This is both a work-immanent analysis of Lun dao, and an introduction to Jin’s thought. It begins with the problem of induction, which is the study’s central theme, and proceeds to outline Jin’s ontological response. In addition, it also considers his epistemological response to the problem.

Ontology was once understood to be the philosophical inquiry into the structure of reality: the analysis and categorization of ‘what there is’. Recently, however, a field called ‘ontology’ has become part of the rapidly growing research industry in information technology. The two fields have more in common than just their name. Theory and Applications of Ontology is a two-volume anthology that aims to further an informed discussion about the relationship between ontology in philosophy and ontology in information technology. It fills an important lacuna in cutting-edge research on ontology in both fields, supplying stage-setting overview articles on history and method, presenting directions of current research in either field, and highlighting areas of productive interdisciplinary contact. Theory and Applications of Ontology: Philosophical Perspectives presents ontology in philosophy in ways that computer scientists are not likely to find elsewhere. The volume offers an overview of current research traditions in ontology, contrasting analytical, phenomenological, and hermeneutic approaches. It introduces the reader to current philosophical research on those categories of everyday and scientific reasoning that are most relevant to present and future research in information technology.

The implications for philosophy and cognitive science of developments in statistical learning theory. In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni—a philosopher and an engineer—argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors—a central topic in SLT. After discussing philosophical attempts to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis (VC) dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT.

This book constitutes the refereed proceedings of the 18th International Conference on Inductive Logic Programming, ILP 2008, held in Prague, Czech Republic, in September 2008. The 20 revised full papers presented together with the abstracts of 5 invited lectures were carefully reviewed and selected during two rounds of reviewing and improvement from 46 initial submissions. All current topics in inductive logic programming are covered, ranging from theoretical and methodological issues to advanced applications. The papers present original results in the first-order logic representation framework, explore novel logic induction frameworks, and address also new areas such as statistical relational learning, graph mining, or the semantic Web.

Perspectives on Ontology Learning brings together researchers and practitioners from different communities − natural language processing, machine learning, and the semantic web − in order to give an interdisciplinary overview of recent advances in ontology learning. Starting with a comprehensive introduction to the theoretical foundations of ontology learning methods, the edited volume presents the state-of-the-start in automated knowledge acquisition and maintenance. It outlines future challenges in this area with a special focus on technologies suitable for pushing the boundaries beyond the creation of simple taxonomical structures, as well as on problems specifically related to knowledge modeling and representation using the Web Ontology Language. Perspectives on Ontology Learning is designed for researchers in the field of semantic technologies and developers of knowledge-based applications. It covers various aspects of ontology learning including ontology quality, user interaction, scalability, knowledge acquisition from heterogeneous sources, as well as the integration with ontology engineering methodologies.

Resorting to natural law is one way of conveying the philosophical conviction that moral norms are not merely conventional rules. Accordingly, the notion of natural law has a clear metaphysical dimension, since it involves the recognition that human beings do not conceive themselves as sheer products of society and history. And yet, if natural law is to be considered the fundamental law of practical reason, it must show also some intrinsic relationship to history and positive law. The essays in this book examine this tension between the metaphysical and the practical and how the philosophical elaboration of natural law presents this notion as a "limiting-concept", between metaphysics and ethics, between the mutable and the immutable; between is and ought, and, in connection with the latter, even the tension between politics and eschatology as a double horizon of ethics. This book, contributed to by scholars from Europe and America, is a major contribution to the renewed interest in natural law. It provides the reader with a comprehensive overview of natural law, both from a historical and a systematic point of view. It ranges from the mediaeval synthesis of Aquinas through the early modern elaborations of natural law, up to current discussions on the very possibility and practical relevance of natural law theory for the contemporary mind.