Download or read book Leveraging Graph Neural Networks for Efficient Word Representations written by Ryan Himes and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sentence structure can consist of a complex structure of tokens and relationships between tokens which can be hard to represent with only a sequential representation. To capture more information about sentence structure we propose representing a sentence as a graph of tokens and relationships between tokens to learn dynamic word embeddings. The graph structure is used as input for a graph neural network (GNN) to learn syntactic and semantic information about the sentence by learning a next word prediction task to learn the embeddings. We also experiment with using the graph structure as input for different natural language classification tasks. Results show that using the GNN ARMAConv on natural language classification tasks can increase accuracy over a sequential representation. Also, using the newly trained dynamic word embeddings achieves a higher validation accuracy compared to all other word embeddings tested on the tweet disaster classification data set.

Download or read book Leveraging Prior Knowledge and Structure for Data efficient Machine Learning written by Beliz Gunel and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building high-performing end-to-end machine learning systems primarily consists of developing the machine learning model and gathering high-quality training data for the application of interest, assuming one has access to the right hardware. Although machine learning models are getting increasingly commoditized in the last few years with the rise of open-sourced platforms, curating high-quality labeled training datasets is still either costly or not feasible for many real-world applications. Hence, we mainly focus on data in this thesis, specifically how to (1) reduce dependence on labeled data with data-efficient machine learning methods through either injecting domain-specific prior knowledge or leveraging existing software systems and datasets that have initially been created for different tasks, (2) effectively manage training data and build associated tooling in order to maximize the utility of the data, and (3) improve the quality of the data representations achieved by embeddings by matching the structure of the data to the geometry of the embedding space. We start by describing our works on building data-efficient machine learning methods for accelerated magnetic resonance imaging (MRI) reconstruction through physics-driven augmentations for consistency training, scale-equivariant unrolled neural networks, and weak supervision using untrained neural networks. Then, we describe our works on building data-efficient machine learning methods for natural language understanding. In particular, we discuss a supervised contrastive learning approach for pre-trained language model fine-tuning and a large-scale data augmentation method to retrieve in-domain data. Related to effectively managing training data, we discuss our proposed information extraction system for form-like documents Glean and focus on the often overlooked aspects of training data management and associated tooling. We highlight the importance of effectively managing training data by showing that it is at least as critical as the machine learning model advances in terms of downstream extraction performance on a real-world dataset. Finally, to improve embedding representations for a variety of types of data, we investigate spaces with heterogeneous curvature. We demonstrate mixed-curvature representations provide higher quality representations both for graphs and for word embeddings. Also, we investigate integrating entity embeddings from Wikidata knowledge graph to an abstractive text summarization model to enhance factuality.

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Deep Learning for Unstructured Data by Leveraging Domain Knowledge written by Shanshan Zhang and published by . This book was released on 2019 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unstructured data such as texts, strings, images, audios, videos are everywhere due to the social interaction on the Internet and the high-throughput technology in sciences, e.g., chemistry and biology. However, for traditional machine learning algorithms, classifying a text document is far more difficult than classifying a data entry in a spreadsheet. We have to convert the unstructured data into some numeric vectors which can then be understood by machine learning algorithms. For example, a sentence is first converted to a vector of word counts, and then fed into a classification algorithm such as logistic regression and support vector machine. The creation of such numerical vectors is very challenging and difficult. Recent progress in deep learning provides us a new way to jointly learn features and train classifiers for unstructured data. For example, recurrent neural networks proved successful at learning from a sequence of word indices; convolutional neural networks are effective to learn from videos, which are sequences of pixel matrices. Our research focuses on developing novel deep learning approaches for text and graph data. Breakthroughs using deep learning have been made during the last few years for many core tasks in natural language processing, such as machine translation, POS tagging, named entity recognition, etc. However, when it comes to informal and noisy text data, such as tweets, HTMLs, OCR, there are two major issues with modern deep learning technologies. First, deep learning requires large amount of labeled data to train an effective model; second, neural network architectures that work with natural language are not proper with informal text. In this thesis, we address the two important issues and develop new deep learning approaches in four supervised and unsupervised tasks with noisy text. We first present a deep feature engineering approach for informative tweets discovery during the emerging disasters. We propose to use unlabeled microblogs to cluster words into a limited number of clusters and use the word clusters as features for tweets discovery. Our results indicate that when the number of labeled tweets is 100 or less, the proposed approach is superior to the standard classification based on the bag or words feature representation. We then introduce a human-in-the-loop (HIL) framework for entity identification from noisy web text. Our work explores ways to combine the expressive power of REs, ability of deep learning to learn from large data into a new integrated framework for entity identification from web data. The evaluation on several entity identification problems shows that the proposed framework achieves very high accuracy while requiring only a modest human involvement. We further extend the framework of entity identification to an iterative HIL framework that addresses the entity recognition problem. We particularly investigate how human invest their time when a user is allowed to choose between regex construction and manual labeling. Finally, we address a fundamental problem in the text mining domain, i.e, embedding of rare and out-of-vocabulary (OOV) words, by refining word embedding models and character embedding models in an iterative way. We illustrate the simplicity but effectiveness of our method when applying it to online professional profiles allowing noisy user input. Graph neural networks have been shown great success in the domain of drug design and material sciences, where organic molecules and crystal structures of materials are represented as attributed graphs. A deep learning architecture that is capable of learning from graph nodes and graph edges is crucial for property estimation of molecules. In this dissertation, We propose a simple graph representation for molecules and three neural network architectures that is able to directly learn predictive functions from graphs. We discover that, it is true graph networks are superior than feature-driven algorithms for formation energy prediction. However, the superiority can not be reproduced on band gap prediction. We also discovered that our proposed simple shallow neural networks perform comparably with the state-of-the-art deep neural networks.

