To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse — definitions of classes, relations

system that automatically induces an ontology from any large on-line text collection in a specific domain. The ontology that is induced consists of domain concepts, related by kind-of and part-of links. To achieve domain-independence, we use a combination of relatively shallow methods along with any

initial criteria for a two-step normalisation of ontologies implemented using OWL or related DL based formalisms. For the first – “ontological normalisation ” – we accept Welty and Guarino’s analysis. For the second – “implementation normalisation ” – we propose an approach based on decomposing

, i.e. metadata that comprises class instances and relationship instances. These instances are not based on a fix structure, but on a domain ontology. We discuss some of the requirements one has to meet when developing such a framework, e.g. the integration of a metadata crawler, inference services

via related concepts. One approach for mapping disparate ontologies is to use a standard upper ontology. In determining how Semantic Web technologies might be applied to United States (U.S.) Government domains, we consider whether the use of standard upper ontologies makes sense in these environments

In this work we present a robust approach for dynami-cally harvesting domain knowledge from open domain corpora and lexical resources. It relies on the notion of Semantic Domains and provides a fully unsupervised method for terminology extraction and ontology learn-ing. It makes use of an algorithm

The goal of this paper is to integrate the Conceptual Mapping Model with an ontology -based knowledge representation (i.e. Suggested Upper Merged Ontology (SUMO)) in order to demonstrate that conceptual metaphor analysis can be restricted and eventually, automated. In particular, we

. Statistical clinical query pattern derivation described in this paper is an approach to obtain this information semiautomatically. It is based on predicting clinical query patterns given medical ontologies, domain corpora and statistical analysis. The patterns identified in this way are then compared to a

The goal of this paper is to integrate the Conceptual Mapping Model with an ontology-based knowledge representation (i.e. Suggested Upper Merged Ontology (SUMO)) in order to demonstrate that conceptual metaphor analysis can be restricted and eventually, automated. In particular, we

identified in the domain corpus, aggregates them by computing centroids for known relation types and stores the centroids in a central Knowledge Base (KB). Comparing verb vectors extracted from unknown relations with the stored centroids yields link-type suggestions. Domain experts evaluate these suggestions