children. One of the major advantages of computer graphics and using game-based techniques over the traditional face-to-face therapies is that individuals, especially children immerse in the game’s virtual environment and consequently feel more open to share their cognitive behavioural characteristics naturally. The aim of this work is to investigate the effects of graphical agents on cognitive behaviours to generate more efficient cognitive models.

An increasing amount of information is packed into BIMs, with the 3D geometry serving as a central index leading to other information. The 3DIR project investigates information retrieval from such environments. Here, the 3D visualization can be exploited when formulating queries, computing the relevance of information items, or visualizing search results. The need for such a system was specified using workshops with end users. A prototype was built on a commercial BIM platform. Following an evaluation, the system was enhanced to exploit model topology. Relationships between 3D objects are used to widen the search, whereby relevant information items linked to a related 3D object (rather than linked directly to objects selected by the user) are still retrieved but ranked lower. An evaluation of the enhanced prototype demonstrates its effectiveness but highlights its added complexity. Care needs to be taken when exploiting topological relationships, but that a tight coupling between text-based retrieval and the 3D model is generally effective in information retrieval from BIMs.

The volume of information being created, generated and stored is huge. Without adequate knowledge of Information Retrieval (IR) methods, the retrieval process for information would be cumbersome and frustrating. Studies have further revealed that IR methods are essential in information centres (for example, Digital Library environment) for storage and retrieval of information. Therefore, with more than one billion people accessing the Internet, and millions of queries being issued on a daily basis, modern Web search engines are facing a problem of daunting scale. The main problem associated with the existing search engines is how to avoid irrelevant information retrieval and to retrieve the relevant ones. In this study, the existing system of library retrieval was studied. Problems associated with them were analyzed in order to address this problem. The concept of existing information retrieval models was studied, and the knowledge gained was used to design a digital library information retrieval system. It was successfully implemented using a real life data. The need for a continuous evaluation of the IR methods for effective and efficient full text retrieval system was recommended.

Search engines have become increasingly better at providing information to users. However, they still face major challenges, such as determining how searchers process information, how they make relevance judgments, and how their cognitive or emotional states affect their search progress. We address these challenges by exploring searchers' affective dimension. In particular, we investigate how feelings, facial expressions, and electrodermal activity (EDA) could help to understand information relevance, search progress, and information processing strategies (IPS). To meet this goal, we designed an experiment in which 45 participants were exposed to affective stimuli prior to solving a fact-finding search task. Results indicate that initial affective dimensions are linked to IPSs, search progress, and task completion. However, further analyses suggest that affective-related features alone have limited utility in the binary classification of relevance using machine learning techniques.

The goal of an information retrieval system is to provide the information that is relevant to the user's query. In some cases the information relevant to the user request may not exist in the user's native language. Situations may also arise where the user is able to read documents in languages different from the native one, but might have difficulty in formulating queries in them. The main intention behind Multilingual Information Retrieval is to find the relevant information available irrespective of the language used in the query.

Objective: Information visualization is the study of interactive depictions of abstract and data to strengthen the human cognition. Designing an appropriate information visualization system may be very useful technique for scholars, who intent to get scientific information from digital libraries. The objective of current study was to map and visualize the key-information of dissertations in academic libraries. To achieve the aim, an information retrieval system was designed to present the interactive graphic view of dissertations’ subjects in academic. Methods: An information retrieval system was designed by information visualization toolkit that presents the related subjects of dissertations in academic libraries. In addition, the satisfaction-levels of library-users were analyzed by administrating a standard questionnaire (QUIS Questionnaire). Results: The study indicated that the designed IR system helped to provide a user-friendly environment through displaying subjective relations of dissertations, overwhelming variety of colors in displaying information. Fast and easy access to the cover-to-cover information of dissertations and user-interaction facilities are the advantages of designed IR. Analysis of data furthermore indicated that the users’ satisfaction from the system was from medium to high grade. Conclusion: Designing the IR-system revealed an excessive influence on users’ satisfaction; therefore, proposing such systems for employing in academic libraries is very suitable and its implementation is necessary.

The Product Lifecycle Management (PLM) approach faces new challenges if transferred for Industrial Product Service Systems (IPS 2 ). The vast amount of heterogeneous data generated throughout an IPS 2 's lifecycle complicates the retrieval of required information for IPS 2 actors. However, these actors’ risky decisions determine an IPS 2 's success during use phase. Thus, an approach is needed that supports actors in finding targeted information. This paper presents a framework that utilizes semantic and text mining techniques in order to improve the information retrieval process in IPS 2 -PLM and to allow IPS 2 actors to focus on their value-adding tasks rather than spending a lot of time for finding information.