Text mining and information professionals: Role, issues and challenges

Security and privacy must be maintained in an information-rich environment. While information availability needs to be ensured, appropriate access also must be ensured. The complexity of this task is heightened in the presence of greater data sharing. Informatics professionals need a detailed understanding of security and privacy regulations and methods. Security and privacy have been extensively addressed in guidelines and regulations and are pivotal to the electronic health record (EHR). Informatics professionals should be familiar with security and privacy implications, especially those of the Health Insurance Portability and Accountability Act of 1996 (HIPAA) and Meaningful Use programs, although these are not the limits of regulatory involvement or best practices regarding these issues. Among the issues commonly dealt with are data breaches: unauthorized access to protected health information. Mechanisms and implications are discussed.

Purpose The purpose of this study is to advance the understanding of innovation offshoring by integrating knowledge management (KM) practices. It aims to identify how KM contributes to strategic offshoring, sourcing decisions for innovation and the utilization of knowledge-intensive business services. By doing so, it seeks to provide a framework that enhances both theoretical insights and practical applications in the field of innovation offshoring. Design/methodology/approach This study uses a tri-method approach – bibliometric analysis, co-citation analysis and text mining – to examine the intersection of innovation offshoring and KM. Data from Web of Science (WoS), analyzed using Leximancer and Bibexcel, reveal key KM practices, such as absorptive capacity and knowledge-sharing, that enhance offshoring-driven innovation. Integrating KM with global sourcing strategies is shown to manage complexities, lower transaction costs and boost innovation performance. Addressing the limitations of prior single-method studies, this research presents a structured KM-offshoring integration framework, mapping the thematic interconnections between KM practices and global sourcing strategies to enhance innovation performance and cross-border knowledge transfer. Findings This study finds that effective KM practices are crucial for optimizing innovation offshoring strategies. Key KM elements such as internal knowledge creation, absorptive capacity and knowledge-sharing routines significantly enhance innovation outcomes. The integration of KM practices with global sourcing strategies helps firms manage complexities, reduce transaction costs and improve innovation performance by leveraging global talent and resources. Research limitations/implications The study’s reliance on data from the WoS and the Association of Business Schools journal ranking may introduce selection bias, limiting the generalizability of findings. Future research should incorporate additional databases such as Scopus to validate the results. Moreover, the study predominantly uses bibliometric methods, which could benefit from being complemented by qualitative analyses to provide deeper insights into KM practices in innovation offshoring. Practical implications For practitioners, this study underscores the critical role of KM in optimizing innovation offshoring strategies. Effective KM practices enable firms to capture, share and use knowledge efficiently across global operations, mitigating the risks associated with geographical and cognitive distances in offshoring arrangements. Social implications The integration of KM in innovation offshoring has significant social implications. By effectively managing knowledge, firms can contribute to the development of global knowledge economies, promote cross-border collaborations and support the growth of local talent pools. This can lead to increased innovation, economic development and the creation of high-value jobs in emerging markets. Originality/value This study is original in its comprehensive approach to examining the intersection of innovation offshoring and KM. It provides valuable insights into how KM practices can enhance innovation performance in global sourcing contexts. The findings contribute to both academic literature and practical applications, offering a nuanced understanding of the strategic role of KM in innovation offshoring.

The aim of this paper is to explore the use and applications of Knowledge Management (KM) for improving Library and Information Services (LIS) in Bangladesh. This paper seeks to address the following research questions: How KM can be used efficiently in the libraries of Bangladesh for the improvement of LIS performance? What KM tools and techniques are being used in the libraries of Bangladesh? What difficulties are faced by the information professionals for applying KM tools and techniques in the libraries of Bangladesh? What are likely to be the means or ways to overcome those difficulties? The methodology includes a combination of both quantitative and qualitative approaches. An in-depth interview method has been used to collect data using a semi-structured questionnaire consisting of open-ended and close-ended questions. The results of this study show that document management is the highly used KM tool used in the libraries of Bangladesh followed by intranet, telephones, instant messenger, groupware, digital warehouse, and web conferencing. Results also reveal that the use and application of KM in LIS supports improved access to information resources and services, enriched professional knowledge of information professionals, enhanced environment and culture of knowledge sharing, and changed work behaviour of information professionals. The study identifies that lack of awareness about KM, lack of skilled manpower, communication gap and lack of knowledge sharing, and lack of technological infrastructure are the main problems for the use and application of KM. Finally, this study suggests that creating awareness about KM, trained and skilled manpower, reducing communication gap and improved knowledge sharing, technological and knowledge infrastructure, and the use of KM system/tools are the possible ways to overcome the problems for the use and application of KM in the libraries of Bangladesh.

