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.

Fine-grained proper noun ontologies for question answering

Abstract: The WordNet lexical ontology, which is primarily composed of common nouns, has been widely used in retrieval tasks. Here, we explore the notion of a fine-grained proper noun ontology and argue for the utility of such an ontology in retrieval tasks. To support this claim, we build a fine-grained proper noun ontology from unrestricted news text and use this ontology to improve performance on a question answering task.

A lightweight methodology for rapid ontology engineering

Abstract: UPON Lite focuses on users, typically domain experts without ontology expertise, minimizing the role of ontology engineers.

Acquisition of OWL DL Axioms from Lexical Resources

Abstract: State-of-the-art research on automated learning of ontologies from text currently focuses on inexpressive ontologies. The acquisition of complex axioms involving logical connectives, role restrictions, and other expressive features of the Web Ontology Language OWL remains largely unexplored. In this paper, we present a method and implementation for enriching inexpressive OWL ontologies with expressive axioms which is based on a deep syntactic analysis of natural language definitions. We argue that it can serve as a core for a semi-automatic ontology engineering process supported by a methodology that integrates methods for both ontology learning and evaluation. The feasibility of our approach is demonstrated by generating complex class descriptions from Wikipedia definitions and from a fishery glossary provided by the Food and Agriculture Organization of the United Nations.

From Ontology to Semantic Similarity: Calculation of Ontology-Based Semantic Similarity

Abstract: Advances in high-throughput experimental techniques in the past decade have enabled the explosive increase of omics data, while effective organization, interpretation, and exchange of these data require standard and controlled vocabularies in the domain of biological and biomedical studies. Ontologies, as abstract description systems for domain-specific knowledge composition, hence receive more and more attention in computational biology and bioinformatics. Particularly, many applications relying on domain ontologies require quantitative measures of relationships between terms in the ontologies, making it indispensable to develop computational methods for the derivation of ontology-based semantic similarity between terms. Nevertheless, with a variety of methods available, how to choose a suitable method for a specific application becomes a problem. With this understanding, we review a majority of existing methods that rely on ontologies to calculate semantic similarity between terms. We classify existing methods into five categories: methods based on semantic distance, methods based on information content, methods based on properties of terms, methods based on ontology hierarchy, and hybrid methods. We summarize characteristics of each category, with emphasis on basic notions, advantages and disadvantages of these methods. Further, we extend our review to software tools implementing these methods and applications using these methods.

Ontology Evolution within Ontology Editors.

Abstract: An ontology over a period of time needs to be modified to reflect changes in the real world, changes in the user’s requirements, drawbacks in the initial design, to incorporate additional functionality or to allow for incremental improvement. Although changes are inevitable during the development and deployment of an ontology, most of the current ontology editors unfortunately do not provide enough support for efficient copying with changes. Since changes are the force that drives the evolution process, in this paper we discuss the requirements for the ontology editors in order to support ontology evolution.