Optimizing Ontology Alignments through NSGA-II without Using Reference Alignment

Ontology is widely used to solve the data heterogeneity problems on the semantic web, but the available ontologies could themselves introduce heterogeneity. In order to reconcile these ontologies to implement the semantic interoperability, we need to find the relationships among the entities in various ontologies, and the process of identifying them is called ontology alignment. In all the existing matching systems that use evolutionary approaches to optimize their parameters, a reference alignment between two ontologies to be aligned should be given in advance which could be very expensive to obtain especially when the scale of ontologies is considerably large. To address this issue, in this paper we propose a novel approach to utilize the NSGA-II to optimize the ontology alignments without using the reference alignment. In our approach, an adaptive aggregation strategy is presented to improve the efficiency of optimizing process and two approximate evaluation measures, namely match coverage and match ratio, are introduced to replace the classic recall and precision on reference alignment to evaluate the quality of the alignments. Experimental results show that our approach is effective and can find the solutions that are very close to those obtained by the approaches using reference alignment, and the quality of alignments is in general better than that of state of the art ontology matching systems such as GOAL and SAMBO

Synthesis of Model Transformations from Metamodels and Examples

Model transformations are central elements of model-driven engineering (MDE). However, model transformation development requires a high level of expertise in particular model transformation languages, and model transformation specifications are often difficult to manually construct, due to the lack of tool support, and the dependencies involved in transformation rules.In this thesis, we describe techniques for automatically or semi-automatically synthesising transformations from metamodels and examples, in order to reduce model transformation development costs and time, and improve model transformation quality.We proposed two approaches for synthesising transformations from metamodels. The first approach is the Data Structure Similarity Approach, an exhaustive metamodel matching approach, which extracts correspondences between metamodels by only focusing on the type of features. The other approach is the Search-based Optimisation Approach, which uses an optimisation algorithm to extract correspondences from metamodels by data structure similarity, name syntax similarity, and name semantic similarity. The correspondence patterns between the classes and features of two metamodels are extracted by either of these two methods. To enable the production of specifications in multiple model transformation languages from correspondences, we introduced an intermediate language which uses a simplified transformation notation to express transformation specifications in a language-independent manner, and defined the mapping rules from this intermediate language to different transformation languages.We also investigated Model Transformation by Examples Approach. We used machine learning techniques to learn model transformation rules from datasets of examples, so that the trained model could generate target model from source model directly.We evaluated our approaches on a range of cases of different kinds of transformation, and compared the model transformation accuracy and quality of our versions to the previously-developed manual versions of these cases.Key words: model transformation, model-driven engineering, transformation syn-thesis, metamodel matching, model transformation by example

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

Sensor data fusion for the industrial artificial intelligence of things

The emergence of smart sensors, artificial intelligence, and deep learning technologies yield artificial intelligence of things, also known as the AIoT. Sophisticated cooperation of these technologies is vital for the effective processing of industrial sensor data. This paper introduces a new framework for addressing the different challenges of the AIoT applications. The proposed framework is an intelligent combination of multi-agent systems, knowledge graphs and deep learning. Deep learning architectures are used to create models from different sensor-based data. Multi-agent systems can be used for simulating the collective behaviours of the smart sensors using IoT settings. The communication among different agents is realized by integrating knowledge graphs. Different optimizations based on constraint satisfaction as well as evolutionary computation are also investigated. Experimental analysis is undertaken to compare the methodology presented to state-of-the-art AIoT technologies. We show through experimentation that our designed framework achieves good performance compared to baseline solutions.publishedVersio

A service broker for Intercloud computing

This thesis aims at assisting users in finding the most suitable Cloud resources taking into account their functional and non-functional SLA requirements. A key feature of the work is a Cloud service broker acting as mediator between consumers and Clouds. The research involves the implementation and evaluation of two SLA-aware match-making algorithms by use of a simulation environment. The work investigates also the optimal deployment of Multi-Cloud workflows on Intercloud environments

A Multiobjective Evolutionary Conceptual Clustering Methodology for Gene Annotation Within Structural Databases: A Case of Study on the Gene Ontology Database

Current tools and techniques devoted to examine the content of large databases are often hampered by their inability to support searches based on criteria that are meaningful to their users. These shortcomings are particularly evident in data banks storing representations of structural data such as biological networks. Conceptual clustering techniques have demonstrated to be appropriate for uncovering relationships between features that characterize objects in structural data. However, typical con ceptual clustering approaches normally recover the most obvious relations, but fail to discover the lessfrequent but more informative underlying data associations. The combination of evolutionary algorithms with multiobjective and multimodal optimization techniques constitutes a suitable tool for solving this problem. We propose a novel conceptual clustering methodology termed evolutionary multiobjective conceptual clustering (EMO-CC), re lying on the NSGA-II multiobjective (MO) genetic algorithm. We apply this methodology to identify conceptual models in struc tural databases generated from gene ontologies. These models can explain and predict phenotypes in the immunoinflammatory response problem, similar to those provided by gene expression or other genetic markers. The analysis of these results reveals that our approach uncovers cohesive clusters, even those comprising a small number of observations explained by several features, which allows describing objects and their interactions from different perspectives and at different levels of detail.Ministerio de Ciencia y Tecnología TIC-2003-00877Ministerio de Ciencia y Tecnología BIO2004-0270EMinisterio de Ciencia y Tecnología TIN2006-1287