5,628 research outputs found
Ontology of core data mining entities
In this article, we present OntoDM-core, an ontology of core data mining
entities. OntoDM-core defines themost essential datamining entities in a three-layered
ontological structure comprising of a specification, an implementation and an application
layer. It provides a representational framework for the description of mining
structured data, and in addition provides taxonomies of datasets, data mining tasks,
generalizations, data mining algorithms and constraints, based on the type of data.
OntoDM-core is designed to support a wide range of applications/use cases, such as
semantic annotation of data mining algorithms, datasets and results; annotation of
QSAR studies in the context of drug discovery investigations; and disambiguation of
terms in text mining. The ontology has been thoroughly assessed following the practices
in ontology engineering, is fully interoperable with many domain resources and
is easy to extend
A review on the application of evolutionary computation to information retrieval
In this contribution, different proposals found in the specialized literature for the
application of evolutionary computation to the field of information retrieval will be
reviewed. To do so, different kinds of IR problems that have been solved by evolutionary
algorithms are analyzed. Some of the specific existing approaches will be specifically
described for some of these problems and the obtained results will be critically
evaluated in order to give a clear view of the topic to the reader.CICYT under project TIC2002-03276University of Granada under project ‘‘Mejora de Metaheur ısticas mediante Hibridaci on y sus
Aplicaciones
A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling
This paper presents a survey of some fuzzy linguistic information access systems. The review shows
information retrieval systems, filtering systems, recommender systems, and web quality evaluation tools,
which are based on tools of fuzzy linguistic modelling. The fuzzy linguistic modelling allows us to
represent and manage the subjectivity, vagueness and imprecision that is intrinsic and characteristic of the
processes of information searching, and, in such a way, the developed systems allow users the access to
quality information in a flexible and user-adapted way.European Union (EU)
TIN2007-61079
PET2007-0460Ministry of Public Works
90/07Excellence Andalusian Project
TIC529
Learning Task Specifications from Demonstrations
Real world applications often naturally decompose into several sub-tasks. In
many settings (e.g., robotics) demonstrations provide a natural way to specify
the sub-tasks. However, most methods for learning from demonstrations either do
not provide guarantees that the artifacts learned for the sub-tasks can be
safely recombined or limit the types of composition available. Motivated by
this deficit, we consider the problem of inferring Boolean non-Markovian
rewards (also known as logical trace properties or specifications) from
demonstrations provided by an agent operating in an uncertain, stochastic
environment. Crucially, specifications admit well-defined composition rules
that are typically easy to interpret. In this paper, we formulate the
specification inference task as a maximum a posteriori (MAP) probability
inference problem, apply the principle of maximum entropy to derive an analytic
demonstration likelihood model and give an efficient approach to search for the
most likely specification in a large candidate pool of specifications. In our
experiments, we demonstrate how learning specifications can help avoid common
problems that often arise due to ad-hoc reward composition.Comment: NIPS 201
A Database Approach to Content-based XML retrieval
This paper describes a rst prototype system for content-based retrieval from XML data. The system's design supports both XPath queries and complex information retrieval queries based on a language modelling approach to information retrieval. Evaluation using the INEX benchmark shows that it is beneficial if the system is biased to retrieve large XML fragments over small fragments
A fuzzy approach to similarity in Case-Based Reasoning suitable to SQL implementation
The aim of this paper is to formally introduce a notion of acceptance and similarity,
based on fuzzy logic, among case features in a case retrieval system. This is pursued
by rst reviewing the relationships between distance-based similarity (i.e. the
standard approach in CBR) and fuzzy-based similarity, with particular attention
to the formalization of a case retrieval process based on fuzzy query specication.
In particular, we present an approach where local acceptance relative to a feature
can be expressed through fuzzy distributions on its domain, abstracting the actual
values to linguistic terms. Furthermore, global acceptance is completely grounded
on fuzzy logic, by means of the usual combinations of local distributions through
specic dened norms. We propose a retrieval architecture, based on the above notions
and realized through a fuzzy extension of SQL, directly implemented on a
standard relational DBMS. The advantage of this approach is that the whole power
of an SQL engine can be fully exploited, with no need of implementing specic
retrieval algorithms. The approach is illustrated by means of some examples from
a recommender system called MyWine, aimed at recommending the suitable wine
bottles to a customer providing her requirements in both crisp and fuzzy way
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