Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Actias system: supervised multi-strategy learning paradigm using
  categorical logic   One of the most difficult problems in the development of intelligent systems
is the construction of the underlying knowledge base. As a consequence, the
rate of progress in the development of this type of system is directly related
to the speed with which knowledge bases can be assembled, and on its quality.
We attempt to solve the knowledge acquisition problem, for a Business
Information System, developing a supervised multistrategy learning paradigm.
This paradigm is centred on a collaborative data mining strategy, where groups
of experts collaborate using data-mining process on the supervised acquisition
of new knowledge extracted from heterogeneous machine learning data models.
  The Actias system is our approach to this paradigm. It is the result of
applying the graphic logic based language of sketches to knowledge integration.
The system is a data mining collaborative workplace, where the Information
System knowledge base is an algebraic structure. It results from the
integration of background knowledge with new insights extracted from data
models, generated for specific data modelling tasks, and represented as rules
using the sketches language.

34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci