The transition towards a circular economy (CE) will require data sharing across different platforms and data spaces of parties operating in a variety of supply chains. From a circular economy compliance monitoring perspective, beyond the access to mandatory data that governments will receive, authorities may benefit from accessing additional business data from the source on a voluntary basis, which is challenging. While platforms and data spaces solve a great deal of complexity and interoperability within their realm, platform, and data space interoperability is still challenging. In the logistics domain, efforts have been made to overcome these issues of data sharing across logistics platforms with a Semantic data sharing architecture developed by the CEF FEDeRATED Action, at the heart of which is a semantic model aligning other semantic models for logistics. In this paper, we take the Semantic data sharing architecture as a point of departure and examine the opportunities and limitations that it has for CE monitoring, and how it relates to other developments in the EU and beyond. Many of these developments acknowledge the need for data access across heterogeneous systems and – processes of actors; others add security and trust to data sharing that goes all the way to the level to cover legal obligations. The goal of this paper is to gain further insights into how data sharing across multiple platforms and data spaces enables circular economy monitoring, where government organizations would need to address the issue of how they would interface with, and access data that resides in multiple platforms and data spaces. We found that the various models can be aligned on some architecture principles that promote interoperability across dimensions (e.g. federation, keeping data at the source), yet they still differ on other dimensions (e.g. data model and semantics, as well as how they address issues of identification, authentication and authorization). We suggest further efforts towards developing meta-level agreements and standardization for data space interoperability and we propose further research directions on that topic.@en

To access business data for compliance monitoring of the circular economy (CE), governments would need to deal with issues of both legislative complexities arising from many new regulations in the area of CE and sustainability, as well as the digital complexity for accessing business data that resides in different business systems and data spaces. While earlier research has touched upon (1) the legal complexity through the identification of common high-level concepts of what to monitor, and (2) the digital complexities through the use of upper ontologies, so far these aspects have been treated to a large extent in isolation and not been linked systematically. In this research in progress paper, we propose an approach on how to link the two, discuss advances in the area and limitations, and identify areas that need to be addressed to allow governments to tap into the rich business data sources for compliance monitoring in the future.@en

With the rise of data analytics use in government, government organizations are starting to explore the possibilities of using business data to create further public value. This process, however, is far from straightforward: key questions that governments need to address relate to the quality of this external data and the value it brings. In the domain of global trade, customs administrations are responsible on the one hand to control trade for safety and security and duty collection and on the other hand they need to facilitate trade and not hinder economic activities. With the increased trade volumes, also due to growth in eCommerce, customs administrations have turned their attention to the use of data analytics to support their risk management processes. Beyond the internal customs data sources, customs is starting to explore the value of business data provided by business infrastructures and platforms. While these external data sources seem to hold valuable information for customs, data quality of the external data sources, as well as the value they bring to customs need to be well understood. Building on a case study conducted in the context of the PROFILE research project, this contribution reports the findings on data quality and data linking of ENS customs data with external data (BigDataMari) and other customs (import declaration) data and we discuss specific lessons learned and recommendations for practice. In addition, we also develop a data quality and data value evaluation framework applied to customs as high-level framework to help data users to evaluate potential value of external data sources. From a theoretical perspective this paper further extends earlier research on value of data analytics for government supervision, by zooming on data quality.@en

https://cordis.europa.eu/project/id/786748/results https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5f1716216&appId=PPGMS @en

Governance requirements for systems supporting information sharing be-tween businesses and government organisations (B&G) are determined by a high variety of stakeholders with often conflicting interests. These conflict-ing interests can hamper the introduction and scaling-up of ICT-innovations that change their roles and authorities. We address one such innovation: the introduction of blockchain technologies in the B&G context. Who can gov-ern data and the system depends on several elements of the design of a blockchain-based system, particularly the data structure, consensus mecha-nism and network topology. Design choices regarding these elements affect who can make decisions and hence we call them blockchain control points. These control points require an explicit and well-understood relationship be-tween the design decisions and the interests of stakeholders. Yet, the litera-ture on blockchain technology and governance does not offer such insight. Therefore, we developed a framework to assess the alignment between stakeholders interest and blockchain design choices. This framework consists of three views and their interrelationships, 1) a stakeholder view providing insight into the tensions between stakeholder’s interests and governance re-quirements, 2), a governance view on the rights concerning the data and the system, and 3) a blockchain control view describing how design decisions on the control points affect whether governance requirements are met and how parties can exercise their rights. Making these links explicit enables an un-derstanding of how technical design choices can trigger organizational dy-namics from the stakeholder view and vice versa. Based on the framework we formulate a research agenda concerning blockchain design choices and governance.@en

Ontology alignment is a fundamental task to reconcile the heterogeneity among various information systems using distinct information sources. The evolutionary algorithms (EAs) have been already considered as the primary strategy to develop an ontology alignment system. However, such systems have two significant drawbacks: they either need a ground truth that is often unavailable, or they utilize the population-based EAs in a way that they require massive computation and memory. This article presents a new ontology alignment system, called SANOM, which uses the well-known simulated annealing as the principal technique to find the mappings between two given ontologies while no ground truth is available. In contrast to population-based EAs, the simulated annealing need not generate populations, which makes it significantly swift and memory-efficient for the ontology alignment problem. This article models the ontology alignment problem as optimizing the fitness of a state whose optimum is obtained by using the simulated annealing. A complex fitness function is developed that takes advantage of various similarity metrics including string, linguistic, and structural similarities. A randomized warm initialization is specially tailored for the simulated annealing to expedite its convergence. The experiments illustrate that SANOM is competitive with the state-of-the-art and is significantly superior to other EA-based systems.

