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Author: Heiko Paulheim Publisher: Springer Nature ISBN: 3031303873 Category : Computers Languages : en Pages : 165
Book Description
This book explains the ideas behind one of the most well-known methods for knowledge graph embedding of transformations to compute vector representations from a graph, known as RDF2vec. The authors describe its usage in practice, from reusing pre-trained knowledge graph embeddings to training tailored vectors for a knowledge graph at hand. They also demonstrate different extensions of RDF2vec and how they affect not only the downstream performance, but also the expressivity of the resulting vector representation, and analyze the resulting vector spaces and the semantic properties they encode.
Author: Heiko Paulheim Publisher: Springer Nature ISBN: 3031303873 Category : Computers Languages : en Pages : 165
Book Description
This book explains the ideas behind one of the most well-known methods for knowledge graph embedding of transformations to compute vector representations from a graph, known as RDF2vec. The authors describe its usage in practice, from reusing pre-trained knowledge graph embeddings to training tailored vectors for a knowledge graph at hand. They also demonstrate different extensions of RDF2vec and how they affect not only the downstream performance, but also the expressivity of the resulting vector representation, and analyze the resulting vector spaces and the semantic properties they encode.
Author: William L. William L. Hamilton Publisher: Springer Nature ISBN: 3031015886 Category : Computers Languages : en Pages : 141
Book Description
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Author: Nuria Oliver Publisher: Springer Nature ISBN: 3030865207 Category : Computers Languages : en Pages : 817
Book Description
The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media.
Author: Kamal Karlapalem Publisher: Springer Nature ISBN: 3030757684 Category : Computers Languages : en Pages : 455
Book Description
The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021. The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data.
Author: Xin Wang Publisher: Springer Nature ISBN: 3030414078 Category : Computers Languages : en Pages : 398
Book Description
This book constitutes the thoroughly refereed proceedings of the 9th Joint International Semantic Technology Conference, JIST 2019, held in Hangzhou, China, in November 2019. The 24 full papers presented were carefully reviewed and selected from 70 submissions. They present applications of semantic technologies, theoretical results, new algorithms and tools to facilitate the adoption of semantic technologies and are organized in topical sections on knowledge graphs; data management; question answering and NLP; ontology and reasoning; government open data; and semantic web for life sciences.
Author: Peter A. Sammons Publisher: Gower Publishing, Ltd. ISBN: 9780566086359 Category : Business & Economics Languages : en Pages : 180
Book Description
Peter Sammons provides managers with a readable, highly practical guide to buying and managing knowledge. The author looks at the knowledge economy, to set the scene on the manager's growing responsibility to buy-in knowledge for their organization. He explores intellectual property rights: how they are created, transferred and protected. He sets out some alternative strategies to buying knowledge. There's advice on how to work with universities, contract research organizations and consultancy firms. And the most neglected area of all - knowledge transfer from supplier to buyer - is given exhaustive treatment.
Author: Xiaoyan Zhu Publisher: Springer Nature ISBN: 9811519560 Category : Computers Languages : en Pages : 226
Book Description
This book constitutes the refereed proceedings of the 4th China Conference on Knowledge Graph and Semantic Computing, CCKS 2019, held in Hangzhou, China, in August 2019. The 18 revised full papers presented were carefully reviewed and selected from 140 submissions. The papers cover wide research fields including the knowledge graph, the semantic Web, linked data, NLP, information extraction, knowledge representation and reasoning.
Author: Maryam Purvis Publisher: Springer ISBN: 3642022766 Category : Computers Languages : en Pages : 211
Book Description
This volume constitutes the revised selected papers of the First International Conference, ICCMSN 2008, held in Dunedin, New Zealand, in June 2009. The 19 revised papers presented were carefully reviewed and selected from a total of 34 submissions. The papers are organized in topical sections on virtual environments and second life; knowledge networks and learning in social networks; applications and integration of social networking systems as well as social concepts associated with social networking.
Author: Chilton, Michael A. Publisher: IGI Global ISBN: 1466646802 Category : Business & Economics Languages : en Pages : 387
Book Description
"This book examines current research in support of knowledge management by focusing on how knowledge resources can be used to create and sustain competitive advantages, combining imitation and innovation theories"--Provided by publisher.
Author: Zhiyuan Liu Publisher: Springer Nature ISBN: 9811555737 Category : Computers Languages : en Pages : 319
Book Description
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
Author: Heiko Paulheim
Author: Heiko Paulheim
Author: William L. William L. Hamilton
Author: Nuria Oliver
Author: Kamal Karlapalem
Author: Xin Wang
Author: Peter A. Sammons
Author: Xiaoyan Zhu
Author: Maryam Purvis
Author: Chilton, Michael A.
Author: Zhiyuan Liu