Abstract. The main contribution of this paper is an initial method for automatically exploiting ontology design patterns with the aim of further automating the creation of enterprise ontologies in small-scale application contexts. The focus is so far on developing a fully automated construction method, thereby somewhat reducing the requirements on ontology customization and level of detail. In this paper we present an approach how to use knowledge (patterns) from other areas, like data modeling, knowledge reuse, software analysis and software design, to create ontology patterns. These design patterns are then used within our method for automatically matching and pruning them, in accordance with information extracted from existing knowledge sources within the company in question. Though the method still needs some fine-tuning, it has already been used when creating an enterprise ontology for a suppliercompany within the automotive industry. 1

Abstract A domain ontology represents a shared understanding of a given sector of reality (for instance, mathematics, economics, tourism etc.). Many application fields like Information Retrieval, Information Extraction and so on, as long as the Semantic Web [2], the next Web generation, need this kind of structured domain knowledge in order to add the missing semantic layout. However, an accurate search through the Internet shows the lack of large domain ontologies available to the community. In fact, building such knowledge resources requires big efforts in terms of time, costs and work due to the difficulty in identifying and properly defining domain concepts and their inter-relationships. One primary problem in this process is to establish an appropriate is-a hierarchy for the ontology. To this end, general-purpose lexical resources like WordNet [3] can be of help because they code a massive, although nonspecific, quantity of knowledge. This paper shows an original solution to the problem of building an is-a hierarchy for a domain ontology. This is achieved through the automatic enrichment and reorganization of the WordNet hierarchy by properly adding domain knowledge structured in the form of concept trees. I.

Purpose: This paper presents a framework for ontology evolution tailored to Digital Libraries, which makes use of two different sources for change detection and propagation, the usage of ontologies by users and the changes of available data.

Ontologies have been widely accepted as the most advanced knowledge representation model. They are among the most important building blocks of semantic web, hence, very crucial for the success of semantic web. This paper discusses a fast and efficient method to facilitate the evaluation and enrichment of domain ontologies using a text-mining approach. We exploit domain specific texts and glossaries or dictionaries in order to automatically generate g-groups and fgroups. These groups are sets of concepts/terms which have either taxonomic or non-taxonomic relationships among them. The domain expert ontology engineer reviews these generated groups and uses them to evaluate and enrich the domain ontology. We have developed an extensive and detailed ontology in the field of environmental science using this approach in interaction with domain expert. Empirical results show that our approach can support domain expert ontology engineers in building domain specific ontologies efficiently. Keywords ontology enrichment, text mining, clustering, feature groups 1.

Marketing has faced new challenges over the past decade. The days of the mass market are definitely over. Consumers now are exposed to numerous cable channels and satellite channels. Many people do not get their information from TV at all, but use Web sites. The population has also developed. Minorities have grown and asserted their own tastes and

Abstract Things change. Words change, meanings and context change. To manage a large volume of evolving bio-medical data of various types, one needs to employ several techniques from areas such as knowledge representation, semantic web and databases. Many of these techniques require a formal description of a part of the real world. Ontologies can provide a set of shared and precisely defined terms in various degrees of formality to describe a particular domain of interest. When the knowledge changes, then the related definitions will be altered. Changes to ontologies may occur for many reasons. The issues arising from ontological change can affect the validity of information in applications that are tightly bound to concepts in a particular ontological context. Many knowledge-based systems are now reaching a stage where they need a change management strategy to update their ontological knowledge. This area is becoming increasingly important in science as high throughput techniques frequently necessitate updates to existing scientific 'truths'. In this chapter, we survey and review state of the art change management in bio-ontologies as well as some of the available tools and techniques in this area. We also survey various potential changes in biomedical ontologies, with actual examples from some of the most popular ontologies in the biomedical domain. In addition we investigate the potential of some of the advanced formalisms in this context by proposing our formal method for analyzing and supporting ontology evolution and change management. 1

An increasing number of approaches for ontology engineering from text are gearing towards the use of online sources such as company intranet and the World Wide Web. Despite such rise, not much work can be found in aspects of preprocessing and cleaning dirty texts from online sources. This paper presents an enhancement of an Integrated Scoring for Spelling error correction, Abbreviation expansion and Case restoration (ISSAC). ISSAC is implemented as part of a text preprocessing phase in an ontology engineering system. New evaluations performed on the enhanced ISSAC using 700 chat records reveal an improved accuracy of 98 % as compared to 96.5 % and 71 % based on the use of only basic ISSAC and of Aspell, respectively.

Deliverable D7.1.1.a (WP7.1) This informal deliverable aims to provide use cases for SEKT. The use cases are described in natural language and serve as a first input for detailed discussion with other partners. In particular, we want to clarify the interactions of technical work packages and to understand better the synergies to be expected from combining the three core technologies of SEKT. Our focus is not on the end-user perspective, but rather on the modelling expert perspective. In future refinements it is foreseen that the most relevant use cases will be refined and serve as a basis for joint system implementations of SEKT partners.

Deliverable D12.1.1. (WP12.1) This document accompanies the SEKT web site and mailing lists. Important features of the web site, which consists of a public and a private part, as well as open issues are being described. A brief overview summarizes the existing mailing lists available for SEKT and some subscription details are presented. Both, web site and mailing lists, are up-and-running from the beginning of the project and are subject to continuous improvement.

Deliverable D3.1.1.a (WP3.1) This document is an informal deliverable provided to SEKT partners. The main aim of this document is to get partners quickly started with using the KAON Open Source ontology management infrastructure. KAON consists of a number of different modules providing a broad bandwidth of functionalities centered around creation, storage, retrieval, maintenance and application of ontologies.