Abstract-Band - Fakultät für Informatik, TU Wien - Technische ...

genug sind, um funktionale Abhängigkeiten in DLs zu modellieren. Deshalb erweitern
wir pIdCs zu tree-based identification constraints. Mittels diesen
untersuchen wir Redundanzen in DL KBs. Der Hauptbeitrag dieser Arbeit ist
eine Definition der Beschreibungslogik Normalform (DLNF), welche eine sinnesgetreue
Erweiterung der BCNF zu DLs ist. Zusätzlich stellen wir eine direkte Abbildung
von relationalen Schemata auf DL KBs vor. Wir zeigen, dass jedes relationale
Schema genau dann in BCNF ist, wenn auch die direkte Abbildung
dieses Schemas auf eine DL KB in DLNF ist und umgekehrt.
Iraklis Kordomatis
Machine Learning Algorithms for Visual Pattern Detection on Web Pages
Studium: Masterstudium Information & Knowledge Management
BetreuerIn: Univ.Prof. Dr. Reinhard Pichler
In this thesis the question how to robustly identify web objects across different
sites is tackled. TAMCROW introduces a novel approach exploiting visually
perceivable characteristics of a web object and its surrounding objects. This approach
is entirely independent of textual labels, and hence has the noteworthy
advantage of being language-agnostic. Another main advantage of the visual
detection approach is sample parsimony. Fewer examples are required for the
learning process to learn how to find certain web objects on previously
unknown pages. Moreover, visual cues are crucial for the human perception
and as a consequence also for the usability of a web page. Therefore, web
designers create web pages coherent with the human perception in order to
yield a high usability. Supervised machine learning techniques are applied for
the object identification process. The knowledge is limited to features representing
the visual appearance of the different web objects. An additional
question is whether it is possible to predict the role of a web object by its visual
appearance which is formally a classification problem. Within the scope of this
master thesis, the following machine learning techniques are investigated in
detail: logistic regression, k~nearest-neighbor, classification trees (in particular,
c4.5 of Quinlan) and support vector machines. The evaluation results are
illustrated in chapter \ref{ch:evaluationResults} indicating that the approach
developed within the TAMCROW project is very fruitful. The workflow on web
object identification is evaluated with different scenarios. These scenarios
include searches for train, bus and flight connections as well as for accommodations.
K-page cross-validation is used as evaluation technique. The mean
precision is chosen as performance measure, since it fits best for the used
scenarios. The results are significant for all classification techniques.
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