Sofia Triantafillou

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Scientific practice typically involves repeatedly studying a system, each time trying to unravel a different perspective. In each study, the scientist may take measurements under different experimental conditions (interventions, manipulations, perturbations) and measure different sets of quantities (variables). The result is a collection of heterogeneous(More)
This paper documents part of a research project under the title: “Computer-Assisted Education and Communication of Individuals with Autistic Syndrome”, which aims at designing and developing computer-based environments for aiding the education and assessment of autistic children. The theoretical basis of the project is explained. Finally, a scenario titled(More)
Modern data-analysis methods are typically applicable to a single dataset. In particularly, they cannot integratively analyze datasets containing different, but overlapping, sets of variables. We show that by employing causal models instead of models based on the concept of association alone, it is possible to make additional interesting inferences by(More)
In this paper we address the problem of incorporating prior knowledge, in the form of causal relations, in causal models. Prior approaches mostly consider knowledge about the presence or absence of edges in the model. We use the formalism of Maximal Ancestral Graphs (MAGs) and adapt cSAT+ to solve this problem, an algorithm for reasoning with datasets(More)
Gene Ontology information related to the biological role of genes is organized in a hierarchical manner that can be represented by a directed acyclic graph (DAG). Space filling visualizations, such as the treemaps, have the capacity to display thousands of items legibly in limited space via a two-dimensional rectangular map. Treemaps have been used to(More)
We present methods able to predict the presence and strength of conditional and unconditional dependencies (correlations) between two variables Y and Z never jointly measured on the same samples, based on multiple data sets measuring a set of common variables. The algorithms are specializations of prior work on learning causal structures from overlapping(More)
The development of the Semantic Web proceeds in steps, building each layer on top of the other. Currently, the focus of research efforts is concentrated on logic and proofs, both of which are essential, since they will allow systems to infer new knowledge by applying principles on the existing data and explain their actions. Research is shifting towards the(More)