Download or read book Graph Neural Networks for Multimodal Learning and Representation written by Mahmoud Khademi and published by . This book was released on 2019 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, several deep learning models are proposed that operate on graph-structured data. These models, which are known as graph neural networks, emphasize on methods for reasoning about non-Euclidean data. By combining end-to-end and handcrafted learning, graph neural networks can supply both relational reasoning and compositionality which are extremely important in many emerging tasks. This new paradigm is also consistent with the attributes of human intelligence: a human represents complicated systems as compositions of simple units and their interactions. Another important feature of graph neural networks is that they can often support complex attention mechanisms, and learn rich contextual representations by sending messages across different components of the input data. The main focus of this thesis is to solve some multimodal learning tasks by either introducing new graph neural network architectures or extending the existing graph neural network models and applying them to solve the tasks. I address three tasks: visual question answering (VQA), scene graph generation, and automatic image caption generation. I show that graph neural networks are effective tools to achieve better performance on these tasks. Despite all the hype and excitements about the future influence of graph neural networks, an open question about graph neural networks remains: how can we obtain the (structure of) the graphs that graph neural networks perform on? That is, how can we transform sensory input data such as images and text into graphs. A second main emphasis of this thesis is, therefore, to introduce new techniques and algorithms to address this issue. We introduce a generative graph neural network model based on reinforcement learning and recurrent neural networks (RNNs) to extract a structured representation from sensory data. The specific contributions are the following: We introduce a new neural network architecture, Multimodal Neural Graph Memory Networks (MN-GMN), for the VQA task. A key issue for VQA is how to reason about information from different image regions that is relevant for answering the question. Our novel approach uses graph structure with different region features as node attributes and applies a recently proposed powerful graph neural network model, Graph Network (GN), to reason about objects and their interactions in the scene context. The flexibility of GNs allows us to integrate bimodal sources of local information, text and visual, both within and across each modality. Experiments show MN-GMN outperforms the state-of-the-art on Visual7W and VQA v2.0 datasets and achieves comparable to the state-of-the-art results on CLEVR dataset. We propose a new algorithm, called Deep Generative Probabilistic Graph Neural Networks (DG-PGNN), to generate a scene graph for an image. The input to DG-PGNN is an image, together with a set of region-grounded captions (RGCs) and object bounding-box proposals for the image. To generate the scene graph, DG-PGNN constructs and updates a new model, called a Probabilistic Graph Network (PGN). A PGN can be thought of as a scene graph with uncertainty: it represents each node and each edge by a CNN feature vector and defines a probability mass function (PMF) for node-type (object category) of each node and edge-type (predicate class) of each edge. The DG-PGNN sequentially adds a new node to the current PGN by learning the optimal ordering in a Deep Q-learning framework, where states are partial PGNs, actions choose a new node, and rewards are defined based on the ground-truth. After adding a node, DG-PGNN uses message passing to update the feature vectors of the current PGN by leveraging contextual relationship information, object co-occurrences, and language priors from captions. The updated features are then used to fine-tune the PMFs. Our experiments show that the proposed algorithm significantly outperforms the state-of-the-art results on the Visual Genome dataset for the scene graph generation. We present a novel context-aware attention-based deep architecture for image caption generation. Our architecture employs a Bidirectional Grid LSTM, which takes visual features of an image as input and learns complex spatial patterns based on a two-dimensional context, by selecting or ignoring its input. The Grid LSTM can be seen as a graph neural network model with a grid structure. The Grid LSTM has not been applied to the image caption generation task before. Another novel aspect is that we leverage a set of local RGCs obtained by transfer learning. The RGCs often describe the properties of the objects and their relationships in an image. To generate a global caption for the image, we integrate the spatial features from the Grid LSTM with the local region-grounded texts, using a two-layer Bidirectional LSTM. The first layer models the global scene context such as object presence. The second layer utilizes a novel dynamic spatial attention mechanism, based on another Grid LSTM, to generate the global caption word-by-word while considering the caption context around a word in both directions. Unlike recent models that use a soft attention mechanism, our dynamic spatial attention mechanism considers the spatial context of the image regions. Experimental results on the MS-COCO dataset show that our architecture outperforms the state-of-the-art.

