Ontology Engineering With Ontology Design Patterns Foundations And Applications

The use of ontologies for data and knowledge organization has become ubiquitous in many data-intensive and knowledge-driven application areas, in science, industry, and the humanities. At the same time, ontology engineering best practices continue to evolve. In particular, modular ontology modeling based on ontology design patterns is establishing itself as an approach for creating versatile and extendable ontologies for data management and integration. This book is the very first comprehensive treatment of Ontology Engineering with Ontology Design Patterns. It contains both advanced and introductory material accessible for readers with only a minimal background in ontology modeling. Some introductory material is written in the style of tutorials, and specific chapters are devoted to examples and to applications. Other chapters convey the state of the art in research regarding ontology design patterns. The editors and the contributing authors include the leading contributors to the development of ontology-design-pattern-driven ontology engineering.

Ontologies are the corner stone of data modeling and knowledge representation, and engineering an ontology is a complex task in which domain knowledge, ontological accuracy and computational properties need to be carefully balanced. As with any engineering task, the identification and documentation of common patterns is important, and Ontology Design Patterns (ODPs) provide ontology designers with a strong connection to requirements and a better communication of their semantic content and intent. This book, Advances in Pattern-Based Ontology Engineering, contains 23 extended versions of selected papers presented at the annual Workshop on Ontology Design and Patterns (WOP) between 2017 and 2020. This yearly event, which attracts a large number of researchers and professionals in the field of ontology engineering and ontology design patterns, covers issues related to quality aspects of ontology engineering and ODPs for data and knowledge representation, and is usually co-located with the International Semantic Web Conference (ISWC), apart from WOP 2020, which was held virtually due to the COVID-19 pandemic. Topics covered by the papers collected here focus on recent advances in ontology design and patterns, and range from a method to instantiate content patterns, through a proposal on how to document a content pattern, to a number of patterns emerging in ontology modeling in various situations and applications. The book provides an overview of important advances in ontology engineering and ontology design patterns, and will be of interest to all those working in the field.

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.

Ontologies are formal knowledge models that describe concepts and relationships and enable data integration, information search, and reasoning. Ontology Design Patterns (ODPs) are reusable solutions intended to simplify ontology development and support the use of semantic technologies by ontology engineers. ODPs document and package good modelling practices for reuse, ideally enabling inexperienced ontologists to construct high-quality ontologies. Although ODPs are already used for development, there are still remaining challenges that have not been addressed in the literature. These research gaps include a lack of knowledge about (1) which ODP features are important for ontology engineering, (2) less experienced developers' preferences and barriers for employing ODP tooling, and (3) the suitability of the eXtreme Design (XD) ODP usage methodology in non-academic contexts. This dissertation aims to close these gaps by combining quantitative and qualitative methods, primarily based on five ontology engineering projects involving inexperienced ontologists. A series of ontology engineering workshops and surveys provided data about developer preferences regarding ODP features, ODP usage methodology, and ODP tooling needs. Other data sources are ontologies and ODPs published on the web, which have been studied in detail. To evaluate tooling improvements, experimental approaches provide data from comparison of new tools and techniques against established alternatives. The analysis of the gathered data resulted in a set of measurable quality indicators that cover aspects of ODP documentation, formal representation or axiomatisation, and usage by ontologists. These indicators highlight quality trade-offs: for instance, between ODP Learnability and Reusability, or between Functional Suitability and Performance Efficiency. Furthermore, the results demonstrate a need for ODP tools that support three novel property specialisation strategies, and highlight the preference of inexperienced developers for template-based ODP instantiation---neither of which are supported in prior tooling. The studies also resulted in improvements to ODP search engines based on ODP-specific attributes. Finally, the analysis shows that XD should include guidance for the developer roles and responsibilities in ontology engineering projects, suggestions on how to reuse existing ontology resources, and approaches for adapting XD to project-specific contexts.

