Knowledge Representation And Reasoning With Deep Neural Networks
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Knowledge Representation and Reasoning with Deep Neural Networks
Knowledge representation and reasoning is one of the central challenges of artificial intelligence, and has important implications in many fields including natural language understanding and robotics. Representing knowledge with symbols, and reasoning via search and logic has been the dominant paradigm for many decades. In this work, we use deep neural networks to learn to both represent symbols and perform reasoning end-to-end from data. By learning powerful non-linear models, our approach generalizes to massive amounts of knowledge and works well with messy real-world data using minimal human effort. First, we show that recurrent neural networks with an attention mechanism achieve state-of-the-art reasoning on a large structured knowledge graph. Next, we develop Neural Programmer, a neural network augmented with discrete operations that can be learned to induce latent programs with backpropagation. We apply Neural Programmer to induce short programs on two datasets: a synthetic dataset requiring arithmetic and logic reasoning, and a natural language question answering dataset that requires reasoning on semi-structured Wikipedia tables. We present what is to our awareness the first weakly supervised, end-to-end neural network model to induce such programs on a real-world dataset. Unlike previous learning approaches to program induction, the model does not require domain-specific grammars, rules, or annotations. Finally, we discuss methods to scale Neural Programmer training to large databases.
Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications
Author: Management Association, Information Resources
language: en
Publisher: IGI Global
Release Date: 2019-10-11
Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.
MDATA Cognitive Model: Theory and Applications
This book presents the theoretical foundations of the MDATA cognitive model and its applications in the field of cybersecurity. The MDATA model is an innovative analytical tool designed to simulate and improve cognitive processes. It bridges cognitive science and cybersecurity, making it essential for professionals and researchers in these fields. The core content explores three critical technologies within the MDATA model: knowledge representation, knowledge acquisition, and knowledge application. Each section provides in-depth technical analysis and practical applications, enabling readers to grasp the structural and operational principles of the model. With clear implementation strategies, the book equips readers to apply the MDATA model in real-world scenarios. Through detailed case studies, the book demonstrates how the MDATA model enhances the identification and resolution of cybersecurity threats. Applications include network attack analysis, open-source intelligence, public sentiment monitoring, and cybersecurity assessments. Readers will gain a powerful tool for navigating complex cybersecurity incidents, making this book an indispensable resource for cybersecurity professionals, AI researchers, and data analysts. A foundational understanding of cybersecurity and cognitive science is recommended.