Non-taxonomic relations between concepts appear as a major building block in common ontology definitions. In fact, their definition consumes much of the time needed for engineering an ontology. We here describe

Ontologies have become an important means for structuring information and information systems and, hence, important in knowledge as well as in software engineering. However, there remains the problem of engineering large and adequate ontologies within short time frames in order to keep costs low. For this purpose, eorts have been made to facilitate the ontology engineering process, in particular the acquisition of ontologies from domain texts. We present a general architecture for discovering conceptual structures and engineering ontologies. Based on the architecture we propose a new approach to extend current approaches, who mostly focus on the semi-automatic acquisition of taxonomies, by the discovery of non-taxonomic conceptual relations. We use a generalized association rule algorithm that does not only detect relations between concepts, but also determines the appropriate level of abstraction at which to dene relations. 1 Introduction Ontologies 1 have shown their usefulness...

The focused access to knowledge resources like intranet documents plays a vital role in knowledge management and supports in general the shifting towards a Semantic Web. Ontologies act as a conceptual backbone for semantic document access by providing a common understanding and conceptualization of a domain. Building domain-specific ontologies is a time-consuming and expensive manual construction task. This paper describes our actual and ongoing work in supporting semi-automatic ontology acquisition from a corporate intranet of an insurance company. We present a comprehensive architecture and generic method for discovering a domain-tailored ontology from given intranet resources. 1 Introduction The amount of information available to corporate employees has grown drastically with the use of intranets. Unfortunately this growth of available information has made the access to useful or necessary information much more difficult due to the fact that the access is usually based on ...

for automated ontology learning extracts relevant domain terms from a corpus of text, relates them to appropriate concepts in a general-purpose ontology, and detects taxonomic and other semantic relations among the concepts. The authors used it to automatically translate multiword terms from English to Italian.

... we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, KAON Text-To-Onto.

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The authors have built a software environment that supports the construction and assessment of a domain ontology for intelligent information integration within a virtual user community.

In this paper we firstly present a progress report of the OntoWordNet project, a research program aimed at achieving a formal specification of WordNet. Within this program, we developed a hybrid bottom-up top-down methodology to automatically extract association relations from WordNet, and interpret these associations in terms of a set of conceptual relations, formally defined in the DOLCE foundational ontology. Preliminary results provide us with the conviction that a research program aiming to obtain a consistent, modularized, and axiomatized ontology from WordNet can be completed in acceptable time with the support of semi-automatic techniques.

Experience so ws t at t e quality o f t e sto red kno wledge determines t e success (t erefo re t e effective usage) o f an o nto lo gy. In fact, an o nto lo gy w ere relevant co ncepts are absent, o r are no t co nfo rmant to a do main view o f a given co mmunity, will be scarcely used, o r even disregarded. In t is paper we present a meto d and a set o f so ftware to o ls aimed at suppo rting do main experts in po pulating a do main o nto lo gy and o btaining a s ared co nsensus o n its co ntent. "Co nsensus" is ac ieved in an implicit and explicit way: implicitly, since candidate co ncepts are selected amo ng t e terms t at are frequently and co nsistently referred in t e do cuments pro duced by t e virtual co mmunity o f users; explicitly, tro ug t e use o f a web-based gro upware aimed at co nsensus building. 1

This paper describes our actual and ongoing work in supporting semi-automatic ontology acquisition from a corporate intranet of an insurance company. A comprehensive architecture and a system for semi-automatic ontology acquisition supports processing semi-structured information (e.g. contained in dictionaries) and natural language documents and including existing core ontologies (e.g. GermaNet, WordNet). We present a method for acquir- ing a application-tailored domain ontology from given heterogeneous intranet sources.