24,183 research outputs found
A multi-layered Bayesian network model for structured document retrieval
New standards in document representation, like for example SGML, XML, and MPEG-7, compel Information Retrieval to design and implement models and tools to index, retrieve and present documents according to the given document structure. The paper presents the design of an Information Retrieval system for multimedia structured documents, like for example journal articles, e-books, and MPEG-7 videos. The system is based on Bayesian Networks, since this class of mathematical models enable to represent and quantify the relations between the structural components of the document. Some preliminary results on the system implementation are also presented
A multi-layered Bayesian network model for structured document retrieval
New standards in document representation, like for example SGML, XML, and MPEG-7, compel Information Retrieval to design and implement models and tools to index, retrieve and present documents according to the given document structure. The paper presents the design of an Information Retrieval system for multimedia structured documents, like for example journal articles, e-books, and MPEG-7 videos. The system is based on Bayesian Networks, since this class of mathematical models enable to represent and quantify the relations between the structural components of the document. Some preliminary results on the system implementation are also presented
Using Ontologies for Semantic Data Integration
While big data analytics is considered as one of the most important paths to competitive advantage of today’s enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities. Here, we focus on a specific paradigm for semantic data integration, called Ontology-Based Data Access (OBDA). The goal of this paper is to provide an overview of OBDA, pointing out both the techniques that are at the basis of the paradigm, and the main challenges that remain to be addressed
Probabilistic Graphical Model Representation in Phylogenetics
Recent years have seen a rapid expansion of the model space explored in
statistical phylogenetics, emphasizing the need for new approaches to
statistical model representation and software development. Clear communication
and representation of the chosen model is crucial for: (1) reproducibility of
an analysis, (2) model development and (3) software design. Moreover, a
unified, clear and understandable framework for model representation lowers the
barrier for beginners and non-specialists to grasp complex phylogenetic models,
including their assumptions and parameter/variable dependencies.
Graphical modeling is a unifying framework that has gained in popularity in
the statistical literature in recent years. The core idea is to break complex
models into conditionally independent distributions. The strength lies in the
comprehensibility, flexibility, and adaptability of this formalism, and the
large body of computational work based on it. Graphical models are well-suited
to teach statistical models, to facilitate communication among phylogeneticists
and in the development of generic software for simulation and statistical
inference.
Here, we provide an introduction to graphical models for phylogeneticists and
extend the standard graphical model representation to the realm of
phylogenetics. We introduce a new graphical model component, tree plates, to
capture the changing structure of the subgraph corresponding to a phylogenetic
tree. We describe a range of phylogenetic models using the graphical model
framework and introduce modules to simplify the representation of standard
components in large and complex models. Phylogenetic model graphs can be
readily used in simulation, maximum likelihood inference, and Bayesian
inference using, for example, Metropolis-Hastings or Gibbs sampling of the
posterior distribution
APQL: A process-model query language
As business process management technology matures, organisations acquire more and more business process models. The management of the resulting collections of process models poses real challenges. One of these challenges concerns model retrieval where support should be provided for the formulation and efficient execution of business process model queries. As queries based on only structural information cannot deal with all querying requirements in practice, there should be support for queries that require knowledge of process model semantics. In this paper we formally define a process model query language that is based on semantic relationships between tasks in process models and is independent of any particular process modelling notation
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