The Landscape of Ontology Reuse Approaches

Ontology reuse aims to foster interoperability and facilitate knowledge reuse. Several approaches are typically evaluated by ontology engineers when bootstrapping a new project. However, current practices are often motivated by subjective, case-by-case decisions, which hamper the definition of a recommended behaviour. In this chapter we argue that to date there are no effective solutions for supporting developers' decision-making process when deciding on an ontology reuse strategy. The objective is twofold: (i) to survey current approaches to ontology reuse, presenting motivations, strategies, benefits and limits, and (ii) to analyse two representative approaches and discuss their merits

Exploiting conceptual spaces for ontology integration

The widespread use of ontologies raises the need to integrate distinct conceptualisations. Whereas the symbolic approach of established representation standards – based on first-order logic (FOL) and syllogistic reasoning – does not implicitly represent semantic similarities, ontology mapping addresses this problem by aiming at establishing formal relations between a set of knowledge entities which represent the same or a similar meaning in distinct ontologies. However, manually or semi-automatically identifying similarity relationships is costly. Hence, we argue, that representational facilities are required which enable to implicitly represent similarities. Whereas Conceptual Spaces (CS) address similarity computation through the representation of concepts as vector spaces, CS rovide neither an implicit representational mechanism nor a means to represent arbitrary relations between concepts or instances. In order to overcome these issues, we propose a hybrid knowledge representation approach which extends FOL-based ontologies with a conceptual grounding through a set of CS-based representations. Consequently, semantic similarity between instances – represented as members in CS – is indicated by means of distance metrics. Hence, automatic similarity detection across distinct ontologies is supported in order to facilitate ontology integration

Observing LOD: Its Knowledge Domains and the Varying Behavior of Ontologies Across Them

Linked Open Data (LOD) is the largest, collaborative, distributed, and publicly-accessible Knowledge Graph (KG) uniformly encoded in the Resource Description Framework (RDF) and formally represented according to the semantics of the Web Ontology Language (OWL). LOD provides researchers with a unique opportunity to study knowledge engineering as an empirical science: to observe existing modelling practices and possibly understanding how to improve knowledge engineering methodologies and knowledge representation formalisms. Following this perspective, several studies have analysed LOD to identify (mis-)use of OWL constructs or other modelling phenomena e.g. class or property usage, their alignment, the average depth of taxonomies. A question that remains open is whether there is a relation between observed modelling practices and knowledge domains (natural science, linguistics, etc.): do certain practices or phenomena change as the knowledge domain varies? Answering this question requires an assessment of the domains covered by LOD as well as a classification of its datasets. Existing approaches to classify LOD datasets provide partial and unaligned views, posing additional challenges. In this paper, we introduce a classification of knowledge domains, and a method for classifying LOD datasets and ontologies based on it. We classify a large portion of LOD and investigate whether a set of observed phenomena have a domain-specific character

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

Ontology Alignment using Biologically-inspired Optimisation Algorithms

It is investigated how biologically-inspired optimisation methods can be used to compute alignments between ontologies. Independent of particular similarity metrics, the developed techniques demonstrate anytime behaviour and high scalability. Due to the inherent parallelisability of these population-based algorithms it is possible to exploit dynamically scalable cloud infrastructures - a step towards the provisioning of Alignment-as-a-Service solutions for future semantic applications