Decomposition Techniques for Subgraph Matching

In the constraint programming framework, state-of-the-art static and dynamic decomposition techniques are hard to apply to problems with complete initial constraint graphs. For such problems, we propose a hybrid approach of these techniques in the presence of global constraints. In particular, we solve the subgraph isomorphism problem. Further we design specific heuristics for this hard problem, exploiting its special structure to achieve decomposition. The underlying idea is to precompute a static heuristic on a subset of its constraint network, to follow this static ordering until a first problem decomposition is available, and to switch afterwards to a fully propagated, dynamically decomposing search. Experimental results show that, for sparse graphs, our decomposition method solves more instances than dedicated, state-of-the-art matching algorithms or standard constraint programming approaches.

Controlling Functional Uncertainty

There have been two different methods for checking the satisfiability of feature descriptions that use the functional uncertainty device, namely~\cite{Kaplan:88CO} and \cite{Backofen:94JSC}. Although only the one in \cite{Backofen:94JSC} solves the satisfiability problem completely, both methods have their merits. But it may happen that in one single description, there are parts where the first method is more appropriate, and other parts where the second should be applied. In this paper, we present a common framework that allows one to combine both methods. This is done by presenting a set of rules for simplifying feature descriptions. The different methods are described as different controls on this rule set, where a control specifies in which order the different rules must be applied.

DISCO---An HPSG-based NLP System and its Application for Appointment Scheduling

The natural language system DISCO is described. It combines o a powerful and flexible grammar development system; o linguistic competence for German including morphology, syntax and semantics; o new methods for linguistic performance modelling on the basis of high-level competence grammars; o new methods for modelling multi-agent dialogue competence; o an interesting sample application for appointment scheduling and calendar management.

A Complete and Recursive Feature Theory

Various feature descriptions are being employed in logic programming languages and constrained-based grammar formalisms. The common notational primitive of these descriptions are functional attributes called features. The descriptions considered in this paper are the possibly quantified first-order formulae obtained from a signature of binary and unary predicates called features and sorts, respectively. We establish a first-order theory FT by means of three axiom schemes, show its completeness, and construct three elementarily equivalent models. One of the models consists of so-called feature graphs, a data structure common in computational linguistics. The other two models consist of so-called feature trees, a record-like data structure generalizing the trees corresponding to first-order terms. Our completeness proof exhibits a terminating simplification system deciding validity and satisfiability of possibly quantified feature descriptions.