The ever-increasing scientific literature and the exponential growth of large-scale molecular data have prompted active research in biological text mining to facilitate literature-based curation of molecular databases. Meanwhile, systems biology and bio-ontologies are emerging as critical tools in biological research where complex data in disparate resources are generated, integrated and analyzed. Both rely on literature for data annotation and analysis. The challenges facing us are to develop broadly utilized text mining tools and systems, and to bring together developer and user communities for system development and evaluation. We describe a framework for linking text mining tools with ontology and systems biology, extending from a previously developed text mining resource, iProLINK. We focus on molecular and ontological resources, including genes/proteins, protein-protein interaction (PPI), and Protein Ontology. The framework consists of two major components: a user interface for text mining of PPI from an integrated tool server and software modules to allow text mining outputs to be created, ranked, and used by the community. Use cases are presented for assessing the gaps and making recommendations for future development.

Text mining tools are now widely used for the efficient management of information and resources in business, academic and research organizations. This paper provides a comprehensive overview of research articles on the application of text mining techniques in the field of Supply Chain Risk Management and the apparel industry. Research articles published between 2000 and 2020, were obtained from various journals through two online databases, i.e., SCOPUS and IEEE Xplore. Through a systematic approach following PRISMA guidelines, 370 research papers were screened, filtered and finally classified into three main areas: Supply Chain Risk Management and outsourcing in the apparel industry, application of text mining in Supply Chain Risk Management and application of text mining in the apparel industry. In this study, we have identified a comprehensive list of various available data sources for text mining, methodologies and risks associated with outsourcing in the apparel industry. We classify the gaps in expanding the application of text mining in the apparel industry’s Supply Chain Risk Management. Extracting useful information from online newspapers through text mining could vividly enhance the ability to monitor supply chain risks and provide the ability to link data to provide decision makers with the right information at the right time.

Core competencies for information professionals of Thai academic libraries in the next decade (A.D. 2010–2019)

Suggests various ways in which library and information professionals can and should demonstrate to their organizations and to the public that their function is not merely useful but provides an essential service. These are likely to include a combination of marketing, corporate intelligence, management skills and innovative thought and to be based on the belief that library and information professionals are best equipped to make sense of the new areas of information and knowledge management that are currently proliferating. Argues that the key to success lies in a new style of management, defined as ‘intrapreneurial’ as applied to library and information service management. The fundamental nature and characteristics of intrapreneurs are discussed. These qualities include the ability to: allocate resources for service quality; delegate and organize; reduce individual and team stress; accept responsibility of leadership; motivate at all levels; select a good team; and develop a good team. The five requirements identified as being essential to allow the growth of intrapreneurship comprise: encouragement of risk-taking; financing, rewarding and recognizing innovation; improved means for dissemination of innovation in the public sector; provision of political leadership; and development of a new breed of public sector managers who are trained and motivated as intrapreneurs.

