Gerasimos Lampouras

Generating natural language descriptions from OWL ontologies: the natural OWL system

We present Naturalowl, a natural language generation system that produces texts describing individuals or classes of owl ontologies. Unlike simpler owl verbalizers, which typically express a single axiom at a time in controlled, often not entirely fluent natural language primarily for the benefit of domain experts, we aim to generate fluent and coherent multi-sentence texts for end-users. With a system like Naturalowl, one can publish information in owl on the Web, along with automatically produced corresponding texts in multiple languages, making the information accessible not only to computer programs and domain experts, but also end-users. We discuss the processing stages of Naturalowl, the optional domain-dependent linguistic resources that the system can use at each stage, and why they are useful. We also present trials showing that when the domain-dependent linguistic resources are available, Naturalowl produces significantly better texts compared to a simpler verbalizer, and that the resources can be created with relatively light effort.

An Open-Source Natural Language Generator for OWL Ontologies and its Use in Protege and Second Life

We demonstrate an open-source natural language generation engine that produces descriptions of entities and classes in English and Greek from OWL ontologies that have been annotated with linguistic and user modeling information expressed in RDF. We also demonstrate an accompanying plug-in for the Protege ontology editor, which can be used to create the ontologys annotations and generate previews of the resulting texts by invoking the generation engine. The engine has been embedded in robots acting as museum tour guides in the physical world and in Second Life; here we demonstrate the latter application.

Adaptive Natural Language Interaction

The subject of this demonstration is natural language interaction, focusing on adaptivity and profiling of the dialogue management and the generated output (text and speech). These are demonstrated in a museum guide use-case, operating in a simulated environment. The main technical innovations presented are the profiling model, the dialogue and action management system, and the text generation and speech synthesis systems.

Finding Short Definitions of Terms on Web Pages

We present a system that finds short definitions of terms on Web pages. It employs a Maximum Entropy classifier, but it is trained on automatically generated examples; hence, it is in effect unsupervised. We use rouge-w to generate training examples from encyclopedias and Web snippets, a method that outperforms an alternative centroid-based one. After training, our system can be used to find definitions of terms that are not covered by encyclopedias. The system outperforms a comparable publicly available system, as well as a previously published form of our system.