An ontology-based approach to define and manage B2B interoperability

Electronic business surely represents the new development perspective for world-wide trade. Together with the idea of ebusiness, and the exigency to exchange business messages between trading partners, the concept of business-to-business (B2B) integration arouse. B2B integration is becoming necessary to allow partners to communicate and exchange business documents, like catalogues, purchase orders, reports and invoices, overcoming architectural, applicative, and semantic differences, according to the business processes implemented by each enterprise. Business relationships can be very heterogeneous, and consequently there are variousways to integrate enterprises with each other. Moreover nowadays not only large enterprises, but also the small- and medium- enterprises are moving towards ebusiness: more than two-thirds of Small and Medium Enterprises (SMEs) use the Internet as a business tool. One of the business areas which is actively facing the interoperability problem is that related with the supply chain management. In order to really allow the SMEs to improve their business and to fully exploit ICT technologies in their business transactions, there are three main players that must be considered and joined: the new emerging ICT technologies, the scenario and the requirements of the enterprises and the world of standards and standardisation bodies. This thesis presents the definition and the development of an interoperability framework (and the bounded standardisation intiatives) to provide the Textile/Clothing sectorwith a shared set of business documents and protocols for electronic transactions. Considering also some limitations, the thesis proposes a ontology-based approach to improve the functionalities of the developed framework and, exploiting the technologies of the semantic web, to improve the standardisation life-cycle, intended as the development, dissemination and adoption of B2B protocols for specific business domain. The use of ontologies allows the semantic modellisation of knowledge domains, upon which it is possible to develop a set of components for a better management of B2B protocols, and to ease their comprehension and adoption for the target users.

The changing politics of preference formation in international trade negotiations: the European Union in the Doha Round

This research seeks to provide an explanation for variations of “politics” of preference formation in international trade negotiations. Building on the ‘policy determines politics’ argument, I hypothesize the existence of a causal relationship between issue-characteristics and their variations with politics dynamics and their variations. More specifically, this study seeks to integrate into a single analytical framework two dimensions along which variations in the “politics of preference formation” can be organized: configurations of power relationships among the relevant actors in the structures within which they interact as well as the logic and the motivations of the actors involved in the policy making process. To do so, I first construct a four-cell typology of ‘politics of preference formation’ and, then, I proceed by specifying that the type of state-society configurations as well as the type of actors’ motivations in the “politics of preference formation” depend, respectively, on the degree to which a policy issue is perceived as politically salient and on the extent to which the distributional implications of such an issue can be calculated by the relevant stakeholders in the policy making process. The empirical yardstick against which the validity of the theoretical argument proposed is tested is drawn from evidence concerning the European Union’s negotiating strategy in four negotiating areas in the context of the so-called WTO’s Doha Development Round of multilateral trade negotiations: agriculture, competition, environment and technical assistance and capacity building.

An architecture for scaling ontology networks

Constructing ontology networks typically occurs at design time at the hands of knowledge engineers who assemble their components statically. There are, however, use cases where ontology networks need to be assembled upon request and processed at runtime, without altering the stored ontologies and without tampering with one another. These are what we call "virtual [ontology] networks", and keeping track of how an ontology changes in each virtual network is called "multiplexing". Issues may arise from the connectivity of ontology networks. In many cases, simple flat import schemes will not work, because many ontology managers can cause property assertions to be erroneously interpreted as annotations and ignored by reasoners. Also, multiple virtual networks should optimize their cumulative memory footprint, and where they cannot, this should occur for very limited periods of time. We claim that these problems should be handled by the software that serves these ontology networks, rather than by ontology engineering methodologies. We propose a method that spreads multiple virtual networks across a 3-tier structure, and can reduce the amount of erroneously interpreted axioms, under certain raw statement distributions across the ontologies. We assumed OWL as the core language handled by semantic applications in the framework at hand, due to the greater availability of reasoners and rule engines. We also verified that, in common OWL ontology management software, OWL axiom interpretation occurs in the worst case scenario of pre-order visit. To measure the effectiveness and space-efficiency of our solution, a Java and RESTful implementation was produced within an Apache project. We verified that a 3-tier structure can accommodate reasonably complex ontology networks better, in terms of the expressivity OWL axiom interpretation, than flat-tree import schemes can. We measured both the memory overhead of the additional components we put on top of traditional ontology networks, and the framework's caching capabilities.