In recent years, knowledge graphs (KGs) and ontologies have been widely adopted for modeling many kinds of domain. They are frequently released openly, something which benefits those who are starting new projects, because it offers them a wide choice of ontology reuse and the possibility to link to existing data. Understanding the content of an ontology or a knowledge graph is far from straightforward, however, and existing methods address this issue only partially, while exploring and comparing multiple ontologies can be a tedious manual task. This book, Empirical Ontology Design Patterns, starts from the premise that identifying the Ontology Design Patterns (ODPs) used in an ontology or a knowledge graph will go some way to addressing this problem. Its main focus is to provide tools which will effectively support the task of automatically identifying ODPs in existing ontologies and knowledge graphs. The book analyses the role of ODPs in ontology engineering, placing this analysis in the wider context of existing approaches to ontology reuse and implementation. It introduces a novel method for extracting empirical ontology design patterns (EODPs) from ontologies, and another for extracting EODPs from knowledge graphs whose schemas are implicit. Both methods are applied to ontologies and knowledge graphs frequently adopted and reused, such as Wikidata. The book also offers an ontology which can be used as a basis for annotating ODPs in ontologies and knowledge graphs, whether manually or automatically. The book will be of interest to all those whose work involves the use or reuse of ontologies and knowledge graphs.

The Semantic Web is characterized by the existence of a very large number of distributed semantic resources, which together define a network of ontologies. These ontologies in turn are interlinked through a variety of different meta-relationships such as versioning, inclusion, and many more. This scenario is radically different from the relatively narrow contexts in which ontologies have been traditionally developed and applied, and thus calls for new methods and tools to effectively support the development of novel network-oriented semantic applications. This book by Suárez-Figueroa et al. provides the necessary methodological and technological support for the development and use of ontology networks, which ontology developers need in this distributed environment. After an introduction, in its second part the authors describe the NeOn Methodology framework. The book’s third part details the key activities relevant to the ontology engineering life cycle. For each activity, a general introduction, methodological guidelines, and practical examples are provided. The fourth part then presents a detailed overview of the NeOn Toolkit and its plug-ins. Lastly, case studies from the pharmaceutical and the fishery domain round out the work. The book primarily addresses two main audiences: students (and their lecturers) who need a textbook for advanced undergraduate or graduate courses on ontology engineering, and practitioners who need to develop ontologies in particular or Semantic Web-based applications in general. Its educational value is maximized by its structured approach to explaining guidelines and combining them with case studies and numerous examples. The description of the open source NeOn Toolkit provides an additional asset, as it allows readers to easily evaluate and apply the ideas presented.

Semantic Web technologies enable people to create data stores on the Web, build vocabularies, and write rules for handling data. They have been in use for several years now, and knowledge extraction and knowledge discovery are two key aspects investigated in a number of research fields which can potentially benefit from the application of semantic web technologies, and specifically from the development and reuse of ontologies. This book, Applications and Practices in Ontology Design, Extraction, and Reasoning, has as its main goal the provision of an overview of application fields for semantic web technologies. In particular, it investigates how state-of-the-art formal languages, models, methods, and applications of semantic web technologies reframe research questions and approaches in a number of research fields. The book also aims to showcase practical tools and background knowledge for the building and querying of ontologies. The first part of the book presents the state-of-the-art of ontology design, applications and practices in a number of communities, and in doing so it provides an overview of the latest approaches and techniques for building and reusing ontologies according to domain-dependent and independent requirements. Once the data is represented according to ontologies, it is important to be able to query and reason about them, also in the presence of uncertainty, vagueness and probabilities. The second part of the book covers some of the latest advances in the fields of ontology, semantics and reasoning, without losing sight of the book’s practical goals.

Ontologies have become increasingly important as the use of knowledge graphs, machine learning, natural language processing (NLP), and the amount of data generated on a daily basis has exploded. As of 2014, 90% of the data in the digital universe was generated in the two years prior, and the volume of data was projected to grow from 3.2 zettabytes to 40 zettabytes in the next six years. The very real issues that government, research, and commercial organizations are facing in order to sift through this amount of information to support decision-making alone mandate increasing automation. Yet, the data profiling, NLP, and learning algorithms that are ground-zero for data integration, manipulation, and search provide less than satisfactory results unless they utilize terms with unambiguous semantics, such as those found in ontologies and well-formed rule sets. Ontologies can provide a rich "schema" for the knowledge graphs underlying these technologies as well as the terminological and semantic basis for dramatic improvements in results. Many ontology projects fail, however, due at least in part to a lack of discipline in the development process. This book, motivated by the Ontology 101 tutorial given for many years at what was originally the Semantic Technology Conference (SemTech) and then later from a semester-long university class, is designed to provide the foundations for ontology engineering. The book can serve as a course textbook or a primer for all those interested in ontologies.