Upper ontology

About: Upper ontology is a research topic. Over the lifetime, 9767 publications have been published within this topic receiving 220721 citations. The topic is also known as: top-level ontology & foundation ontology.

The TERMINAE Method and Platform for Ontology Engineering from Texts

Abstract: Designed about ten years ago, the TERMINAE method and workbench for ontology engineering from texts have been going on evolving since then. Our investigations integrate the experience gained through its use in industrial and academic projects, the progress of natural language processing as well as the evolution of the ontology engineering. Several new methodological guidelines, such as the reuse of core ontologies, have been added to the method and implemented in the workbench. It has also been modified in order to be compliant to some recent standards such as the OWL knowledge representation. The paper recalls the terminology engineering principles underlying TERMINAE and comments its originality. Then it presents the kind of conceptual model that is built with this method, and its knowledge representation. The method and the support provided by the workbench are detailed and illustrated with a case-study in law. With regard to the state of the art, TERMINAE is one of the most supervised methods in the trend of ontology learning. This option raises epistemological issues about how language and knowledge can be articulated and the distance that separate formal ontologies from learned conceptual models.

Applying the Web ontology language to management information definitions

Abstract: The extended markup language (XML) has emerged in the Internet world as a standard representation format, which can be useful to describe and transmit management information. However, XML formats alone do not give formal semantics to it. To solve this question, ontology languages based on the resource description framework can be used to improve the expressiveness of management information specifications. This article presents an approach that uses an XML-based ontology language to define network and system management information. For this, the structures of the Web ontology language known as OWL are analyzed and compared to those used in management definitions, also studying the advantages ontology languages can provide in this area.

Ontology-based approach for information fusion

Abstract: Ontologies have received increasing interest in the computer science community and their benefits have been recognized in many areas. In this paper, we discuss the role of ontologies to facilitate information fusion fiom heterogeneous data and knowledge sources in support of high-level information fusion processes. We review several approaches where ontologies help provide semantic integration of information. We present preliminary work about ontological engineering for level 2 and 3 information fusion that should help semantic integration. Ontology development methods and tools should support the ontological engineering process. To this end, we propose a methodological approach and a flexible environment for ontology management that enables the building of extensible ontologies, and the mappingfiom ontologies to information sources.

An integrative proximity measure for ontology alignment

Abstract: Integrating heterogeneous resources of the web will require finding agreement between the underlying ontologies. A variety of methods from the literature may be used for this task, basically they perform pair-wise comparison of entities from each of the ontologies and select the most similar pairs. We introduce a similarity measure that takes advantage of most of the features of OWL-Lite ontologies and integrates many ontology comparison techniques in a common framework. Moreover, we put forth a computation technique to deal with one-to-many relations and circularities in the similarity definitions.

Learning to Share Meaning in a Multi-Agent System

Abstract: The development of the semantic Web will require agents to use common domain ontologies to facilitate communication of conceptual knowledge. However, the proliferation of domain ontologies may also result in conflicts between the meanings assigned to the various terms. That is, agents with diverse ontologies may use different terms to refer to the same meaning or the same term to refer to different meanings. Agents will need a method for learning and translating similar semantic concepts between diverse ontologies. Only until recently have researchers diverged from the last decade's “common ontology” paradigm to a paradigm involving agents that can share knowledge using diverse ontologies. This paper describes how we address this agent knowledge sharing problem of how agents deal with diverse ontologies by introducing a methodology and algorithms for multi-agent knowledge sharing and learning in a peer-to-peer setting. We demonstrate how this approach will enable multi-agent systems to assist groups of people in locating, translating, and sharing knowledge using our Distributed Ontology Gathering Group Integration Environment (DOGGIE) and describe our proof-of-concept experiments. DOGGIE synthesizes agent communication, machine learning, and reasoning for information sharing in the Web domain.