11 research outputs found
Requirements for Supporting Individual Human Creativity in the Design Domain
International audienceCreativity is an important activity in many professional and leisure domains. This article presents a first step towards a system which will provide a set of tools for enhancing the individual creative abilities of the user in a design task. We have identified aspects which are characterise individual creativity: motivation, domain knowledge, externalization, inspiration and analogies, and requirements handling. Based on these aspects we have defined requirements and suggest associated system functionalities
Mining the Semantic Web
In the Semantic Web vision of the World Wide Web, content will not only be accessible
to humans but will also be available in machine interpretable form as ontological
knowledge bases. Ontological knowledge bases enable formal querying and reasoning
and, consequently, a main research focus has been the investigation of how deductive
reasoning can be utilized in ontological representations to enable more advanced
applications.
However, purely logic methods have not yet proven to be very effective for several
reasons: First, there still is the unsolved problem of scalability of reasoning to Web
scale. Second, logical reasoning has problems with uncertain information, which is
abundant on Semantic Web data due to its distributed and heterogeneous nature.
Third, the construction of ontological knowledge bases suitable for advanced reasoning
techniques is complex, which ultimately results in a lack of such expressive real-world
data sets with large amounts of instance data.
From another perspective, the more expressive structured representations open up
new opportunities for data mining, knowledge extraction and machine learning
techniques. If moving towards the idea that part of the knowledge already lies in the
data, inductive methods appear promising, in particular since inductive methods can
inherently handle noisy, inconsistent, uncertain and missing data. While there has
been broad coverage of inducing concept structures from less structured sources (text,
Web pages), like in ontology learning, given the problems mentioned above, we focus
on new methods for dealing with Semantic Web knowledge bases, relying on statistical
inference on their standard representations.
%\textsc{Comment: the last sentence makes no sense to me. Whoever knows what it
is supposed to mean: please improve}
We argue that machine learning research has to offer a wide variety of methods applicable to different expressivity levels of Semantic Web knowledge bases: Ranging
from weakly expressive but widely available knowledge bases in RDF to highly
expressive first-order knowledge bases, this paper surveys statistical approaches to
mining the Semantic Web. We specifically cover similarity and distance-based
methods, kernel machines, multivariate prediction models, relational graphical models
and first-order probabilistic learning approaches and discuss their applicability to
Semantic Web representations. Finally, we present selected experiments which were
conducted on Semantic Web mining tasks for some of the algorithms presented before.
This is intended to show the breadth and general potential of this exiting new research
and application area for data mining