UML for the Semantic Web: Transformation-Based Approaches

The perspective role of UML as a conceptual modelling language for the Semantic Web has become an important research topic. We argue that UML could be a key technology for overcoming the ontology development bottleneck thanks to its wide acceptance and sophisticated tool support. Transformational approaches are a promising way of establishing a connection between UML and web-based ontology languages. We compare some proposals for defining transformations between UML and web ontology languages and discuss the different ways they handle the conceptual differences between these languages. We identify commonalities and differences of the approaches and point out open questions that have not or not satisfyingly been addressed by existing approaches

Ontology-Based Information Sharing in Weakly Structured Environments

Harmelen, F.A.H. van [Promotor]Herzog, O. [Copromotor

Backpropagating through Markov Logic Networks

We integrate Markov Logic networks with deep learning architectures operating on high-dimensional and noisy feature inputs. Instead of relaxing the discrete components into smooth functions, we propose an approach that allows us to backpropagate through standard statistical relational learning components using perturbation-based differentiation. The resulting hybrid models are shown to outperform models solely relying on deep learning based function fitting. We find that using noise perturbations is required to allow the proposed hybrid models to robustly learn from the training data

Winnowing ontologies based on application use

The requirements of specific applications and services are often over estimated when ontologies are reused or built. This sometimes results in many ontologies being too large for their intended purposes. It is not uncommon that when applications and services are deployed over an ontology, only a few parts of the ontology are queried and used. Identifying which parts of an ontology are being used could be helpful to winnow the ontology, i.e., simplify or shrink the ontology to smaller, more fit for purpose size. Some approaches to handle this problem have already been suggested in the literature. However, none of that work showed how ontology-based applications can be used in the ontology-resizing process, or how they might be affected by it. This paper presents a study on the use of the AKT Reference Ontology by a number of applications and services,and investigates the possibility of relying on this usage information to winnow that ontology

Approximate OWL-Reasoning with Screech

Applications of expressive ontology reasoning for the Semantic Web require scalable algorithms for deducing implicit knowledge from explicitly given knowledge bases. Besides the development of more effi- cient such algorithms, awareness is rising that approximate reasoning solutions will be helpful and needed for certain application domains. In this paper, we present a comprehensive overview of the Screech approach to approximate reasoning with OWL ontologies, which is based on the KAON2 algorithms, facilitating a compilation of OWL DL TBoxes into Datalog, which is tractable in terms of data complexity. We present three different instantiations of the Screech approach, and report on experiments which show that a significant gain in efficiency can be achieved

Multi Visualization and Dynamic Query for Effective Exploration of Semantic Data

Semantic formalisms represent content in a uniform way according to ontologies. This enables manipulation and reasoning via automated means (e.g. Semantic Web services), but limits the user’s ability to explore the semantic data from a point of view that originates from knowledge representation motivations. We show how, for user consumption, a visualization of semantic data according to some easily graspable dimensions (e.g. space and time) provides effective sense-making of data. In this paper, we look holistically at the interaction between users and semantic data, and propose multiple visualization strategies and dynamic filters to support the exploration of semantic-rich data. We discuss a user evaluation and how interaction challenges could be overcome to create an effective user-centred framework for the visualization and manipulation of semantic data. The approach has been implemented and evaluated on a real company archive

Evaluating Ontology Matching Systems on Large, Multilingual and Real-world Test Cases

In the field of ontology matching, the most systematic evaluation of matching systems is established by the Ontology Alignment Evaluation Initiative (OAEI), which is an annual campaign for evaluating ontology matching systems organized by different groups of researchers. In this paper, we report on the results of an intermediary OAEI campaign called OAEI 2011.5. The evaluations of this campaign are divided in five tracks. Three of these tracks are new or have been improved compared to previous OAEI campaigns. Overall, we evaluated 18 matching systems. We discuss lessons learned, in terms of scalability, multilingual issues and the ability do deal with real world cases from different domains

PADTUN - using semantic technologies in tunnel diagnosis and maintenance domain

YesA Decision Support System (DSS) in tunnelling domain deals with identifying pathologies based on disorders present in various tunnel portions and contextual factors affecting a tunnel. Another key area in diagnosing pathologies is to identify regions of interest (ROI). In practice, tunnel experts intuitively abstract regions of interest by selecting tunnel portions that are susceptible to the same types of pathologies with some distance approximation. This complex diagnosis process is often subjective and poorly scales across cases and transport structures. In this paper, we introduce PADTUN system, a working prototype of a DSS in tunnelling domain using semantic technologies. Ontologies are developed and used to capture tacit knowledge from tunnel experts. Tunnel inspection data are annotated with ontologies to take advantage of inferring capabilities offered by semantic technologies. In addition, an intelligent mechanism is developed to exploit abstraction and inference capabilities to identify ROI. PADTUN is developed in real-world settings offered by the NeTTUN EU Project and is applied in a tunnel diagnosis use case with Société Nationale des Chemins de Fer Français (SNCF), France. We show how the use of semantic technologies allows addressing the complex issues of pathology and ROI inferencing and matching experts’ expectations of decision support

Exploring Large Document Repositories with RDF Technology: The DOPE Project

This thesaurus-based search system uses automatic indexing, RDF-based querying, and concept-based visualization of results to support exploration of large online document repositories