99,390 research outputs found
Efficient Discovery of Ontology Functional Dependencies
Poor data quality has become a pervasive issue due to the increasing
complexity and size of modern datasets. Constraint based data cleaning
techniques rely on integrity constraints as a benchmark to identify and correct
errors. Data values that do not satisfy the given set of constraints are
flagged as dirty, and data updates are made to re-align the data and the
constraints. However, many errors often require user input to resolve due to
domain expertise defining specific terminology and relationships. For example,
in pharmaceuticals, 'Advil' \emph{is-a} brand name for 'ibuprofen' that can be
captured in a pharmaceutical ontology. While functional dependencies (FDs) have
traditionally been used in existing data cleaning solutions to model syntactic
equivalence, they are not able to model broader relationships (e.g., is-a)
defined by an ontology. In this paper, we take a first step towards extending
the set of data quality constraints used in data cleaning by defining and
discovering \emph{Ontology Functional Dependencies} (OFDs). We lay out
theoretical and practical foundations for OFDs, including a set of sound and
complete axioms, and a linear inference procedure. We then develop effective
algorithms for discovering OFDs, and a set of optimizations that efficiently
prune the search space. Our experimental evaluation using real data show the
scalability and accuracy of our algorithms.Comment: 12 page
An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
We propose a multi-agent algorithm able to automatically discover relevant
regularities in a given dataset, determining at the same time the set of
configurations of the adopted parametric dissimilarity measure yielding compact
and separated clusters. Each agent operates independently by performing a
Markovian random walk on a suitable weighted graph representation of the input
dataset. Such a weighted graph representation is induced by the specific
parameter configuration of the dissimilarity measure adopted by the agent,
which searches and takes decisions autonomously for one cluster at a time.
Results show that the algorithm is able to discover parameter configurations
that yield a consistent and interpretable collection of clusters. Moreover, we
demonstrate that our algorithm shows comparable performances with other similar
state-of-the-art algorithms when facing specific clustering problems
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Machine learning : techniques and foundations
The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples
An Integrated Semantic Web Service Discovery and Composition Framework
In this paper we present a theoretical analysis of graph-based service
composition in terms of its dependency with service discovery. Driven by this
analysis we define a composition framework by means of integration with
fine-grained I/O service discovery that enables the generation of a graph-based
composition which contains the set of services that are semantically relevant
for an input-output request. The proposed framework also includes an optimal
composition search algorithm to extract the best composition from the graph
minimising the length and the number of services, and different graph
optimisations to improve the scalability of the system. A practical
implementation used for the empirical analysis is also provided. This analysis
proves the scalability and flexibility of our proposal and provides insights on
how integrated composition systems can be designed in order to achieve good
performance in real scenarios for the Web.Comment: Accepted to appear in IEEE Transactions on Services Computing 201
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
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