A clustering method for robust and reliable large scale functional and structural protein sequence annotation

Bioinformatics, in the last few decades, has played a fundamental role to give sense to the huge amount of data produced. Obtained the complete sequence of a genome, the major problem of knowing as much as possible of its coding regions, is crucial. Protein sequence annotation is challenging and, due to the size of the problem, only computational approaches can provide a feasible solution. As it has been recently pointed out by the Critical Assessment of Function Annotations (CAFA), most accurate methods are those based on the transfer-by-homology approach and the most incisive contribution is given by cross-genome comparisons. In the present thesis it is described a non-hierarchical sequence clustering method for protein automatic large-scale annotation, called “The Bologna Annotation Resource Plus” (BAR+). The method is based on an all-against-all alignment of more than 13 millions protein sequences characterized by a very stringent metric. BAR+ can safely transfer functional features (Gene Ontology and Pfam terms) inside clusters by means of a statistical validation, even in the case of multi-domain proteins. Within BAR+ clusters it is also possible to transfer the three dimensional structure (when a template is available). This is possible by the way of cluster-specific HMM profiles that can be used to calculate reliable template-to-target alignments even in the case of distantly related proteins (sequence identity < 30%). Other BAR+ based applications have been developed during my doctorate including the prediction of Magnesium binding sites in human proteins, the ABC transporters superfamily classification and the functional prediction (GO terms) of the CAFA targets. Remarkably, in the CAFA assessment, BAR+ placed among the ten most accurate methods. At present, as a web server for the functional and structural protein sequence annotation, BAR+ is freely available at http://bar.biocomp.unibo.it/bar2.0.

Empirical ontology design patterns

Knowledge graphs (KGs) and ontologies have been widely adopted for modelling numerous domains. However, understanding the content of an ontology/KG is far from straightforward: existing methods partially address this issue. This thesis is based on the assumption that identifying the Ontology Design Patterns (ODPs) in an ontology or a KG contributes to address this problem. Most times, the reused ODPs are not explicitly annotated, or their reuse is unintentional. Therefore, there is a challenge to automatically identify ODPs in existing ontologies and KGs, which is the main focus of this research work. This thesis analyses the role of ODPs in ontology engineering, through experiences in actual ontology projects, placing this analysis in the context of existing ontology reuse approaches. Moreover, this thesis introduces a novel method for extracting empirical ODPs (EODPs) from ontologies, and a novel method for extracting EODPs from knowledge graphs, whose schemas are implicit. The first method groups the extracted EODPs in clusters: conceptual components. Each conceptual component represents a modelling problem, e.g. representing collections. As EODPs are fragments possibly extracted from different ontologies, some of them will fall in the same cluster, meaning that they are implemented solutions to the same modelling problem. EODPs and conceptual components enable the empirical observation and comparison of modelling solutions to common modelling problems in different ontologies. The second method extracts EODPs from a KG as sets of probabilistic axioms/constraints involving the ontological entities instantiated. These EODPs may support KG inspection and comparison, providing insights on how certain entities are described in a KG. An additional contribution of this thesis is an ontology for annotating ODPs in ontologies and KGs.

Supporting requirement elicitation and ontology testing in knowledge graph engineering

Knowledge graphs and ontologies are closely related concepts in the field of knowledge representation. In recent years, knowledge graphs have gained increasing popularity and are serving as essential components in many knowledge engineering projects that view them as crucial to their success. The conceptual foundation of the knowledge graph is provided by ontologies. Ontology modeling is an iterative engineering process that consists of steps such as the elicitation and formalization of requirements, the development, testing, refactoring, and release of the ontology. The testing of the ontology is a crucial and occasionally overlooked step of the process due to the lack of integrated tools to support it. As a result of this gap in the state-of-the-art, the testing of the ontology is completed manually, which requires a considerable amount of time and effort from the ontology engineers. The lack of tool support is noticed in the requirement elicitation process as well. In this aspect, the rise in the adoption and accessibility of knowledge graphs allows for the development and use of automated tools to assist with the elicitation of requirements from such a complementary source of data. Therefore, this doctoral research is focused on developing methods and tools that support the requirement elicitation and testing steps of an ontology engineering process. To support the testing of the ontology, we have developed XDTesting, a web application that is integrated with the GitHub platform that serves as an ontology testing manager. Concurrently, to support the elicitation and documentation of competency questions, we have defined and implemented RevOnt, a method to extract competency questions from knowledge graphs. Both methods are evaluated through their implementation and the results are promising.