Ontology alignment through argumentation

Currently, the majority of matchers are able to establish simple correspondences between entities, but are not able to provide complex alignments. Furthermore, the resulting alignments do not contain additional information on how they were extracted and formed. Not only it becomes hard to debug the alignment results, but it is also difficult to justify correspondences. We propose a method to generate complex ontology alignments that captures the semantics of matching algorithms and human-oriented ontology alignment definition processes. Through these semantics, arguments that provide an abstraction over the specificities of the alignment process are generated and used by agents to share, negotiate and combine correspondences. After the negotiation process, the resulting arguments and their relations can be visualized by humans in order to debug and understand the given correspondences.(undefined

Don't Treat the Symptom, Find the Cause! Efficient Artificial-Intelligence Methods for (Interactive) Debugging

In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in e-commerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, the power grid to ensure our energy supply, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play. Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above, and many more. It exploits and orchestrates i.a. techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, optimization, stochastics, statistics, decision making under uncertainty, machine learning, as well as calculus, combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems. In this thesis, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these issues.Comment: Habilitation Thesi

Alignment Incoherence in Ontology Matching

Ontology matching is the process of generating alignments between ontologies. An alignment is a set of correspondences. Each correspondence links concepts and properties from one ontology to concepts and properties from another ontology. Obviously, alignments are the key component to enable integration of knowledge bases described by different ontologies. For several reasons, alignments contain often erroneous correspondences. Some of these errors can result in logical conflicts with other correspondences. In such a case the alignment is referred to as an incoherent alignment. The relevance of alignment incoherence and strategies to resolve alignment incoherence are in the center of this thesis. After an introduction to syntax and semantics of ontologies and alignments, the importance of alignment coherence is discussed from different perspectives. On the one hand, it is argued that alignment incoherence always coincides with the incorrectness of correspondences. On the other hand, it is demonstrated that the use of incoherent alignments results in severe problems for different types of applications. The main part of this thesis is concerned with techniques for resolving alignment incoherence, i.e., how to find a coherent subset of an incoherent alignment that has to be preferred over other coherent subsets. The underlying theory is the theory of diagnosis. In particular, two specific types of diagnoses, referred to as local optimal and global optimal diagnosis, are proposed. Computing a diagnosis is for two reasons a challenge. First, it is required to use different types of reasoning techniques to determine that an alignment is incoherent and to find subsets (conflict sets) that cause the incoherence. Second, given a set of conflict sets it is a hard problem to compute a global optimal diagnosis. In this thesis several algorithms are suggested to solve these problems in an efficient way. In the last part of this thesis, the previously developed algorithms are applied to the scenarios of - evaluating alignments by computing their degree of incoherence; - repairing incoherent alignments by computing different types of diagnoses; - selecting a coherent alignment from a rich set of matching hypotheses; - supporting the manual revision of an incoherent alignment. In the course of discussing the experimental results, it becomes clear that it is possible to create a coherent alignment without negative impact on the alignments quality. Moreover, results show that taking alignment incoherence into account has a positive impact on the precision of the alignment and that the proposed approach can help a human to save effort in the revision process

Microservice Transition and its Granularity Problem: A Systematic Mapping Study

Microservices have gained wide recognition and acceptance in software industries as an emerging architectural style for autonomic, scalable, and more reliable computing. The transition to microservices has been highly motivated by the need for better alignment of technical design decisions with improving value potentials of architectures. Despite microservices' popularity, research still lacks disciplined understanding of transition and consensus on the principles and activities underlying "micro-ing" architectures. In this paper, we report on a systematic mapping study that consolidates various views, approaches and activities that commonly assist in the transition to microservices. The study aims to provide a better understanding of the transition; it also contributes a working definition of the transition and technical activities underlying it. We term the transition and technical activities leading to microservice architectures as microservitization. We then shed light on a fundamental problem of microservitization: microservice granularity and reasoning about its adaptation as first-class entities. This study reviews state-of-the-art and -practice related to reasoning about microservice granularity; it reviews modelling approaches, aspects considered, guidelines and processes used to reason about microservice granularity. This study identifies opportunities for future research and development related to reasoning about microservice granularity.Comment: 36 pages including references, 6 figures, and 3 table

Automatic Generation of Trace Links in Model-driven Software Development

Traceability data provides the knowledge on dependencies and logical relations existing amongst artefacts that are created during software development. In reasoning over traceability data, conclusions can be drawn to increase the quality of software. The paradigm of Model-driven Software Engineering (MDSD) promotes the generation of software out of models. The latter are specified through different modelling languages. In subsequent model transformations, these models are used to generate programming code automatically. Traceability data of the involved artefacts in a MDSD process can be used to increase the software quality in providing the necessary knowledge as described above. Existing traceability solutions in MDSD are based on the integral model mapping of transformation execution to generate traceability data. Yet, these solutions still entail a wide range of open challenges. One challenge is that the collected traceability data does not adhere to a unified formal definition, which leads to poorly integrated traceability data. This aggravates the reasoning over traceability data. Furthermore, these traceability solutions all depend on the existence of a transformation engine. However, not in all cases pertaining to MDSD can a transformation engine be accessed, while taking into account proprietary transformation engines, or manually implemented transformations. In these cases it is not possible to instrument the transformation engine for the sake of generating traceability data, resulting in a lack of traceability data. In this work, we address these shortcomings. In doing so, we propose a generic traceability framework for augmenting arbitrary transformation approaches with a traceability mechanism. To integrate traceability data from different transformation approaches, our approach features a methodology for augmentation possibilities based on a design pattern. The design pattern supplies the engineer with recommendations for designing the traceability mechanism and for modelling traceability data. Additionally, to provide a traceability mechanism for inaccessible transformation engines, we leverage parallel model matching to generate traceability data for arbitrary source and target models. This approach is based on a language-agnostic concept of three similarity measures for matching. To realise the similarity measures, we exploit metamodel matching techniques for graph-based model matching. Finally, we evaluate our approach according to a set of transformations from an SAP business application and the domain of MDSD

i3MAGE: Incremental, Interactive, Inter-Model Mapping Generation

Data integration is a highly important prerequisite for most enterprise data analyses. While hard in general, a particular concern is about human effort for designing a global integration schema, authoring queries against that schema, and creating mappings to connect data sources with the global schema. Ontology-based data integration (OBDI), which employs ontologies as a target model, reduces the effort for schema design and usage. On the other side, it requires mappings that are particularly difficult to create. Architects who work with OBDI hence need systems to support the process of mapping development. One key type of tooling to support mapping development is automatic or semi-automatic generation of mapping suggestions. While many such tools exist in the wider sphere of data integration, few are built to work in the case of OBDI, where the inter-model gap between relational input schemata and a target ontology has to be bridged. Among those that support OBDI at all, none so far are fully optimized for this specific case by performing a truly inter-model matching while also leveraging distinct but corresponding aspects of both models. We propose i3MAGE, an approach and a system for automatic and semi-automatic generation of mappings in OBDI. The system is built on generic inter-model matching, and it is optimized in various ways for matching relational source schemata to target ontology schemata. To be truly semi-automatic in every respect, i3MAGE works both incrementally, building mappings pay-as-you-go, and interactively in exchange with a human user. We introduce a specialized benchmark and evaluate i3MAGE against a number of other approaches. In addition, we provide examples, where i3MAGE can be deployed in holistic data integration environments