J. Joachim Quantz

Knowledge-based disambiguation for machine translation

The resolution of ambiguities is one of the central problems for Machine Translation. In this paper we propose a knowledge-based approach to disambiguation which uses Description Logics (dl) as representation formalism. We present the process of anaphora resolution implemented in the Machine Translation systemfast and show how thedl systemback is used to support disambiguation.The disambiguation strategy uses factors representing syntactic, semantic, and conceptual constraints with different weights to choose the most adequate antecedent candidate. We show how these factors can be declaratively represented as defaults inback. Disambiguation is then achieved by determining the interpretation that yields a qualitatively minimal number of exceptions to the defaults, and can thus be formalized as exception minimization.

Deriving Inference Rules for Terminological Logics

Terminological Logics can be investigated under different perspectives. The aim of this paper is to provide the basis for a tighter combination of theoretical investigations with issues arising in the actual implementation of terminological representation systems. We propose to use inference rules, derived via the sequent calculus, as a new method for specifying terminological inference algorithms. This approach combines the advantages of the tableaux methods and the normalize-combine algorithms that have been predominant in terminological proof theory so far. We first show how a completeness proof for the inference rules of a relatively restricted terminological logic can be given. We then show how these inference rules can be used to construct normalize-compare algorithms and prove their completeness. Furthermore, these rules can be used in two ways for the characterization of terminological representation systems: first, the incompleteness of of systems can be documented by listing those rules that have not been implemented; second, the reasoning strategy can be described by spesifying which rules are applied forward and which backward.

Parallelizing Description Logics

Description Logics (DL), one of the major paradigms in Knowledge Representation, face efficiency problems due to large-scale applications, expressive dialects, or complete inference algorithms. In this paper we investigate the potential of parallelizing DL algorithms to meet this challenge. Instead of relying on a parallelism inherent in logic programming languages, we propose to exploit the application-specific potentials of DL and to use a more data-oriented parallelization strategy that is also applicable to imperative programming languages. We argue that object-level propagation is the most promising inference component for such a parallelization, as opposed to normalization, comparison, or classification.

Dialogue acts in automatic dialogue interpreting

In this paper we demonstrate that for an adequate translation of an utterance spoken in a dialogue the dialogue act it performs has to be determined. We introduce an approach that automatically assigns types of dialogue acts to utterances on the basis of both micro- and macro-structural information. Technically, this assignment is realized by modeling preference rules as weighted defaults in the Description Logic system FLEX. The dialogue-act type of an utterance is determined by qualitatively minimizing the exceptions to these defaults. The results described here have been developed within the VERBMOBIL project, a project concerned with face-to-face dialogue interpreting funded by the German Federal Ministry of Education, Science, Research and Technology (BMBF). We present the rather positive results of a first evaluation of this implementation showing the accuracy of dialogue act assignment.

On Intuitionistic Query Answering in Description Bases

In this paper we present a weak and a strong intuitionistic calculus for query answering in Description Logics (DL). Given the standard model-theoretic semantics for DL, a complete query-answering calculus has to perform complex case analyses to cope with implicit disjunctions stemming from some of the concept-forming operators in DL. To avoid this complexity we propose an intuitionistic approach to query answering based on the Sequent-Calculus-style axiomatization of DL we have developed in [20] and [21]. By taking into account only the intuitionistic inference schemata of this axiomatization, we obtain a strong intuitionistic query-answering calculus. An additional restriction to reasoning about explicit objects allows a further simplification of the proof theory and yields a weak intuitionistic calculus.

Weighted Defaults in Description Logics: Formal Properties and Proof Theory

We present a Preferential Default Description Logic (pddl) based on weighted defaults and show that the preferential entailment relation ∑ satisfies all properties of system P and Rational Monotonicity. The main characteristics of this pddl is that it uses an ordering between multisets of defaults for priorization, treats defaults similar to material implications, and adheres strictly to the principle of exception minimization.

Parallel propagation in the description-logic system FLEX*

In this paper we describe a parallel implementation of object-level propagation in the Description-Logic (DL) system FLEX. We begin by analyzing the parallel potential of the main DL inference algorithms normalization, subsumption checking, classification, and object-level propagation. Instead of relying on a parallelism inherent in logic programming languages, we propose to exploit the application-specific potentials of DLs and to use a more data-oriented parallelization strategy that is also applicable to imperative programming languages. Propagation turns out to be the most promising inference component for such a parallelization. We present two alternative PROLOG implementations of parallelized propagation on a loosely coupled MIMD (Multiple Instruction, Multiple Data) system, one based on a farm strategy, the other based on distributed objects . Evaluation based on benchmarks containing artificial examples shows that the farm strategy yields only poor results. The implementation based on distributed objects, on the other hand, achieves a considerable speed-up, in particular for large-size applications. We finally discuss the impact of these results for real applications.

The VERBMOBIL domain model version 1.0

This report describes the domain model used in the German Machine Translation project VERBMOBIL. In order make the design principles underlying the modeling explicit, we begin with a brief sketch of the VERBMOBIL demonstrator architecture from the perspective of the domain model. We then present some rather general considerations on the nature of domain modeling and its relationship to semantics. We claim that the semantic information contained in the model mainly serves two tasks. For one thing, it provides the basis for a conceptual transfer from German to English; on the other hand, it provides information needed for disambiguation. We argue that these tasks pose different requirements, and that domain modeling in general is highly task-dependent. A brief overview of domain models or ontologies used in existing NLP systems confirms this position. We finally describe the different parts of the domain model, explain our design decisions, and present examples of how the information contained in the model can be actually used in the VERBMOBIL demonstrator. In doing so, we also point out the main functionality of FLEX, the Description Logic system used for the modeling. The VERBMOBIL Domain Model Version 1.0

An HPSG parser based on Description Logics

In this paper I present a parser based on Description Logics (DL) for a German HPSG-Style fragment. The specified parser relies mainly on the inferential capabilities of the underlying DL system. Given a preferential default extension for DL disambiguation is achieved by choosing the parse containing a qualitatively minimal number of exceptions.

Wissenspräsentation und -repräsentation

Ausgehend von einer Trennung zwischen Wissensprasentation und -reprasentation in KL-ΟNE Systemen werden Probleme bei der graphischen Prasentation behandelt. Hierzu gehoren Informationseingrenzung, Navigation in grosen Netzen und Visualisierung von Strukturveranderungen. Es werden die in GROW realisierten Losungen vorgestellt und neue Anforderungen an die Reprasentation entwickelt.1