... 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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Automatic intelligent web exploration will benefit from shallow information extraction techniques if the latter can be brought to work within many different domains. The major bottleneck for this, however, lies in the so far difficult and expensive modeling of lexical knowledge, extraction rules, and an ontology that together define the information extraction system. In this paper we present a bootstrapping approach that allows for the fast creation of an ontology-based information extracting system relying on several basic components, viz. a core information extraction system, an ontology engineering environment and an inference engine. We make extensive use of machine learning techniques to support the semi-automatic, incremental bootstrapping of the domain-specific target information extraction system.

Abstract. This paper describes an automatic approach to identify lexical patterns which represent semantic relationships between concepts, from an on-line encyclopedia. Next, these patterns can be applied to extend existing ontologies or semantic networks with new relations. The experiments have been performed with the Simple English Wikipedia and WordNet 1.7. A new algorithm has been devised for automatically generalising the lexical patterns found in the encyclopedia entries. We have found general patterns for the hyperonymy, hyponymy, holonymy and meronymy relations and, using them, we have extracted more than 1200 new relationships that did not appear in WordNet originally. The precision of these relationships ranges between 0.61 and 0.69, depending on the relation. 1

Abstract. Ontologies are a tool for Knowledge Representation that is now widely used, but the effort employed to build an ontology is high. We describe here a procedure to automatically extend an ontology such as WordNet with domain-specific knowledge. The main advantage of our approach is that it is completely unsupervised, so it can be applied to different languages and domains. Our experiments, in which several domain-specific concepts from a book have been introduced, with no human supervision, into WordNet, have been successful. 1

Ontologies play an increasingly important role in Knowledge Management. One of the main problems associated with ontologies is that they need to be constructed and maintained. Manual construction of larger ontologies is usually not feasible within companies because of the effort and costs required. Therefore, a semi-automatic approach to ontology construction and maintenance is what everybody is wishing for. The paper presents a framework for semi-automatically learning ontologies from domainspecific texts by applying machine learning techniques. The TEXT-TO-ONTO framework integrates manual engineering facilities to follow a balanced cooperative modelling paradigm. 1

This paper describes SAMOVAR (Systems Analysis of Modelling and Validation of Renault Automobiles), aiming at preserving and exploiting the memory of past projects in automobile design (in particular the memory of the problems encountered during a project) so as to exploit them in new projects. SAMOVAR relies on (1) the building of ontologies (in particular, thanks to the use of a linguistic tool on a textual corpus in order to enrich a core ontology in a semi-automatic way), (2) the semantic annotations of the descriptions of problems relatively to these ontologies, (3) the formalisation of the ontologies and annotations in RDF(S) so as to integrate in SAMOVAR the tool CORESE that enables an ontology-guided search in the base of the problem descriptions.

We describe here a procedure to combine two different existing techniques for Ontology Enrichment with domain-specific concepts. The resulting algorithm is fully unsupervised, and the level of precision is higher than when they are used separately, so we believe that both algorithms benefit from each other. The experiments have been performed by extending WordNet with concepts extracted from The Lord of the Rings.

Knowledge Acquisition is still the bottleneck in building many kinds of applications, such as inference engines. We describe here a procedure to automatically extend an ontology with domain-specific knowledge. The main advantage of our approach is that it is completely unsupervised, so it can be applied to different languages and domains. Our initial results have been highly successful and we believe that with some improvement in accuracy it can be applied to large ontologies.

Abstract not found