Ontology-based Information Extraction with SOBA

In this paper we describe SOBA, a sub-component of the SmartWeb multi-modal dialog system. SOBA is a component for ontologybased information extraction from soccer web pages for automatic population of a knowledge base that can be used for domainspecific question answering. SOBA realizes a tight connection between the ontology, knowledge base and the information extraction component. The originality of SOBA is in the fact that it extracts information from heterogeneous sources such as tabular structures, text and image captions in a semantically integrated way. In particular, it stores extracted information in a knowledge base, and in turn uses the knowledge base to interpret and link newly extracted information with respect to already existing entities

Recent developments for the linguistic linked open data infrastructure

In this paper we describe the contributions made by the European H2020 project “Pret-a-LLOD” (‘Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors’) to the further development of the Linguistic Linked Open Data (LLOD) infrastructure. Pret-a-LLOD aims to develop a new methodology for building data value chains applicable to a wide range of sectors and applications and based around language resources and language technologies that can be integrated by means of semantic technologies. We describe the methods implemented for increasing the number of language data sets in the LLOD. We also present the approach for ensuring interoperability and for porting LLOD data sets and services to other infrastructures, as well as the contribution of the projects to existing standards

SOBA: SmartWeb Ontology-based Annotation

Buitelaar P, Cimiano P, Frank A, Racioppa S. SOBA: SmartWeb Ontology-based Annotation. In: Proceedings of the Demo Session at the International Semantic Web Conference (ISWC). 2006

Ontology-based Information Extraction with SOBA

Buitelaar P, Cimiano P, Racioppa S, Siegel M. Ontology-based Information Extraction with SOBA. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC). ELRA; 2006: 2321-2324

Ontology-based Information Extraction and Integration from Heterogeneous Data Sources

Buitelaar P, Cimiano P, Frank A, Hartung M, Racioppa S. Ontology-based Information Extraction and Integration from Heterogeneous Data Sources. International Journal of Human Computer Studies (JHCS). 2008;66(11):759-788

LingInfo: Design and Applications of a Model for the Integration of Linguistic Information in Ontologies

Buitelaar P, Declerck T, Frank A, et al. LingInfo: Design and Applications of a Model for the Integration of Linguistic Information in Ontologies. In: Proceedings of the OntoLex Workshop at LREC. ELRA; 2006: 28-32

Persistent Expectation Management in Human-Robot Teaming (WP5)

We report Year 4 progress in the TRADR project WP5: Persistent models for human-robot teaming. We focused on the analysis, modelling and online-processing of the information-gathering tasks that the human-robot team is performing during a mission, with the goal to enable the robotic system to follow the mission (understand which tasks have been assigned to whom, what the progress is) and provide support for the management of the activities through the agent system and based on the working agreements. The reported work includes further development of team communication processing, ontology modelling, task management support, working agreements. The developed modules are integrated in the TRADR system and were evaluated during the TRADR evaluation exercise

Ontology Engineering for the Design and Implementation of Personal Pervasive Lifestyle Support

ABSTRACT The PAL project 1 is developing an embodied conversational agent (robot and its avatar), and applications for child-agent activities that help children from 8 to 14 years old to acquire the required knowledge, skills, and attitude for adequate diabetes selfmanagement. Formal and informal caregivers can use the PAL system to enhance their supportive role for this self-management learning process. We are developing a common ontology (i) to support normative behavior in a flexible way, (ii) to establish mutual understanding in the human-agent system, (iii) to integrate and utilize knowledge from the application and scientific domains, and (iv) to produce sensible human-agent dialogues. The common ontology is constructed by relating and integrating partly existing separate ontologies that are specific to certain contexts or domains. This paper presents the general vision, approach, and state of the art

Ontology Engineering for the Design and Implementation of Personal Pervasive Lifestyle Support

The PAL project1 is developing an embodied conversational agent (robot and its avatar), and applications for child-agent activities that help children from 8 to 14 years old to acquire the required knowledge, skills, and attitude for adequate diabetes selfmanagement. Formal and informal caregivers can use the PAL system to enhance their supportive role for this self-management learning process. We are developing a common ontology (i) to support normative behavior in a flexible way, (ii) to establish mutual understanding in the human-agent system, (iii) to integrate and utilize knowledge from the application and scientific domains, and (iv) to produce sensible human-agent dialogues. The common ontology is constructed by relating and integrating partly existing separate ontologies that are specific to certain contexts or domains. This paper presents the general vision, approach, and state of the art.Interactive Intelligenc

Ontology Engineering for the Design and Implementation of Personal Pervasive Lifestyle Support

<p>The PAL project1 is developing an embodied conversational agent (robot and its avatar), and applications for child-agent activities that help children from 8 to 14 years old to acquire the required knowledge, skills, and attitude for adequate diabetes selfmanagement. Formal and informal caregivers can use the PAL system to enhance their supportive role for this self-management learning process. We are developing a common ontology (i) to support normative behavior in a flexible way, (ii) to establish mutual understanding in the human-agent system, (iii) to integrate and utilize knowledge from the application and scientific domains, and (iv) to produce sensible human-agent dialogues. The common ontology is constructed by relating and integrating partly existing separate ontologies that are specific to certain contexts or domains. This paper presents the general vision, approach, and state of the art.</p