Download or read book Graph Prompting Unlocking the Power of Graph Neural Networks and Prompt Engineering for Advanced AI Applications written by Anand Vemula and published by Anand Vemula. This book was released on with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Graph Prompting" explores the intersection of Graph Neural Networks (GNNs) and prompt engineering, providing a comprehensive guide on leveraging these technologies for advanced AI applications. The book is structured into several key sections, each delving into different aspects of graph-based AI. #### Fundamentals of Graph Theory The book begins by laying the foundation with essential concepts in graph theory, such as nodes, edges, types of graphs, and graph representations. It explains fundamental metrics like degree, centrality, and clustering coefficients, and covers important algorithms for pathfinding and connectivity. #### Introduction to Prompting The next section introduces prompting in AI, particularly for large language models (LLMs). It covers the basics of prompt engineering, types of prompts (instruction-based, task-based), and design principles. Techniques like contextual prompting, chain-of-thought prompting, and few-shot/zero-shot prompting are discussed, providing practical examples and use cases. #### Graph Neural Networks (GNNs) A comprehensive overview of GNNs follows, detailing their architecture and applications. Key models like Graph Convolutional Networks (GCNs), GraphSAGE, and Graph Attention Networks (GATs) are explained with examples. The section also covers advanced GNN models, including transformer-based graph models and attention mechanisms. #### Graph Prompting for LLMs This section focuses on integrating GNNs with LLMs. It explores techniques for using graph embeddings in prompting, enhancing the capabilities of LLMs in various tasks such as recommendation systems, anomaly detection, and question answering. Practical applications and case studies demonstrate the effectiveness of these integrations. #### Ethics and Fairness in Graph Prompting Ethical considerations are crucial, and the book addresses biases in graph data and fairness in graph algorithms. It discusses the ethical implications of using graph data and provides strategies to ensure fairness and mitigate biases. #### Practical Applications and Case Studies The book highlights real-world applications of graph prompting in healthcare, social networks, and recommendation systems. Each case study showcases the practical benefits and challenges of implementing these technologies in different domains. #### Implementation Guides and Tools For practitioners, the book offers step-by-step implementation guides, using popular libraries like PyTorch Geometric and DGL. Example projects provide hands-on experience, helping readers apply the concepts discussed. #### Future Trends and Conclusion The book concludes with a look at future trends in graph prompting, including scalable GNNs, graph-based reinforcement learning, and ethical AI. It encourages continuous exploration and adaptation to leverage the full potential of graph-based AI technologies. Overall, "Graph Prompting" is a detailed and practical guide, offering valuable insights and tools for leveraging GNNs and prompt engineering to advance AI applications across various domains.

Download or read book A Wavelet Tour of Signal Processing written by Stephane Mallat and published by Elsevier. This book was released on 1999-09-14 with total page 663 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended to serve as an invaluable reference for anyone concerned with the application of wavelets to signal processing. It has evolved from material used to teach "wavelet signal processing" courses in electrical engineering departments at Massachusetts Institute of Technology and Tel Aviv University, as well as applied mathematics departments at the Courant Institute of New York University and ÉcolePolytechnique in Paris. - Provides a broad perspective on the principles and applications of transient signal processing with wavelets - Emphasizes intuitive understanding, while providing the mathematical foundations and description of fast algorithms - Numerous examples of real applications to noise removal, deconvolution, audio and image compression, singularity and edge detection, multifractal analysis, and time-varying frequency measurements - Algorithms and numerical examples are implemented in Wavelab, which is a Matlab toolbox freely available over the Internet - Content is accessible on several level of complexity, depending on the individual reader's needs New to the Second Edition - Optical flow calculation and video compression algorithms - Image models with bounded variation functions - Bayes and Minimax theories for signal estimation - 200 pages rewritten and most illustrations redrawn - More problems and topics for a graduate course in wavelet signal processing, in engineering and applied mathematics

Download or read book Introduction to Graph Neural Networks written by Zhiyuan Liu and published by Morgan & Claypool Publishers. This book was released on 2020-03-20 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.

