Agnieszka Lawrynowicz – författare

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.

The study of patterns in the context of ontology engineering for the semantic web was pioneered more than a decade ago by Blomqvist, Sandkuhl and Gangemi. Since then, this line of research has flourished and led to the development of ontology design patterns, knowledge patterns, and linked data patterns: the patterns as they are known by ontology designers, knowledge engineers, and linked data publishers, respectively. A key characteristic of those patterns is that they are modular and reusable solutions to recurrent problems in ontology engineering and linked data publishing.This book contains recent contributions which advance the state of the art on theory and use of ontology design patterns. The papers collected in this book cover a range of topics, from a method to instantiate content patterns, a proposal on how to document a content pattern, to a number of patterns emerging in ontology modeling in various situations.

This book constitutes the thoroughly refereed post-workshop proceedings of the 12th OWL: Experiences and Directions Workshop, OWLED 2015, held in Bethlehem, PA, USA, in October 2015, co-located with ISWC 2015, the International Semantic Web Conference.The 18 revised papers presented were carefully reviewed and selected from 35 initial submissions. Bridging the gap between ontology engineering practices and software engineering, the papers describe reuse methods employed throughout the ontology development cycle; modeling / terminological decisions, alignment and comparison between ontologies, how ontologies are stored, versioned, distributed, and consumed over the Web.

This book constitutes the thoroughly refereed post-conference proceedings of the Satellite Events of the 14th European Conference on the Semantic Web, ESWC 2017, held in Portoroz, Slovenia, in May/June2017.The volume contains 8 poster and 24 demonstration papers, selected from 105 submissions. Additionally, this book includes a selection of 13 best workshop papers. The papers cover various aspects of the semantic web.The chapter 'Scholia, Scientometrics and Wikidata' is available open access under a CC BY 4.0 license via link.springer.com.