Ontology Learning from text aims at generating domain ontologies from textual resources by applying natural language processing and machine learning techniques. It is inherent in the ontology learning process that the acquired ontologies represent uncertain and possibly contradicting knowledge. From a logical perspective, the learned ontologies are potentially inconsistent knowledge bases that thus do not allow meaningful reasoning directly. In this paper we present an approach to generate consistent OWL ontologies from learned ontology models by taking the uncertainty of the knowledge into account. We further present evaluation results from experiments with ontologies learned from a Digital Library.

Creating metadata by annotating documents is one of the major techniques for putting machine understandable data on the Web. Though there exist many tools for annotating web pages, few of them fully support the creation of semantically interlinked metadata, such as necessary for a truely Semantic Web. In this paper, we present an ontology-based annotation environment, OntoAnnotate, which offers comprehensive support for the creation of semantically interlinked metadata by human annotators.

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This is the second of a two-part paper to review ontology research and development, in particular, ontology mapping and evolving. Ontology is defined as a formal explicit specification of a shared conceptualization. Ontology itself is not a static model so that it must have the potential to capture changes of meanings and relations. As such, mapping and evolving ontologies is part of an essential task of ontology learning and development. Ontology mapping is concerned with reusing existing ontologies, expanding and combining them by some means and enabling a larger pool of information and knowledge in different domains to be integrated to support new communication and use. Ontology evolving, likewise, is concerned with maintaining existing ontologies and extending them as appropriate when new information or knowledge is acquired. It is apparent from the reviews that current research into semi-automatic or automatic ontology research in all the three aspects of generation, mapping and evolving have so far achieved limited success. Expert

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

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

Abstract. Domain ontologies very rarely model verbs as relations holding between concepts. However, the role of the verb as a central connecting element between concepts is undeniable. Verbs specify the interaction between the participants of some action or event by expressing relations between them. In parallel, it can be argued from an ontology engineering point of view that verbs express a relation between two classes that specify domain and range. The work described here is concerned with relation extraction for ontology extension along these lines. We describe a system (RelExt) that is capable of automatically identifying highly relevant triples (pairs of concepts connected by a relation) over concepts from an existing ontology. RelExt works by extracting relevant verbs and their grammatical arguments (i.e. terms) from a domain-specific text collection and computing corresponding relations through a combination of linguistic and statistical processing. The paper includes a detailed description of the system architecture and evaluation results on a constructed benchmark. RelExt has been developed in the context of the SmartWeb project, which aims at providing intelligent information services via mobile broadband devices on the FIFA World Cup that will be hosted in Germany in 2006. Such services include location based navigational information as well as question answering in the football domain. 1

Ontologies in current computer science parlance are computer based resources that represent agreed domain semantics. This paper first introduces ontologies in general and subsequently, in particular, shortly outlines the DOGMA ontology engineering approach that separates "atomic" conceptual relations from "predicative" domain rules. In the main part of the paper, we describe and experimentally evaluate work in progress on a potential method to automatically derive the atomic conceptual relations mentioned above from a corpus of English medical texts. Preliminary outcomes are presented based on the clustering of nouns and compound nouns according to co-occurrence frequencies in the subject-verbobject syntactic context.

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.