Robust Dialog Management Through A Context-centric Architecture

This dissertation presents and evaluates a method of managing spoken dialog interactions with a robust attention to fulfilling the human user’s goals in the presence of speech recognition limitations. Assistive speech-based embodied conversation agents are computer-based entities that interact with humans to help accomplish a certain task or communicate information via spoken input and output. A challenging aspect of this task involves open dialog, where the user is free to converse in an unstructured manner. With this style of input, the machine’s ability to communicate may be hindered by poor reception of utterances, caused by a user’s inadequate command of a language and/or faults in the speech recognition facilities. Since a speech-based input is emphasized, this endeavor involves the fundamental issues associated with natural language processing, automatic speech recognition and dialog system design. Driven by ContextBased Reasoning, the presented dialog manager features a discourse model that implements mixed-initiative conversation with a focus on the user’s assistive needs. The discourse behavior must maintain a sense of generality, where the assistive nature of the system remains constant regardless of its knowledge corpus. The dialog manager was encapsulated into a speech-based embodied conversation agent platform for prototyping and testing purposes. A battery of user trials was performed on this agent to evaluate its performance as a robust, domain-independent, speech-based interaction entity capable of satisfying the needs of its users

Creating ontologies for content representation - the OntoSeed suite

Bontas Simperl EP, Schlangen D. Creating ontologies for content representation - the OntoSeed suite. In: Spaccapietra S, ed. Journal on Data Semantics 9 (u.a.: 4th International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE 2005). Lecture Notes in Computer Science, 4601. Berlin u.a.: Springer; 2007: 141-166

Creating ontologies for content representation–-the OntoSeed suite

Bontas Simperl EP, Schlangen D, Schrader T. Creating ontologies for content representation–-the OntoSeed suite. In: Meersman R, ed. Proceedings of the CoopIS/DOA/ODBASE. Berlin, Heidelberg: Springer Verlag; 2005: 924

Creating ontologies for content representation—the OntoSeed suite

Abstract. Due to the inherent difficulties associated with manual ontology building, knowledge acquisition approaches such as ontology reuse or ontology learning from texts are often seen as instruments that can make this tedious process easier. In this paper we present a NLP-based method to aid ontology design in a specific application scenario, namely that in which the resulting ontology is used to support the semantic annotation of text documents. The proposed method uses the World Wide Web in its analysis of the domain-specific documents, thereby greatly reducing the need for linguistic expertise and resources, and suggests ways to specify domain ontologies in a “linguistics-friendly ” format in order to improve further ontology-based natural language processing tasks such as semantic annotation. We present a thorough evaluation of the method, using corpora from three diverse real-world settings (medical information, tourism, and recipes). Additionally, for the first scenario we compare the costs and the benefits of the NLP-based ontology engineering approach against a similar, reuse-oriented experiment.