A paper given at STRAIT to the future, 8th Asia-Pacific Specials, Health and Law Librarians Conference, Hobart, 22–26 August 1999Knowledge management (KM) is becoming a hot topic for many organisations in the public and private sector in Australia, yet there is no generally accepted industry-wide definition, making it a difficult concept to grasp. Knowledge comes in many shapes and sizes. It can be stored in databases, printed on paper, integrated into an organisation's policies, procedures and reports, or contained within an employee's memory. Generally, knowledge management is a discipline that promotes an integrated approach to identifying, managing and sharing all of an organisation's information assets, regardless of how or where they are located. It is strategic in nature and involves the careful interaction of people, process and technology. This paper examines the nature of knowledge management and provides a framework for definition, using client examples to illustrate the issues.The drivers that have led to its increasingly rapid adoption are discussed, specifically addressing the three key issues: environmental (globalisation of business and the mobility of our workforce), organisational (the result of downsizing) and technical (the convergence of technologies). The process of developing a knowledge management strategy is then examined in detail, outlining the nature of the knowledge audit, the identification of information and knowledge hot spots and the identification of ‘quick wins’ as part of the knowledge road map. The paper then addresses the very difficult issue of measuring the return on investment of a KM strategy and outlines the short—and long-term strategies for assessing knowledge management. Finally, the critical role that information professionals can play in knowledge management is outlined, and the paper concludes by encouraging special librarians to play a much more active role in this area or else run the risk of being marginalised.

Construction of the Rangelands of the Western U.S. web site provides a dynamic model for information outreach. Central to its success is the collaboration between librarians, other information professionals, and rangeland experts committed to working together for a common goal: a selective, centralized, web-based resource designed to help meet the information needs of rangeland scientists, range professionals, students, faculty, ranchers, environmentalists, librarians, and other stakeholders. Begun over eight years ago by a University of Arizona AgNIC Project team, the effort is extending to include participation of range and information professionals from the other Western states. In addition to linking to existing digital content, the project seeks to create or support the creation of critical electronic information resources through an integrated user interface.

The Special Interest Group (SIG) on Text Mining (or BioLINK — Biological Literature, Information and Knowledge; http://www.pdg.cnb.uam.es/BioLINK/) was created to address the need for communication and interchange of ideas in the field of text mining and information extraction applied to biology and biomedicine. Information extraction (IE) is an outgrowth of work in automated natural language processing, which began in the 1950s with work on transformational grammar by Zellig Harris [5,6] and later Noam Chomsky [3,4]. Information extraction technology made rapid progress starting in the late 1980s, thanks to a series of conferences focused on evaluation of IE: the Message Understanding Conferences [1]. There is also a long history of research on applications in medicine. Applications to the medical field focus on two distinct sub-problems: improved access to the medical literature and extraction of information from patient records. Despite these successes in other fields, natural language processing (NLP) techniques were not introduced in biology until the late 1990s. Even today, there are two distinct groups: on the one hand, researchers with a background in computer science, and on the other hand, their colleagues with a background in the life sciences, with only limited interaction between the two groups. To improve this situation, the BioLINK group holds regular open meetings to bring together researchers developing text data mining tools and related language processing methods to manage the information explosion in the biomedical field. They include invited and contributed papers, with a focus on developing shared infrastructure (tools, corpora, ontologies) and challenge evaluations, in the style of the KDD Challenge Cups [2]. This year, the BioLINK SIG meeting focused on resources and tools for text mining, with special emphasis on the evaluation of these tools. Speakers from the following areas were invited:

Nowadays, business executives understand that timely and accurate knowledge has become crucial factor for making better and faster business decisions and providing in this way companies a competitive advantage. Especially, with the vast majority of corporate information stored as text in various databases, the need to efficiently extract actionable knowledge from these assets is growing rapidly. Existing approaches are incapable of handling the constantly increasing volumes of textual data and only a small percentage can be effectively analyzed. Business Intelligence (BI) provides a broad set of techniques, tools and technologies that facilitate management of business knowledge, performance, and strategy through automated analytics or human-computer interaction. It unlocks the “hidden” knowledge of the data and enables companies to gain insight into better customers, markets, and business information by combing through vast quantities of data quickly, thoroughly and with sharp analytical precision. A critical component that impacts business performance relates to the evaluation of competition. Measurement and assessment of technological and scientific innovation and the production of relative indicators can provide a clear view about progress. Information related to those activities is usually stored to large databases and can be distinguished in: research information stored in publications or scientific magazines and developmentproduction information stored in patents. Patents are closely related to Technology Watch, the activity of surveying the development of new technologies, of new products, of tendencies of technology as well as measuring their impact on actual technologies, organizations or people. Statistical exploitation of patent data may lead to useful conclusions about technological development, trends or innovation (Chappelier et al., 2002). Traditional methods of extracting knowledge from patent databases are based on manual analysis carried out by experts. Nowadays, these methods are impractical as patent databases grow exponentially. Text Mining (TM) therefore corresponds to the extension of the more traditional Data Mining approach to unstructured textual data and is primarily concerned with the extraction of information implicitly contained in collections of documents. The use of automatic analysis techniques allows us to valorize in a more efficient way the potential wealth of information that the textual databases represent (Hotho et al., 2005). This article describes a methodological approach and an implemented system that combines efficient TM techniques and tools. The BI platform enables users to access, query, analyze, and report the patents. Moreover, future trends and challenges are illustrated and some new research that we are pursuing to enhance the approach are discussed.