Download or read book Introduction to Graph Neural Networks written by Zhiyuan Zhiyuan Liu and published by Springer Nature. This book was released on 2022-05-31 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool. This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.

Download or read book Network Embedding written by Cheng Yang and published by Morgan & Claypool Publishers. This book was released on 2021-03-25 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL) and the background and rise of network embeddings (NE). It introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions. Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.

Download or read book Representation Learning for Natural Language Processing written by Zhiyuan Liu and published by Springer Nature. This book was released on 2020-07-03 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Social Media Processing written by Feng Wu and published by Springer Nature. This book was released on 2023-11-14 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the 11th Chinese National Conference of Social Media Processing, SMP 2023, held in Anhui, China, in November 2023. The 16 full papers presented were carefully reviewed and selected from 88 submissions. The papers are organized in the topical sections on knowledge representation and reasoning; knowledge acquisition and knowledge base construction; linked data, knowledge integration, and knowledge graph storage management; natural language understanding and semantic computing; knowledge graph applications; knowledge graph open resources.

Download or read book Point of Interest Recommendation in Location Based Social Networks written by Shenglin Zhao and published by Springer. This book was released on 2018-07-13 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically introduces Point-of-interest (POI) recommendations in Location-based Social Networks (LBSNs). Starting with a review of the advances in this area, the book then analyzes user mobility in LBSNs from geographical and temporal perspectives. Further, it demonstrates how to build a state-of-the-art POI recommendation system by incorporating the user behavior analysis. Lastly, the book discusses future research directions in this area. This book is intended for professionals involved in POI recommendation and graduate students working on problems related to location-based services. It is assumed that readers have a basic knowledge of mathematics, as well as some background in recommendation systems.

Download or read book Effective and Efficient Representation Learning for Graph Structures written by Ting Chen and published by . This book was released on 2019 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph structures are a powerful abstraction of many real-world data, such as human interactions and information networks. Despite the powerful abstraction, graphs are challenging to model due to the high-dimensional, irregular and heterogeneous characteristics of many real-world graph data. An essential problem arose is how to effectively and efficiently learn the representation for objects in graphs. In this thesis, both the effectiveness as well as efficiency aspects of the graph representation learning problem are addressed. Specifically, we start by proposing an effective approach for learning heterogeneous graph embedding in an unsupervised setting. Then this is generalized to semi-supervised scenario where label guidance is leveraged. The effective graph representation learning models are followed by efficient techniques, where we propose efficient sampling strategies to improve the training efficiency for content-rich graph embedding models. Finally, to reduce storage and memory cost of the embedding table used in various models, we introduce a framework based on KD code, which can compress the embedding table in an end-to-end fashion. We conduct extensive experiments on various real-world tasks on graph data (e.g. anomaly detection, recommendation and text classifications), and the empirical results validate both effectiveness as well as efficiency of our proposed algorithms.

Download or read book Artificial Neural Networks and Machine Learning ICANN 2023 written by Lazaros Iliadis and published by Springer Nature. This book was released on 2023-09-21 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 10-volume set LNCS 14254-14263 constitutes the proceedings of the 32nd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2023, which took place in Heraklion, Crete, Greece, during September 26–29, 2023. The 426 full papers, 9 short papers and 9 abstract papers included in these proceedings were carefully reviewed and selected from 947 submissions. ICANN is a dual-track conference, featuring tracks in brain inspired computing on the one hand, and machine learning on the other, with strong cross-disciplinary interactions and applications.

Download or read book Knowledge Graphs written by Aidan Hogan and published by Morgan & Claypool Publishers. This book was released on 2021-11-08 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.

Download or read book Database Systems for Advanced Applications written by Christian S. Jensen and published by Springer Nature. This book was released on 2021-04-06 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 12681-12683 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021, held in Taipei, Taiwan, in April 2021. The total of 156 papers presented in this three-volume set was carefully reviewed and selected from 490 submissions. The topic areas for the selected papers include information retrieval, search and recommendation techniques; RDF, knowledge graphs, semantic web, and knowledge management; and spatial, temporal, sequence, and streaming data management, while the dominant keywords are network, recommendation, graph, learning, and model. These topic areas and keywords shed the light on the direction where the research in DASFAA is moving towards. Due to the Corona pandemic this event was held virtually.