The Circular Economy (CE) domain has a nature of connecting and linking multiple cross-industry domains (e.g., manufacturing and materials) aiming to reduce value loss and avoid waste by building and implementing CE models (i.e., circular value networks) across these domains. In recent years, ontologies have been recognized as a key for representing domain knowledge in CE. Both CE-specific and domain-specific ontologies exist, with more continuously emerging. Matching CE-related ontologies can generate alignments that enhance the interoperability and reusability of such ontologies.In this paper, we present our initial efforts and findings in matching ontologies within the CE domain.

As we live in a world of limited resources, the transition from a linear economic model to a circular model is crucial. The Circular Economy (CE) paradigm aims to maintain material continuity through the cycle of production, consumption and recycling. The Digital Product Passport (DPP) is currently recognised as a critical instrument for advancing CE, serving as a comprehensive digital repository for product lifecycle information. The DPP paradigm fosters transparency and traceability. However, so far there is no agreed-upon standard for technically representing and expressing DPPs. This paper aims to provide a comprehensive analysis of the requirements of a general (cross-sectoral) DPP, and to discuss the representation of a core DPP model. We propose to express this in the form of an ontology network, i.e., a formal model serving as a “translation layer” from raw data to interpreted information, along with SHACL shapes for increased data quality and validation. Despite existing research on DPPs, a comprehensive tool enabling this transition into using DPPs is yet to be developed, making this paper a pioneering exploration into the modelling of a DPP core ontology.

Circular Economy has the goal to reduce value loss and avoid waste by extending the life span of materials and products, including circulating materials or product parts before they become waste. Circular economy models (e.g., circular value networks) are typically complex and networked, involving different cross-industry domains. In the context of a circular value network, multiple actors, such as suppliers, manufacturers, recyclers, and product end-users, may be involved. In addition, there may be various flows of resources, energy, information and value throughout the network. This means that we face the challenge that the data and information from cross-industry domains in a circular economy model are not built on common ground, and as a result are difficult to understand and use for both humans and machines. Using ontologies to represent domain knowledge can enable actors and stakeholders from different industries in the circular economy to communicate using a common language. The knowledge domains involved include circular economy, sustainability, materials, products, manufacturing, and logistics. The objective of this paper is to investigate the landscape of current ontologies for these domains. This will enable us to in the future explore what existing knowledge can be adapted or used to develop ontologies for circular value networks.

Circular Economy (CE) aims to reduce value loss and avoid waste by extending the life of products,components, and materials. Circular value networks (CVN), i.e. networks of actors realising partsof the CE, are often complex and involving a multitude of actors, such as suppliers, manufacturers,recyclers, and end-users, from different industry sectors. In addition, the networks enable and managevarious flows of resources, energy, information and value. To set up and operate such networks, datasharing is essential, however, one of the main challenges is semantic interoperability, and as a resultdata are difficult to understand, integrate, and use. Ontologies support semantic interoperability, andcan represent domain knowledge and enable stakeholders to communicate. However, the knowledgedomains involved are many, including sustainability, materials, products, manufacturing, and logistics,where well-established ontologies already exist. In addition, these domains need to be connected torelevant industry sectors. In order to bridge these domains we propose a set of core ontology modules,allowing to express links between existing ontologies as well as filling gaps related to core CE concepts.

Circular economy aims at reducing value loss and avoiding waste, by circulating material or productparts before they become waste. Today, lack of support for sharing data in a secure, quality assured, andautomated way is one of the main obstacles that industry actors point to when attempting to create newcircular value networks. Together with using different terminologies and not having explicit definitions ofthe concepts that appear in data, this makes it very difficult to create new ecosystems of actors in Europetoday. A solution to these challenges needs to leverage open standards for semantic data interoperabilityin establishing a shared vocabulary (ontology network) for data documentation, as well as create adecentralized digital platform that enables collaboration in a secure and confidentiality-preservingmanner. This vocabulary can then be used to construct digital twins of circular value networks to furtherenable open collaboration. Once defined, the blueprints of these digital twins will be reusable as templatesand can be reused with a different set of actors, or used within a different industry domain. This visionincludes a number of open research problems, including the development of ontologies that need to modela wide range of different materials and products, not only providing vertical interoperability but alsohorizontal interoperability, for cross-industry value networks. As well as transdisciplinary research onmethods to find, analyse and assess new circular value chain configurations, and form their decentralizeddigital twins. The solutions will allow for automation of planning, management, and execution of circularvalue networks, at a European scale, and beyond. Thereby supporting the acceleration of the digitaland green transitions, automating the discovery and formation of new collaborations in the circulareconomy.

This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale.

The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve.

This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.

RDF Stream Processing (RSP) has been proposed as a way of bridging the gap between the Complex Event Processing (CEP) paradigm and the Semantic Web standards. Uncertainty has been recognized as a critical aspect in CEP, but it has received little attention within the context of RSP. In this paper, we investigate the impact of different RSP optimization strategies for uncertainty management. The paper describes (1) an extension of the RSP-QL* data model to capture bind expressions, filter expressions, and uncertainty functions; (2) optimization techniques related to lazy variables and caching of uncertainty functions, and a heuristic for reordering uncertainty filters in query plans; and (3) an evaluation of these strategies in a prototype implementation. The results show that using a lazy variable mechanism for uncertainty functions can improve query execution performance by orders of magnitude while introducing negligible overhead. The results also show that caching uncertainty function results can improve performance under most conditions, but that maintaining this cache can potentially add overhead to the overall query execution process. Finally, the effect of the proposed heuristic on query execution performance was shown to depend on multiple factors, including the selectivity of uncertainty filters, the size of intermediate results, and the cost associated with the evaluation of the uncertainty functions.

RDF Stream Processing (RSP) has been proposed as a candidate for bringing together the Complex Event Processing (CEP) paradigm and the Semantic Web standards. In this paper, we investigate the impact of explicitly representing and processing uncertainty in RSP for the use in CEP. Additionally, we provide a representation for capturing the relevant notions of uncertainty in the RSP-QL* data model and describe query functions that can operate on this representation. The impact evaluation is based on a use case within electronic healthcare, where we compare the query execution overhead of different uncertainty options in a prototype implementation. The experiments show that the influence on query execution performance varies greatly, but that uncertainty can have noticeable impact on query execution performance. On the otherhand, the overhead grows linearly with respect to the stream rate for all uncertainty options in the evaluation, and the observed performance is sufficient for many use cases. Extending the representation and operations to support more uncertainty options and investigating different query optimization strategies to reduce the impact on execution performance remain important areas for future research.

In the era of data-driven science, corpus-based language technology is an essential part of cyber physical systems. In this chapter, the authors describe the design and the development of an extensible domain-specific web corpus to be used in a distributed social application for the care of the elderly at home. The domain of interest is the medical field of chronic diseases. The corpus is conceived as a flexible and extensible textual resource, where additional documents and additional languages will be appended over time. The main purpose of the corpus is to be used for building and training language technology applications for the “layfication” of the specialized medical jargon. “Layfication” refers to the automatic identification of more intuitive linguistic expressions that can help laypeople (e.g., patients, family caregivers, and home care aides) understand medical terms, which often appear opaque. Exploratory experiments are presented and discussed.

Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.