1,721,254 research outputs found
A Sentence Structure-based Approach to Unsupervised Author Identification
Full text linkAssessing whether two documents were written by the same author is a crucial task, especially in the Internet age, with possible applications to philology and forensics.
The problem has been tackled in the literature by exploiting frequency-based approaches, numeric techniques or writing style analysis. Focusing on this last perspective, this paper proposes a novel technique that takes into account the structure of sentences, assuming that it is strictly related to the author's writing style. Specifically, a (collection of) text(s) in natural language written by a given author is translated into a set of First-Order Logic descriptions, and a model of the author's writing habits is obtained as the result of clustering these descriptions. Then, if an overlapping exists between the models of a known author and of an unknown one, the conclusion can be drawn that they are the same person. Among the advantages of this approach, it does not need a training phase, and performs well also on short texts and/or small collections
WoMan: Logic-based Workflow Learning and Management
No full textWorkflow management is fundamental to efficiently,
effectively, and economically carry out complex working and
domestic activities. Manual engineering of workflow models is
a complex, costly, and error-prone task. The WoMan framework
for workflow management is based on first-order logic. Its core is
an automatic procedure that learns and refines workflow models
from observed cases of process execution. Its innovative peculiarities
include incrementality (allowing quick learning even in
the presence of noise and changed behavior), strict adherence to
the observed practices, ability to learn complex conditions for the
workflow components, and improved expressive power compared
to the state of the art. This paper presents the entire algorithmic
apparatus of WoMan, including translation and learning from
a standard log format for case representation, import/export of
workflow models from/into standard formalisms (Petri nets), and
exploitation of the learned models for process simulation and
monitoring. Qualitative and quantitative experimental evaluation
shows the power and efficiency of WoMan, both in controlled and
in real-world domains
Predicate Invention-based Specialization in Inductive Logic Programming
No full textThree relevant areas of interest in symbolic Machine Learning are incremental supervised learning, multistrategy learning and predicate invention. In many real-world tasks, new observations may point out the inadequacy of the learned model. In such a case, incremental approaches allow to adjust it, instead of learning a new model from scratch. Specifically, when a negative example is wrongly classified by a model, emph{specialization} refinement operators are needed. A powerful way to specialize a theory in Inductive Logic Programming is adding negated preconditions to concept definitions.
This paper describes an empowered specialization operator that allows to introduce the negation of conjunctions of preconditions using predicate invention.
An implementation of the operator is proposed, and experiments purposely devised to stress it prove that the proposed approach is correct and viable even under quite complex conditions
Automatic digital document processing and management: problems, algorithms and techniques
No full textThis text reviews the issues involved in handling and processing digital documents. Examining the full range of a document's lifetime, this book covers acquisition, representation, security, pre-processing, layout analysis, understanding, analysis of single components, information extraction, filing, indexing and retrieval. This title: provides a list of acronyms and a glossary of technical terms; contains appendices covering key concepts in machine learning, and providing a case study on building an intelligent system for digital document and library management; discusses issues of security
A Logic Framework for Incremental Learning of Process Models
No full textStandardized processes are important for correctly carrying out activities in an organization.
Often the procedures they describe are already in operation, and the need is to understand
and formalize them in a model that can support their analysis, replication and enforcement. Manually building these models is complex, costly and error-prone. Hence, the interest in automatically learning them from examples of actual procedures. Desirable options are incrementality in learning and adapting the models, and the ability to express triggers and conditions on the tasks that make up the workflow. This paper proposes a framework based on First-Order Logic that solves many shortcomings of previous approaches to this problem in the literature, allowing to deal with complex domains in a powerful and flexible way. Indeed, First-Order Logic provides a single, comprehensive and expressive representation and manipulation environment for supporting all of the above requirements. A purposely devised experimental evaluation confirms the effectiveness and efficiency of the proposed solution
A multi-strategy approach to structural analogy making
No full textAnalogy is the cognitive process of matching the characterizing
features of two dierent items. This may enable reuse of knowledge across do-
mains, which can be helpful to solve problems. Indeed, abstracting the `role'
of the features away from their specic embodiment in the single items is fun-
damental to recognize the possibility of an analogical mapping between them.
In some sense, similarity is a (simpler) kind of analogy. In order to do so, a
simple representation formalism could be the point of convergence for dier-
ent reasoning operators needed to reproduce general cognitive mechanisms.
Thus, using a formalism equivalent to graphs, this paper proposes the Roles
Argumentation-based Mapper, a multi-strategy operator, that suggests plau-
sible solutions obtained by exploring the entire search space. Applied to the
most critical classical examples in the literature, it proved to be able to solve
issues and to nd insightful analogies
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