In traditional Chinese medicine, different diseases might be treated with the same therapy if they show the same pattern, which suggests that these different diseases could have the similar biological pathway or network in some specific fields. Rheumatoid arthritis (RA) and ulcerative colitis (UC) were taken for the potential shared biological network exploration with data mining approach. Text mining tool was applied to analyze the PubMed literature database and integrate the available biological information to seeking the potential biological network shared in RA and UC. The results show that RA and UC have immune and metabolically shared network obtained from analysis on the PubMed database with text mining. It could be concluded that the text mining could be used to explore the potentially biological network shared by different diseases, which further help for leading some new findings in human or animal study.

Text mining has been successfully used to discover interesting patterns and extract useful information from analyzing massive text data exists on the internet, books and other text sources. Computer science education has become an initiative for The National Science Foundation (NSF) and the White House Office of Science and Technology Policy (OSTP) in the United States. Finding the right tools and technologies that can support that initiative and help students succeed and do well while studying computer science is a plus. Although the literature is rich with research for computer science education, there is no clear guide on the use of learning theories to design educational games to teach computer science. Text mining can analyze the literature to find trends for designing educational games for computer science education, in addition to identifying existing gaps. The paper presents the results from analyzing 204 papers to discover the current state of research related to computer science education, using Voyant, a text mining tool. Analysis of results should provide an insight on how learning theories have been considered/used in computer science education and guide future research through identifying what learning theories need to be considered in designing educational games to teach computer science topics, such as data structures.

In many databases, biocuration primarily involves literature curation, which usually involves retrieving relevant articles, extracting information that will translate into annotations and identifying new incoming literature. As the volume of biological literature increases, the use of text mining to assist in biocuration becomes increasingly relevant. A number of groups have developed tools for text mining from a computer science/linguistics perspective, and there are many initiatives to curate some aspect of biology from the literature. Some biocuration efforts already make use of a text mining tool, but there have not been many broad-based systematic efforts to study which aspects of a text mining tool contribute to its usefulness for a curation task. Here, we report on an effort to bring together text mining tool developers and database biocurators to test the utility and usability of tools. Six text mining systems presenting diverse biocuration tasks participated in a formal evaluation, and appropriate biocurators were recruited for testing. The performance results from this evaluation indicate that some of the systems were able to improve efficiency of curation by speeding up the curation task significantly (∼1.7- to 2.5-fold) over manual curation. In addition, some of the systems were able to improve annotation accuracy when compared with the performance on the manually curated set. In terms of inter-annotator agreement, the factors that contributed to significant differences for some of the systems included the expertise of the biocurator on the given curation task, the inherent difficulty of the curation and attention to annotation guidelines. After the task, annotators were asked to complete a survey to help identify strengths and weaknesses of the various systems. The analysis of this survey highlights how important task completion is to the biocurators’ overall experience of a system, regardless of the system’s high score on design, learnability and usability. In addition, strategies to refine the annotation guidelines and systems documentation, to adapt the tools to the needs and query types the end user might have and to evaluate performance in terms of efficiency, user interface, result export and traditional evaluation metrics have been analyzed during this task. This analysis will help to plan for a more intense study in BioCreative IV.

Biomedical text mining and its applications in cancer research