The primary motivation of this dissertation is to investigate how to enable interoperability in the logistics domain by the aid of ontology alignment. More in detail, the primary research objective of this dissertation is To address interoperability between heterogeneous IT systems in logistics by using ontology alignment. To accomplish the objective, we first look into the literature of ontology alignment using a quantitative approach to get a thorough understanding of the available literature and its progress. We particularly identify several research gaps that are studied in the subsequent chapters that serve the objective of this dissertation. An important lesson learned from the literature analysis is that there are two segregated communities that form ontology alignment but work independently and with minimal interactions with each other. Based on the identified research gaps, we develop a new system, SANOM (simulated annealing-based ontology matching), that addresses the non-deterministic polynomialtime( NP) ontology alignment problembased on thewell-knownevolutionary algorithm, simulated annealing. SANOMis equippedwith an extended Soft TF-IDF (termfrequencyinverse document frequency) string similarity metric that can also detect linguistic similarity among the names of entities in two ontologies in question. Structural similarity metrics are also taken into account that increase the alignment performance for more complex ontologies. Simulated annealing with a warm initialization is used as the matching strategy to find an optimal solution to the ontology alignment problem. The experiments show that SANOM has a very competitive performance with the best systems that participated in the ontology alignment evaluation initiative (OAEI) and is particularly faster than other evolutionary algorithm-based alignment systems. To come to a better understanding of which alignment system is preferred, we develop several methods for evaluation and comparison of alignment systems using different statistical techniques and multi-criteria decision-making methods (MCDM). We first study the frequentist approach for comparing alignment systems. More in detail, we compare different statistical tests for comparing alignment systems over single or multiple benchmarks and propose a proper test based on the number of benchmarks and alignment systems. While these techniques are more reliable than those being currently in use, it suffers from the drawbacks of making decisions based on p-values, which can be addressed by Bayesian statistics. @en

To ensure safety and security of imported goods, customs authorities perform risk assessment on incoming goods. European customs use declaration data provided by traders to execute risk analysis. Lacking availability of this data to customs authorities currently hinders proper risk management and poses threats for the European socio-economy. To solve this issue, this research analysed how Blockchain, an emerging and promising technology, could solve the issue at hand. Using a design-oriented approach, the process to be supported was analysed, the requirements were elicited, and the core blockchain components were identified. This was instrumental to develop a blockchain-based platform to support the availability of declarations data to customs authorities. The feasibility of the designed platform was evaluated through a comparison with TradeLens, an existing supply chain platform underpinned by blockchain technology, and possible implementation issues were identified by using a blockchain governance framework. The platform design contributes to the import control system implementation and research on blockchain technology. The next step is the development of a Proof-of-concept to analyse whether the designed platform supports the process at stake.

Increasingly specialised logistic services are triggering the disaggregation of supply chain functions and fostering the generation of information silos. This is perceived by European customs as a threat, since it affects the reliability of the import risk assessments and cargo inspections. In parallel, the rise of data sharing platforms based on blockchain technology (BCT) are fostering collaboration and accelerating the verification of trade finance documents in the logistics industry. The cross-organisational trust achieved in these platforms has driven the digitisation the documents from which import declaration data is extracted and shared with European customs. The latter see combining data from multiple platforms an opportunity to automate the cross-validation of declaration data, which is still performed manually to a large extent. This would improve supply chain visibility, turn declarations more agile and risk assessments more effective. However, it remains unclear how to integrate declaration procedures in these data ecosystems. There are two major barriers to overcome. Firstly, the lack of interoperability solutions to make data exchanges with multiple platforms compatible for the aggregation of declaration data. Secondly, the need to adapt available identity management solutions to the distributed architecture of these platforms to promote trust between declarants and their partners. To tackle this, a peer-to-peer data collection architecture is presented as a novel solution to migrate from import declarations based on data duplication towards information sharing based on links to the original and trusted shipping data stored in BCT platforms.