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Being able to read successfully the bits and bytes stored inside a digital archive does not necessarily mean we are able to extract meaningful information from an archived digital document. If information about the format of a stored document is not available, the contents of the document are essentially lost.
One solution to the problem is format conversion, but due to the amount of documents and formats involved, manual conversion of archived documents is usually impractical. There is thus an open research question to discover suitable technologies to transform existing documents into new document formats and to determine the constraints within which these technologies can be applied successfully.
In the present work, it is assumed that stored documents are represented as formal description logic ontologies. This makes it possible to view the translation of document formats as an application of ontology matching, an area for which many methods and algorithms have been developed over the recent years.
With very few exceptions, however, current ontology matchers are limited to element-level correspondences matching concepts against concepts, roles against roles, and individuals against individuals. Such simple correspondences are insufficient to describe mappings between complex digital documents.
This thesis presents a method to refine simple correspondences into more complex ones in a heuristic fashion utilizing a modified form of description logic tableau reasoning. The refinement process uses a model-based representation of correspondences. Building on the formal semantics, the process also includes methods to avoid the generation of inconsistent or incoherent correspondences.
In a second part, this thesis also makes use of the model-based representation to determine the best set of correspondences between two ontologies.
The developed similarity measures make use of semantic information from both description logic tableau reasoning as well as from the refinement process.
The result is a new method to semi-automatically derive complex correspondences between description logic ontologies tailored but not limited to the context of format migration.

Gender Roles and the Emergence of a Writer in Denise Chávez's "The Last of the Menu Girls (1986)"
(2018)

The exponential random graph model (ERGM) is a class of stochastic models for network data widely applied in statistical social network analysis. The ERGM can be used to model a wide range of social processes. However, it is generally difficult to estimate due to its intractable normalizing constant. Markov chain Monte Carlo maximum likelihood (MCMC-ML) ERGM estimation is available but tends to be numerically unstable due to model degeneracy of particular specifications. Bayesian ERGM estimation is robust to model degeneracy and is a practical alternative to the MCMC-ML approach.
Bayesian model selection is based on the Bayes factor which is the ratio of marginal likelihoods of concurring models. The research aim of this thesis is to estimate the marginal likelihood of the ERGM class using path sampling which is also called thermodynamic integration. Power posterior sampling is a discretized version of thermodynamic integration using a fixed path of tempering steps to transition from the prior distribution to the posterior distribution of interest. In this thesis, power posterior sampling is used both to integrate over the parameter space of the ERGM posterior distribution of interest and to yield an estimate of the respective intractable ERGM normalizing constant. Existing approaches of estimating the ERGM marginal likelihood rely on a non-parametric density approximation or a Laplace approximation. The proposed power posterior exchange algorithm with explicit evaluation of the likelihood (PPEA-EEL) does not require such approximations and yields a valid estimate of the ERGM marginal likelihood.
As the PPEA-EEL is a computationally expensive approach involving many MCMC samples, new graphical methods to evaluate power posterior samples are developed.
In this thesis a brief introduction to random graphs and network dependencies is given. The ERGM class is discussed and various dependency assumptions are illustrated. MCMC-ML ERGM estimation is applied to policy networks in Ghana, Senegal and Uganda. Bayesian ERGM estimation and Bayesian model selection are discussed. An overview is given on methods of estimating the marginal likelihood originating from importance sampling, namely bridge sampling, path sampling and power posterior sampling. The PPEA-EEL is applied to social network data and the numerical stability of the approach is evaluated.