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Graph-based relational learning has been the focus of relational learning for quite some time. As most of the real-world data is structured, and hence cannot be represented in a single table, various logic-based and graph-based techniques have been proposed for dealing with structured data. Our goal is to perform an in-depth analysis of two such graph-based learning systems. We have selected Subdue to represent the search-based approach and support vector machine (SVM) with graph kernels to represent the kernel-based approach. We perform a comparison between search-based and kernel-based approaches and evaluate their performance in various domains. A search-based approach to learning typically involves a search through a larger hypotheses space. The main concern of a search-based learning system is to search through the hypothesis space efficiently. Kernel-based approaches on the other hand do not involve generation and search of a hypotheses space. Instead, a kernel-based system maps the given input space to a higher-dimensional space to perform linear classification. (Abstract shortened by UMI.).
We perform an experimental comparison of the graph-based multi-relational data mining system, Subdue, and the inductive logic programming system, CProgol, on the Mutagenesis dataset and various artificially generated Bongard problems. Experimental results indicate that Subdue can significantly outperform CProgol while discovering structurally large multi-relational concepts. It is also observed that CProgol is better at learning semantically complicated concepts and it tends to use background knowledge more effectively than Subdue. An analysis of the results indicates that the differences in the performance of the systems are a result of the difference in the expressiveness of the logic-based and the graph-based representations. The ability of graph-based systems to learn structurally large concepts comes from the use of a weaker representation whose expressiveness is intermediate between propositional and first-order logic. The use of this weaker representation is advantageous while learning structurally large concepts but it limits the learning of semantically complicated concepts and the utilization background knowledge.
The Twelfth International Conference on Inductive Logic Programming was held in Sydney, Australia, July 9–11, 2002. The conference was colocated with two other events, the Nineteenth International Conference on Machine Learning (ICML2002) and the Fifteenth Annual Conference on Computational Learning Theory (COLT2002). Startedin1991,InductiveLogicProgrammingistheleadingannualforumfor researchers working in Inductive Logic Programming and Relational Learning. Continuing a series of international conferences devoted to Inductive Logic Programming and Relational Learning, ILP 2002 was the central event in 2002 for researchers interested in learning relational knowledge from examples. The Program Committee, following a resolution of the Community Me- ing in Strasbourg in September 2001, took upon itself the issue of the possible change of the name of the conference. Following an extended e-mail discussion, a number of proposed names were subjected to a vote. In the ?rst stage of the vote, two names were retained for the second vote. The two names were: Ind- tive Logic Programming, and Relational Learning. It had been decided that a 60% vote would be needed to change the name; the result of the vote was 57% in favor of the name Relational Learning. Consequently, the name Inductive Logic Programming was kept.
The goal of this paper is to generate insights about the differences between graph-based and logic-based approaches to multi-relational data mining by performing a case study of the graph-based system, Subdue and the inductive logic programming system, CProgol. We identify three key factors for comparing graph-based and logic-based multi-relational data mining; namely, the ability to discover structurally large concepts, the ability to discover semantically complicated concepts and the ability to effectively utilize background knowledge. We perform an experimental comparison of Subdue and CProgol on the Mutagenesis domain and various artificially generated Bongard problems. Experimental results indicate that Subdue can significantly outperform CProgol while discovering structurally large multi-relational concepts. It is also observed that CProgol is better at learning semantically complicated concepts and it tends to use background knowledge more effectively than Subdue.
This volume contains contributions from participants in the 2007 International Multiconference of Engineers and Computer Scientists. It covers a variety of subjects in the frontiers of intelligent systems and computer engineering and their industrial applications. The book offers up-to-date information on advances in intelligent systems and computer engineering and also serves as an excellent reference work for researchers and graduate students working in the field.
Ever since the early days of machine learning and data mining, it has been realized that the traditional attribute-value and item-set representations are too limited for many practical applications in domains such as chemistry, biology, network analysis and text mining. This has triggered a lot of research on mining and learning within alternative and more expressive representation formalisms such as computational logic, relational algebra, graphs, trees and sequences. The motivation for using graphs, trees and sequences. Is that they are 1) more expressive than flat representations, and 2) potentially more efficient than multi-relational learning and mining techniques. At the same time, the data structures of graphs, trees and sequences are among the best understood and most widely applied representations within computer science. Thus these representations offer ideal opportunities for developing interesting contributions in data mining and machine learning that are both theoretically well-founded and widely applicable. The goal of this book is to collect recent outstanding studies on mining and learning within graphs, trees and sequences in studies worldwide.
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.
The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This, the sixth issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains eight extended and revised versions of papers selected from those presented at DEXA 2011. Topics covered include skyline queries, probabilistic logics and reasoning, theory of conceptual modeling, prediction in networks of moving objects, validation of XML integrity constraints, management of loosely structured multi-dimensional data, data discovery in the presence of annotations, and quality ranking for Web articles.