Literature Based Discovery
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Literature Based Discovery
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Author : Peter Bruza
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-08-17
Literature Based Discovery written by Peter Bruza and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-17 with Computers categories.
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.
Literature Based Discovery
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Author : Peter Bruza
language : en
Publisher: Springer
Release Date : 2009-08-29
Literature Based Discovery written by Peter Bruza and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-29 with Computers categories.
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.
Literature Based Discovery
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Author : Ramalakshmi Sundar
language : en
Publisher:
Release Date : 2007
Literature Based Discovery written by Ramalakshmi Sundar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
Chemical Information Mining
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Author : Debra L. Banville
language : en
Publisher: CRC Press
Release Date : 2008-12-15
Chemical Information Mining written by Debra L. Banville and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-12-15 with Computers categories.
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
Using Statistical And Knowledge Based Approaches For Literature Based Discovery
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Author :
language : en
Publisher:
Release Date : 2007
Using Statistical And Knowledge Based Approaches For Literature Based Discovery written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Biology categories.
Trends And Applications In Knowledge Discovery And Data Mining
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Author : Wei Lu
language : en
Publisher: Springer Nature
Release Date : 2020-10-14
Trends And Applications In Knowledge Discovery And Data Mining written by Wei Lu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-14 with Computers categories.
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)
Systematic Acceleration Of Radical Discovery And Innovation In Science And Technology
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Author : Ronald N. Kostoff
language : en
Publisher:
Release Date : 2005-01-01
Systematic Acceleration Of Radical Discovery And Innovation In Science And Technology written by Ronald N. Kostoff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-01-01 with Bibliometrics categories.
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.
Review Of The Book Literature Based Discovery By Peter Bruza And Marc Weeber Eds
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Author :
language : en
Publisher:
Release Date :
Review Of The Book Literature Based Discovery By Peter Bruza And Marc Weeber Eds written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Indirect Relatedness Evaluation And Visualization For Literature Based Discovery
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Author : Sam Henry
language : en
Publisher:
Release Date : 2019
Indirect Relatedness Evaluation And Visualization For Literature Based Discovery written by Sam Henry and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Text data mining categories.
The exponential growth of scientific literature is creating an increased need for systems to process and assimilate knowledge contained within text. Literature Based Discovery (LBD) is a well established field that seeks to synthesize new knowledge from existing literature, but it has remained primarily in the theoretical realm rather than in real-world application. This lack of real-world adoption is due in part to the difficulty of LBD, but also due to several solvable problems present in LBD today. Of these problems, the ones in most critical need of improvement are: (1) the over-generation of knowledge by LBD systems, (2) a lack of meaningful evaluation standards, and (3) the difficulty interpreting LBD output. We address each of these problems by: (1) developing indirect relatedness measures for ranking and filtering LBD hypotheses; (2) developing a representative evaluation dataset and applying meaningful evaluation methods to individual components of LBD; (3) developing an interactive visualization system that allows a user to explore LBD output in its entirety. In addressing these problems, we make several contributions, most importantly: (1) state of the art results for estimating direct semantic relatedness, (2) development of set association measures, (3) development of indirect association measures, (4) development of a standard LBD evaluation dataset, (5) division of LBD into discrete components with well defined evaluation methods, (6) development of automatic functional group discovery, and (7) integration of indirect relatedness measures and automatic functional group discovery into a comprehensive LBD visualization system. Our results inform future development of LBD systems, and contribute to creating more effective LBD systems.
Augmented Knowledge Graphs For Literature Based Discovery Akg Lbd
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Author : Ali Daowd
language : en
Publisher:
Release Date : 2023
Augmented Knowledge Graphs For Literature Based Discovery Akg Lbd written by Ali Daowd and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
The biomedical literature is expanding exponentially, generating a vast amount of knowledge that frequently goes unnoticed. Consequently, there is an urgent need to develop methods to mine knowledge from published literature to facilitate the automated discovery of hidden biomedical knowledge. Literature-Based Discovery (LBD) is a novel paradigm that aims to uncover new knowledge from the literature via transitive inference. Advances in text mining and knowledge extraction methods have enabled semantics-based LBD, which extracts knowledge in the form of subject-predicate-object semantic triples represented in a Knowledge Graph (KG). The subject and object are normalized biomedical concepts, and the predicate denotes the semantic relation between them. Semantics-based LBD has not seen large scale adoption due to several challenges. Firstly, knowledge extraction methods result in incomplete knowledge extraction due to missing semantic relations. Secondly, extracted biomedical entities are represented by granular and ambiguous representations, leading to a large discovery search space. Thirdly, the over-generation of spurious discoveries as output obscures meaningful discoveries. This dissertation investigates semantics-based methods and KG representation learning to develop novel solutions addressing the fundamental challenges in semantic-based LBD. Specifically, we address the challenges by: (i) incorporating state-of-the-art knowledge extraction to acquire semantic-based knowledge from the literature; (ii) utilizing concept disambiguation and semantic alignment techniques to resolve ambiguity and granularity of concept representations; (iii) leveraging a multi-step Knowledge Graph Completion (KGC) methodology to augment the literature-based KG by predicting missing relations using KG embeddings; and (iv) presenting a knowledge filtering and ranking approach based on the principles of information theory to prioritize interesting discoveries. The outcome of this dissertation is the novel Augmented Knowledge Graphs for LBD (AKG-LBD) framework that enhances traditional semantics-based LBD frameworks. The AKG-LBD framework is assessed by replicating biomedical discoveries published in peer-reviewed journals. The results indicate that AKG-LBD can discover meaningful knowledge with high precision relative to baseline approaches. The main implication of this dissertation is that KGC methods, combined with semantic alignment, enhances the performance of semantics-based LBD by generating augmented literature-based KGs. Additionally, the knowledge filtering and ranking methods are capable of prioritizing interesting knowledge which facilitates the exploration of meaningful biomedical discoveries.