Volume 26, 2007, No. 3
Updates of Logic Programs|
multidimensional logic programming, causal rejection principle, Kripke structure
Dynamic aspects of knowledge representation has been tackled recently by a
variety of approaches in the logic programming style. We consider the approaches
characterized by the causal rejection principle (if there is a conflict between
rules, then more preferred rules override those less preferred). A
classification and a comparison of the approaches is presented in the paper. We
compare them also to our own approach based on Kripke structures.
Computing and Informatics. Volume 26, 2007, No. 3: 225-238.
Extending Temporal Ontology with Uncertain Historical Time|
K. Matousek, M. Falc, Z. Kouba
ontology, temporal reasoning, inference, historical periods
Temporal ontology for representing uncertainly specified time periods is
presented. Mature approaches like Allen interval relations are combined with
introduction of time granularity and time uncertainty concepts. The ontology is
applicable both as a static data representation and for logical data inference.
Logical conclusions can be derived using an automated inference system.
Uncertainty parametrization was developed for handling the domain specific
uncertainty characteristics. Temporal statements containing the most frequent
expressions in the domain of cultural heritage preservation are identified and
categorized with respect to their accuracy. A temporal inference system is
implemented using OCML language. Consistency checks can find non-causal data
clusters and lead to improving current event data. Finally, resource annotation
with Dynamic Narrative Authoring Tool utilizing temporal inference is presented.
Computing and Informatics. Volume 26, 2007, No. 3: 239-254.
Modelling Web Service Composition for Deductive Web Mining|
V. Svatek, M. Vacura, A. ten Teije
web services, web mining, problem-solving methods, ontologies
Composition of simpler web services into custom applications is understood as
promising technique for information requests in a heterogeneous and changing
environment. This is also relevant for applications characterised as deductive
web mining (DWM). We suggest to use problem-solving methods (PSMs) as templates
for composed services. We developed a multi-dimensional, ontology-based
framework, and a collection of PSMs, which enable to characterise DWM
applications at an abstract level; we describe several existing applications in
this framework. We show that the heterogeneity and unboundedness of the web
demands for some modifications of the PSM paradigm used in the context of
traditional artificial intelligence. Finally, as simple proof of concept, we
simulate automated DWM service composition on a small collection of services,
PSM-based templates, data objects and ontological knowledge, all implemented in
Computing and Informatics. Volume 26, 2007, No. 3: 255-279.
Social Navigation for Semantic Web Applications Using Space Maps|
M. Bielikova, K. Matusikova
social navigation, personalized navigation, open information space, observation model, user model, space map, ontology
In this paper we deal with personalized navigation in an open information space.
Our aim is to support effective orientation in increasing amount of information
accessible through the Web. We present a method for personalized navigation
based on social navigation where the information space is represented by an
ontology. Navigational information is obtained by following user footsteps. It
is attached to information fragment mapped to the user goal and to description
of this goal using an ontology. This information is used later to show the way
to similar goals. We use ontology representation of the information space that
supports the effective navigation and the navigational ability to deal with
frequent changes of information content in open environments. We demonstrate the
proposed method in the context of developed software tool PENA for personalized
navigation support in labor supply domain.
Computing and Informatics. Volume 26, 2007, No. 3: 281-299.
Various Approaches to Web Information Processing|
K. Machova, P. Bednar, M. Mach
information extraction, document categorisation, boosting, predicted categories, click stream, kex word generation
The paper focuses on the field of automatic extraction of information from texts
and text document categorisation including pre-processing of text documents,
which can be found on the Internet. In the frame of the presented work, we have
devoted our attention to the following issues related to text categorisation:
increasing the precision of categorisation algorithm results with the aid of a
boosting method; searching a minimum number of decision trees, which enables the
improvement of the categorisation; the influence of unlabeled data with
predicted categories on categorisation precision; shortening click streams
needed to access a given web document; and generation of key words related with
a web document. The paper presents also results of experiments, which were
carried out using the 20 News Groups and Reuters-21578 collections of documents
and a collection of documents from an Internet portal of the Markiza
Computing and Informatics. Volume 26, 2007, No. 3: 301-327.
Lessons Learned from the ECML/PKDD Discovery Challenge on the Atherosclerosis Risk Factors Data|
P. Berka, J. Rauch, M. Tomeckova
atherosclerosis risk, data mining, discovery challenge
It becomes a good habit to organize a data mining cup, a competition or a
challenge at machine learning or data mining conferences. The main idea of the
Discovery Challenge organized at the European Conferences on Principles and
Practice of Knowledge Discovery in Databases since 1999 was to encourage a
collaborative research effort rather than a competition between data miners.
Different data sets have been used for the Discovery Challenge workshops during
the seven years. The paper summarizes our experience gained when organizing and
evaluating the Discovery Challenge on the atherosclerosis risk factor data.
Computing and Informatics. Volume 26, 2007, No. 3: 329-344.
dRAP-Independent: A Data Distribution Algorithm for Mining First-Order Frequent Patterns|
J. Blatak, L. Popelinsky
frequent patterns, inductive logic programming, parallel and distributed data mining, propositionalization
In this paper we present dRAP-Independent, an algorithm for independent
distributed mining of first-order frequent patterns. This system is based on
RAP, an algorithm for finding maximal frequent patterns in first-order logic.
dRAP-Independent utilizes a modified data partitioning schema introduced by
Savasere et al. and offers good performance and low communication overhead. We
analyze the performance of the algorithm on four different tasks: Mutagenicity
prediction -- a standard ILP benchmark, information extraction from biological
texts, context-sensitive spelling correction, and morphological disambiguation
of Czech. The results of the analysis show that the algorithm can generate more
patterns than the serial algorithm RAP in the same overall time.
Computing and Informatics. Volume 26, 2007, No. 3: 345-366.