This book constitutes the refereed proceedings of the 9th International RuleML Symposium, RuleML 2015, held in Berlin, Germany, in August 2015. The 25 full papers, 4 short papers, 2 full keynote papers, 2 invited research track overview papers, 1 invited paper, 1 invited abstracts presented were carefully reviewed and selected from 63 submissions. The papers cover the following topics: general RuleML track; complex event processing track, existential rules and datalog+/- track; legal rules and reasoning track; rule learning track; industry track.

This book constitutes the refereed proceedings of the 9th International RuleML Symposium, RuleML 2015, held in Berlin, Germany, in August 2015. The 25 full papers, 4 short papers, 2 full keynote papers, 2 invited research track overview papers, 1 invited paper, 1 invited abstracts presented were carefully reviewed and selected from 63 submissions. The papers cover the following topics: general RuleML track; complex event processing track, existential rules and datalog+/- track; legal rules and reasoning track; rule learning track; industry track.

As the world enters the era of big data, there is a serious need to give a semantic perspective to the data in order to find unseen patterns, derive meaningful information, and make intelligent decisions. Semantic technologies offer the richest machine-interpretable (rather than just machine-processable) and explicit semantics that are being extensively used in various domains and industries. These technologies reduce the problem of large semantic loss in the process of modelling knowledge, and provide sharable, reusable knowledge,and a common understanding of the knowledge. As a result, the interoperability and interconnectivity of the model make it priceless for addressing the issues of querying data. These technologies work with the concepts and relations that are very lose to the working of the human brain. They provide a semantic representation of any data format: unstructured or semi-structured. As a consequence, data becomes real-world entity rather than a string of characters. For these reasons, semantic technologies are highly valuable tools to simplify the existing problems of the industry leading to new opportunities. However, there are some challenges that need to be addressed to make industrial applications and machines smarter. This book aims to provide a roadmap for semantic technologies and highlights the role of these technologies in industry. The book also explores the present and future prospects of these semantic technologies along with providing answers to various questions like: Are semantic technologies useful for the next era (industry 4.0)? Why are semantic technologies so popular and extensively used in the industry? Can semantic technologies make intelligent industrial applications? Which type of problem requires the immediate attention of researchers? Why are semantic technologies very helpful in people’s future lives? This book will potentially serve as an important guide towards the latest industrial applications of semantic technologies for the upcoming generation, and thus becomes a unique resource for scholars, researchers, professionals and practitioners in the field.

This volume contains the lecture notes of the 11th Reasoning Web Summer School 2015, held in Berlin, Germany, in July/August 2015. In 2015, the theme of the school was Web Logic Rules. This Summer School is devoted to this perspective, and provides insight into the semantic Web, linked data, ontologies, rules, and logic.

Computational cognitive modeling explores cognition by building computational models for cognitive processes, mechanisms and representations. Currently, implementations of cognitive models lack a formal foundation. This inhibits analysis. In this thesis, the cognitive architecture Adaptive Control of Thought - Rational (ACT-R) is formalized and embedded into the rule-based programming language Constraint Handling Rules (CHR). The powerful analytical methods of CHR, particularly confluence analysis, are extended by reasoning modulo equivalence relations. The results are applied to the domain of cognitive modeling.

This book is a timely and critical introduction for those interested in what data science is (and isn’t), and how it should be applied. The language is conversational and the content is accessible for readers without a quantitative or computational background; but, at the same time, it is also a practical overview of the field for the more technical readers. The overarching goal is to demystify the field and teach the reader how to develop an analytical mindset instead of following recipes. The book takes the scientist’s approach of focusing on asking the right question at every step as this is the single most important factor contributing to the success of a data science project. Upon finishing this book, the reader should be asking more questions than I have answered. This book is, therefore, a practising scientist’s approach to explaining data science through questions and examples.

Ontologies are now increasingly used to integrate, and organize data and knowledge, particularly in data and knowledge-intensive applications in both research and industry. The book is devoted to semantic data mining – a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies and knowledge graphs, rather than only purely empirical data. The introductory chapters of the book provide theoretical foundations of both data mining and ontology representation. Taking a unified perspective, the book then covers several methods for semantic data mining, addressing tasks such as pattern mining, classification and similarity-based approaches. It attempts to provide state-of-the-art answers to specific challenges and peculiarities of data mining with use of ontologies, in particular: How to deal with incompleteness of knowledge and the so-called Open World Assumption? What is a truly “semantic” similarity measure? The book contains several chapters with examples of applications of semantic data mining. The examples start from a scenario with moderate use of lightweight ontologies for knowledge graph enrichment and end with a full-fledged scenario of an intelligent knowledge discovery assistant using complex domain ontologies for meta-mining, i.e., an ontology-based meta-learning approach to full data mining processes. The book is intended for researchers in the fields of semantic technologies, knowledge engineering, data science, and data mining, and developers of knowledge-based systems and applications.