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Simulated annealing-based ontology matching (SANOM) participates for the second time at the ontology alignment evaluation initiative (OAEI) 2019. This paper contains the configuration of SANOM and its results on the anatomy and conference tracks. In comparison to the OAEI 2017, SANOM has improved significantly, and its results are competitive with the state-of-the-art systems. In particular, SANOM has the highest recall rate among the participated systems in the conference track, and is competitive with AML, the best performing system, in terms of F-measure. SANOM is also competitive with LogMap on the anatomy track, which is the best performing system in this track with no usage of particular biomedical background knowledge. SANOM has been adapted to the HOBBIT platfrom and is now available for the registered users. abstract environment.

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The l₁-regularized least square problem has been considered in diverse fields. However, finding its solution is exacting as its objective function is not differentiable. In this paper, we propose a new one-layer neural network to find the optimal solution of the l₁-regularized least squares problem. To solve the problem, we first convert it into a smooth quadratic minimization by splitting the desired variable into its positive and negative parts. Accordingly, a novel neural network is proposed to solve the resulting problem, which is guaranteed to converge to the solution of the problem. Furthermore, the rate of the convergence is dependent on a scaling parameter, not to the size of datasets. The proposed neural network is further adjusted to encompass the total variation regularization. Extensive experiments on the l₁ and total variation regularized problems illustrate the reasonable performance of the proposed neural network.

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Ontology alignment is widely used to find the correspondences between different ontologies in diverse fields. After discovering the alignments, several performance scores are available to evaluate them. The scores typically require the identified alignment and a reference containing the underlying actual correspondences of the given ontologies. The current trend in the alignment evaluation is to put forward a new score (e.g., precision, weighted precision, semantic precision, etc.) and to compare various alignments by juxtaposing the obtained scores. However, it is substantially provocative to select one measure among others for comparison. On top of that, claiming if one system has a better performance than one another cannot be substantiated solely by comparing two scalars. In this article, we propose the statistical procedures that enable us to theoretically favor one system over one another. The McNemar's test is the statistical means by which the comparison of two ontology alignment systems over one matching task is drawn. The test applies to a 2 × 2 contingency table, which can be constructed in two different ways based on the alignments, each of which has their own merits/pitfalls. The ways of the contingency table construction and various apposite statistics from the McNemar's test are elaborated in minute detail. In the case of having more than two alignment systems for comparison, the family wise error rate is expected to happen. Thus, the ways of preventing such an error are also discussed. A directed graph visualizes the outcome of the McNemar's test in the presence of multiple alignment systems. From this graph, it is readily understood if one system is better than one another or if their differences are imperceptible. The proposed statistical methodologies are applied to the systems participated in the OAEI 2016 anatomy track, and also compares several well-known similarity metrics for the same matching problem.

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Comparing ontology matching systems are typically performed by comparing their average performances over multiple datasets. However, this paper examines the alignment systems using statistical inference since averaging is statistically unsafe and inappropriate. The statistical tests for comparison of two or multiple alignment systems are theoretically and empirically reviewed. For comparison of two systems, the Wilcoxon signed-rank and McNemar's mid-p and asymptotic tests are recommended due to their robustness and statistical safety in different circumstances. The Friedman and Quade tests with their corresponding post-hoc procedures are studied for comparison of multiple systems, and their [dis]advantages are discussed. The statistical methods are then applied to benchmark and multifarm tracks from the ontology matching evaluation initiative (OAEI) 2015 and their results are reported and visualized by critical difference diagrams.

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Simulated annealing-based ontology matching [1], or SANOM, is an ontology alignment system which ex-ploits the well-known simulated annealing to find the correspondences. The system considers three differ-ent similarity measures, namely string-based, linguistic-based and structural-based measures. A rudimen-tary version of the proposed method is participated in Ontology Alignment Evaluation Initiative (OAEI) 2017, and the results are report accordingly.@en

This paper proposes statistical approaches to test if the difference between two ontology matchers is real. Specifically, the performances of the matchers over multiple data sets are obtained and based on their performances, the conclusion can be drawn whether one method is better than one another or not. To do so, the paired t-test and Wilcoxon signed rank test are proposed and the comparisons over six recently proposed methods are reported.@en

In the Social Web, a large number of individuals stores and shares private data in social networks like Facebook and Twitter. By agreeing with their license agreements that support a revenue model, which is mostly advertising, occasionally combined with (premium) subscription and transactions, these individuals transfer data ownership to these social networks. As individuals, citizens store a lot of data in social networks that is also relevant to government. This chapter proposes an open peer-to-peer social network architecture, based on data ownership by each individual and a Social Web Ontology for interoperability between the peers. Security mechanisms are an important feature of such a network. By extending the Social Web Ontology with concepts and properties for e-Government Services and applying open data principles, the architecture can also be used by authorities. The proposed architecture includes an advertising revenue model that can be offered by intermediaries storing
user owned data. All will prosper by sharing as much data as they are willing, thus interoperability amongst providers is required. An architecture in which a citizen not only can own its data, maintain its social network and sells its data to advertisers, but also provides data to authorities to apply for particular government services, addresses both dat but in some occasions also on subscriptions a privacy challenges and eGovernment services. Authorities can play an important role by stimulating the implementation of a Social Web Ontology, initiate the development of data privacy monitoring modules warning users of potential privacy issues when selling data, and base public services on the Social Web Ontology. It will also allow users to present themselves differently in different contexts based on access control settings, e.g. private, professional, and citizen.@en