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

ontology matchers to be combined and used together, exploiting their
respective strengths in a combined matching approach. In the present thesis,
we present a novel architecture for combining off-the-shelf ontology matchers
based on iterative calls and exchanging information in the form of reference
alignments. Unfortunately though, only a few of the matchers contesting in the
past years' OAEI campaigns actually allow the provision of reference alignments
in the standard OAEI alignment format to support such a combined
matching process. We bypass this lacking functionality by introducing an alternative
approach for aligning results of different ontology matchers using
simple URI replacement to “emulate” reference alignments in the aligned ontologies.
While some matchers still consider classes and properties in ontologies
aligned in such fashion as different, we experimentally prove that our iterative
approach benefits from this emulation, achieving the best results in terms of F-
measure on parts of the OAEI benchmark suite, compared to the single results
of the competing matchers as well as their combined results. The new
combined matcher -- Mix'n'Match -- integrates different matchers in a multithreaded
architecture and provides an anytime behaviour in the sense that it
can be stopped anytime with the best combined matchings found so far.
Gerald Weidinger
OMiGA An Open Minded Grounding on-the-fly Answer Set Solver
Studium: Masterstudium Computational Intelligence
BetreuerIn: O.Univ.Prof. Dr. Thomas Eiter
Answer Set Programming (ASP) is nowadays a well-known and well acknowledged
declarative problem solving paradigm. ASP originates from the area of
artificial intelligence and is often used in knowledge-based reasoning and logic
programming, where its main purpose is solving search and optimization problems.
There are many solvers that solve ASP in a two-step manner, where they
first ground the input program and then in the second step solve the resulting
grounded program. From this approach one problem arises: the exponential
blowup. In order to face this problem some answer set solvers use a grounding
on-the-fly approach where rules are only grounded when needed during
solving. Solvers that use this approach show that they indeed can solve some
instances where standard solvers run out of memory. But on problem instances
where exponential blow up is no issue, these solvers are much slower than
standard solvers, because they have troubles finding applicable rules. Within
this thesis we present the OMiGA solver, which combines the grounding onthe-fly
approach with a Rete network that enables us to efficiently find those
rules and therefore outperform other grounding on-the-fly solvers and even
compare to standard solvers on certain instances.
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