This is the first coherent book on literature-based discovery (LBD). LBD is an inherently multi-disciplinary enterprise. The aim of this volume is to plant a flag in the ground and inspire new researchers to the LBD challenge.

This is the first coherent book on literature-based discovery (LBD). LBD is an inherently multi-disciplinary enterprise. The aim of this volume is to plant a flag in the ground and inspire new researchers to the LBD challenge.

The First Book to Describe the Technical and Practical Elements of Chemical Text MiningExplores the development of chemical structure extraction capabilities and how to incorporate these technologies in daily research workFor scientific researchers, finding too much information on a subject, not finding enough information, or not being able&nb

Literature-based discovery (LBD) is a systematic two-component approach to bridging unconnected disciplines (front-end component, back-end component) based on text mining procedures. LBD allows potentially radical discovery and innovation (radical discovery and innovation is used in the sense of discovery and innovation arising from unexpected insights originating in very disparate disciplines) to be hypothesized. Classically, the LBD front-end component has been used to identify the pool of potential discovery and innovation candidates, and the LBD back-end component has been used to hypothesize the potential discovery and innovation based on literature analysis alone (1). In this report, a systematic two-component approach to bridging unconnected disciplines and accelerating potentially radical discovery and innovation (based wholly or partially on text mining procedures) is presented. The front-end component has similar objectives to those in the classical LBD approach, although it is different mechanistically and operationally. The front- end component in the present report will systematically identify technical disciplines/ technologies (and their associated leading experts) that are directly or indirectly-related to solving technical problems of high interest. The back-end component in the present report is actually a family of back-end techniques, only one of which shares the strictly literature-based analysis of the classical LBD approach. These multiple back-end techniques will identify potential radical discovery and innovation for many different applications. In the present report, the two component techniques that use strictly literature- based analysis for the backend are termed literature-based discovery. The two- component techniques that do not focus on literature-based analysis for the back-end are termed literature-assisted discovery.

Literature-Based Discovery (LBD) refers to the process of uncovering hidden connections that are implicit in scientific literature. Numerous hypotheses have been generated from scientific literature using the LBD paradigm, which influenced innovations in diagnosis, treatment, preventions and overall public health. However, much of the existing research on discovering hidden connections among concepts have used distributional statistics and graph-theoretic measures to capture implicit associations. Such metrics do not explicitly capture the semantics of hidden connections. Rather, they only allude to the existence of meaningful underlying associations. To gain in-depth insights into the meaning of hidden (and other) connections, complementary methods have often been employed. Some of these methods include: 1) the use of domain expertise for concept filtering and knowledge exploration, 2) leveraging structured background knowledge for context and to supplement concept filtering, and 3) developing heuristics a priori to help eliminate spurious connections. While effective in some situations, the practice of relying on domain expertise, structured background knowledge, and heuristics to complement distributional and graph-theoretic approaches, has serious limitations. The main issue is that the intricate context of complex associations is not always known a priori and cannot easily be computed without understanding the underlying semantics of the associations. Complex associations should not be overlooked, since they are often needed to elucidate the mechanisms of interaction and causality relationships among concepts. Moreover, they can capture the broader aspects of a biomedical sub-domain by segregating associations along different thematic dimensions, such as Metabolic Function, Pharmaceutical Treatment, and Neurological Activity. This dissertation proposes an innovative context-driven, automatic subgraph creation method for finding hidden and complex associations among concepts, along multiple thematic dimensions. It outlines definitions for context and shared context, based on implicit and explicit (or formal) semantics, which compensate for deficiencies in statistical and graph-based metrics. It also eliminates the need for heuristics a priori. An evidence-based evaluation of the proposed framework showed that 8 out of 9 existing scientific discoveries could be recovered using this approach. Additionally, insights into the meaning of associations could be obtained using provenance provided by the system. In a statistical evaluation to determine the interestingness of the generated subgraphs, it was observed that an arbitrary association is mentioned in only approximately 4 articles in MEDLINE, on average. These results suggest that leveraging implicit and explicit context, as defined in this dissertation, is a significant advancement of the state-of-the-art in LBD research.

This book constitutes the thoroughly refereed post-workshop proceedings of the workshops that were held in conjunction with the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, in Singapore, Singapore, in May 2020. The 17 revised full papers presented were carefully reviewed and selected from a total of 50 submissions. The five workshops were as follows: · First International Workshop on Literature-Based Discovery (LBD 2020) · Workshop on Data Science for Fake News (DSFN 2020) · Learning Data Representation for Clustering (LDRC 2020) · Ninth Workshop on Biologically Inspired Techniques for Data Mining (BDM · 2020) · First Pacific Asia Workshop on Game Intelligence & Informatics (GII 2020)

This landmark textbook is an essential primer for students and practitioners interested in information seeking, needs and behaviour, user studies and information literacy. Introduction to Information Behaviour uses a combination of theory and practical context to map out what information behaviour is and what we currently know about it, before addressing how it can be better understood in the future. Nigel Ford argues that new understandings of information behaviour research may help maximise the quality and effectiveness of the way information is presented, sought, discovered, evaluated and used. The book introduces the key concepts, issues and themes of information behaviour, illustrates them using key research studies, and provides a clear path through the complex maze of theories and models. The book is structured to move from the basics to the more complex and employs the pedagogical device of “THINK” boxes which invite the reader to think about concepts as they are introduced in order to consolidate their understanding before moving on. Case studies are included throughout the text and each chapter concludes with a round-up of what has been covered, highlighting the implications for professional information practice. The key topics covered include: Defining information behaviour and why is it useful to know about it Information needs Information seeking and acquisition Collaborative information behaviour Factors affecting information behaviour Models and theories of information behaviour Research approaches and methodologies Designing information systems The future trajectory of information behaviour research and practice. Readership: This book will be core reading for students around the world, particularly those on library and information science courses. It will also be of interest to practitioners and professional information users, providers and developers.

The recent “concept of 2.0", a consequence of "Web 2.0", discusses the emergence of a new style, emancipated from the Web, which finds applications in all areas of social activity: management, innovation, education , organization, territory, etc. This book considers the implications of the changing paradigm for competitive, economic and territorial intelligence applied to innovation, value creation and enhancement of territories. Competitive intelligence is therefore in the "2.0" and its values: perpetual beta, user-generated content, social relations, etc., horizontality, a renewed legitimacy. This book, collecting contributions from international experts, testifies to the heterogeneity and richness of possible approaches. It provides a totally new way of evaluating the impact of 2.0 with concrete examples, while analyzing the theoretical models allowing the reader to develop in other contexts the described cases of success.

Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.