122,548 research outputs found
Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in Meta-Mining
The notion of meta-mining has appeared recently and extends the traditional
meta-learning in two ways. First it does not learn meta-models that provide
support only for the learning algorithm selection task but ones that support
the whole data-mining process. In addition it abandons the so called black-box
approach to algorithm description followed in meta-learning. Now in addition to
the datasets, algorithms also have descriptors, workflows as well. For the
latter two these descriptions are semantic, describing properties of the
algorithms. With the availability of descriptors both for datasets and data
mining workflows the traditional modelling techniques followed in
meta-learning, typically based on classification and regression algorithms, are
no longer appropriate. Instead we are faced with a problem the nature of which
is much more similar to the problems that appear in recommendation systems. The
most important meta-mining requirements are that suggestions should use only
datasets and workflows descriptors and the cold-start problem, e.g. providing
workflow suggestions for new datasets.
In this paper we take a different view on the meta-mining modelling problem
and treat it as a recommender problem. In order to account for the meta-mining
specificities we derive a novel metric-based-learning recommender approach. Our
method learns two homogeneous metrics, one in the dataset and one in the
workflow space, and a heterogeneous one in the dataset-workflow space. All
learned metrics reflect similarities established from the dataset-workflow
preference matrix. We demonstrate our method on meta-mining over biological
(microarray datasets) problems. The application of our method is not limited to
the meta-mining problem, its formulations is general enough so that it can be
applied on problems with similar requirements
Exploiting conceptual spaces for ontology integration
The widespread use of ontologies raises the need to integrate distinct conceptualisations. Whereas the symbolic approach of established representation standards – based on first-order logic (FOL) and syllogistic reasoning – does not implicitly represent semantic similarities, ontology mapping addresses this problem by aiming at establishing formal relations between a set of knowledge entities which represent the same or a similar meaning in distinct ontologies. However, manually or semi-automatically identifying similarity relationships is costly. Hence, we argue, that representational facilities are required which enable to implicitly represent similarities. Whereas Conceptual Spaces (CS) address similarity computation through the representation of concepts as vector spaces, CS rovide neither an implicit representational mechanism nor a means to represent arbitrary relations between concepts or instances. In order to overcome these issues, we propose a hybrid knowledge representation approach which extends FOL-based ontologies with a conceptual grounding through a set of CS-based representations. Consequently, semantic similarity between instances – represented as members in CS – is indicated by means of distance metrics. Hence, automatic similarity detection across distinct ontologies is supported in order to facilitate ontology integration
HeteroGenius: A Framework for Hybrid Analysis of Heterogeneous Software Specifications
Nowadays, software artifacts are ubiquitous in our lives being an essential
part of home appliances, cars, cell phones, and even in more critical
activities like aeronautics and health sciences. In this context software
failures may produce enormous losses, either economical or, in the worst case,
in human lives. Software analysis is an area in software engineering concerned
with the application of diverse techniques in order to prove the absence of
errors in software pieces. In many cases different analysis techniques are
applied by following specific methodological combinations that ensure better
results. These interactions between tools are usually carried out at the user
level and it is not supported by the tools. In this work we present
HeteroGenius, a framework conceived to develop tools that allow users to
perform hybrid analysis of heterogeneous software specifications.
HeteroGenius was designed prioritising the possibility of adding new
specification languages and analysis tools and enabling a synergic relation of
the techniques under a graphical interface satisfying several well-known
usability enhancement criteria. As a case-study we implemented the
functionality of Dynamite on top of HeteroGenius.Comment: In Proceedings LAFM 2013, arXiv:1401.056
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