Robert Porzel

DOLCE ergo SUMO: On foundational and domain models in the SmartWeb Integrated Ontology (SWIntO)

Increased availability of mobile computing, such as personal digital assistants (PDAs), creates the potential for constant and intelligent access to up-to-date, integrated and detailed information from the Web, regardless of ones actual geographical position. Intelligent question-answering requires the representation of knowledge from various domains, such as the navigational and discourse context of the user, potential user questions, the information provided by Web services and so on, for example in the form of ontologies. Within the context of the SmartWeb project, we have developed a number of domain-specific ontologies that are relevant for mobile and intelligent user interfaces to open-domain question-answering and information services on the Web. To integrate the various domain-specific ontologies, we have developed a foundational ontology, the SmartSUMO ontology, on the basis of the DOLCE and SUMO ontologies. This allows us to combine all the developed ontologies into a single SmartWeb Integrated Ontology (SWIntO) having a common modeling basis with conceptual clarity and the provision of ontology design patterns for modeling consistency. In this paper, we present SWIntO, describe the design choices we made in its construction, illustrate the use of the ontology through a number of applications, and discuss some of the lessons learned from our experiences.

Less is more: using a single knowledge representation in dialogue systems

The approach to knowledge representation taken in a multi-modal multi-domain dialogue system - SMARTKOM - is presented. We focus on the ontological and representational issues and choices helping to construct an ontology, which is shared by multiple components of the system, can be re-used in different projects and applied to various tasks. Finally, examples highlighting the usefulness of our approach are given.

Towards Ontology-Based Multiagent Simulations: The Plasma Approach.

In multiagent-based simulation systems the agent programming paradigm is adopted for simulation. This simulation approach offers the promise to facilitate the design and development of complex simulations, both regarding the distinct simulation actors and the simulation environment itself. We introduce the simulation middleware PlaSMA which extends the JADE agent framework with a simulation control that ensures synchronization and provides a world model based on a formal ontological description of the respective application domain. We illustrate the benefits of an ontology grounding for simulation design and discuss further gains to be expected from recent advances in ontology engineering, namely the adaption of foundational ontologies and modelling-patterns.

Semantic coherence scoring using an ontology

In this paper we present ONTOSCORE, a system for scoring sets of concepts on the basis of an ontology. We apply our system to the task of scoring alternative speech recognition hypotheses (SRH) in terms of their semantic coherence. We conducted an annotation experiment and showed that human annotators can reliably differentiate between semantically coherent and incoherent speech recognition hypotheses. An evaluation of our system against the annotated data shows that, it successfully classifies 73.2% in a German corpus of 2.284 SRHs as either coherent or incoherent (given a baseline of 54.55%).

Contextual coherence in natural language processing

Controlled and restricted dialogue systems are reliable enough to be deployed in various real world applications. The more conversational a dialogue system becomes, the more difficult and unreliable become recognition and processing. Numerous research projects are struggling to overcome the problems arising with more- or truly conversational dialogue system. We introduce a set of contextual coherence measurements that can improve the reliability of spoken dialogue systems, by including contextual knowledge at various stages in the natural language processing pipeline. We show that, situational knowledge can be successfully employed to resolve pragmatic ambiguities and that it can be coupled with ontological knowledge to resolve semantic ambiguities and to choose among competing automatic speech recognition hypotheses.

An Agent-based Approach to Autonomous Logistic Processes Collaborative Research Centre 637: Autonomous Cooperating Logistic Processes

This paper presents the research activities of the Collaborative Research Centre (CRC) 637 “Autonomous Cooperating Logistic Processes—A Paradigm Shift and its Limitations” at the University of Bremen. After a motivation of autonomous logistics as an answer to current trends in increasingly dynamic markets, we sketch the structure and aims of the interdisciplinary CRC. We present several interpretations of the central motive of autonomous control, pursued by sub-projects over the course of the first project period, and focus on an agent-based approach to autonomous logistics.

Automatic creation of interface specifications from ontologies

The paper presents a system architecture for the automatic generation of interface specifications from ontologies. The ensuing interfaces (XML schema definitions) preserve a significant amount of the knowledge originally encoded in the ontology. The approach is relevant for the engineering of large-scale language technology systems. It has been successfully deployed in a complex multi-modal dialogue system SMARTKOM.

Annotating the Semantic Consistency of Speech Recognition Hypotheses

Recent work on natural language processing systems is aimed at more conversational, context-adaptive systems in multiple domains. An important requirement for such a system is the automatic detection of the domain and a domain consistency check of the given speech recognition hypotheses. We report a pilot study addressing these tasks, the underlying data collection and investigate the feasibility of annotating the data reliably by human annotators.

In Context: Integrating Domain- and Situation-Specific Knowledge

We describe the role of context models in natural language processing systems and their implementation and evaluation in the SmartKom system. We show that contextual knowledge is needed for an ensemble of tasks, such as lexical and pragmatic disambiguation, decontextualizion of domain and common-sense knowledge that was left implicit by the user and for estimating an overall coherence score that is used in intention recognition. As the successful evaluations show, the implemented context model enables a multicontext system, such as SmartKom, to respond felicitously to contextually underspecified questions. This ability constitutes an important step toward making dialogue systems more intuitively usable and conversational without losing their reliability and robustness.

Modeling domain knowledge : know-how and know-what

The approach to knowledge representation taken in the multimodal, multidomain, and multiscenario dialogue system — SmartKom — is presented. We focus on the ontological and representational issues and choices helping to construct an ontology, which is shared by multiple components of the system and can be reused in different projects and applied to various tasks. Finally, two applications of the ontology that highlight the usefulness of our approach are described.