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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.


Papers
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Book ChapterDOI
03 Jun 2007
TL;DR: This work has developed guidelines and a set of methodological tools based on the notions of "normalization" and "stable metrics" for creating ontology metrics that allow the metric author to decide which properties metrics need to fulfil and to appropriately design the desired metric.
Abstract: You can only control what you can measure. Measuring ontologies is necessary to evaluate ontologies both during engineering and application. Metrics allow the fast and simple assessment of an ontology and also to track their subsequent evolution. In the last few years, a growing number of ontology metrics and measures have been suggested and defined. But many of them suffer from a recurring set of problems, most importantly they do not take the semantics of the ontology language properly into account. The work presented here is a principal approach to facilitate the creation of ontology metrics with the clear goal to go beyond structural metrics to proper semantic-aware ontology metrics. We have developed guidelines and a set of methodological tools based on the notions of "normalization" and "stable metrics" for creating ontology metrics. These guidelines allow the metric author to decide which properties metrics need to fulfil and to appropriately design the desired metric. A discussion of an exemplary metric (taken from literature) illustrates and motivates the issues and suggested solutions.

106 citations

Proceedings Article
01 May 2001
TL;DR: The vision of ontology learning that is proposed here includes a number of complementary disciplines that feed on different types of unstructured, semi-structured and fully structured data in order to support a semi-automatic, cooperative ontology engineering process.
Abstract: The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. Therefore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and easy engineering of ontologies and avoidance of a knowledge acquisition bottleneck. Ontology Learning greatly facilitates the construction of ontologies by the ontology engineer. The vision of ontology learning that we propose here includes a number of complementary disciplines that feed on different types of unstructured, semi-structured and fully structured data in order to support a semi-automatic, cooperative ontology engineering process. Our ontology learning framework proceeds through ontology import, extraction, pruning, refinement, and evaluation giving the ontology engineer a wealth of coordinated tools for ontology modeling. Besides of the general framework and architecture, we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, Text-To-Onto, such as ontology learning from free text, from dictionaries, or from legacy ontologies, and refer to some others that need to complement the complete architecture, such as reverse engineering of ontologies from database schemata or learning from XML documents.

105 citations

Journal ArticleDOI
TL;DR: F fuzzyDL is described, an expressive fuzzy ontology reasoner with some unique features that are suitable in domains where vague or imprecise pieces of information play an important role and the availability of effective reasoners able to deal with fuzzy ontologies is described.
Abstract: Classical, two-valued, ontologies have been successfully applied to represent the knowledge in many domains. However, it has been pointed out that they are not suitable in domains where vague or imprecise pieces of information play an important role. To overcome this limitation, several extensions to classical ontologies based on fuzzy logic have been proposed. We believe, however, that the success of fuzzy ontologies strongly depends on the availability of effective reasoners able to deal with fuzzy ontologies.In this paper we describe fuzzyDL, an expressive fuzzy ontology reasoner with some unique features. We discuss its possibilities for fuzzy ontology representation, the supported reasoning services, the different interfaces to interact with it, some implementation details, a comparison with other fuzzy ontology reasoners, and an overview of the main applications that have used it so far.

105 citations

Journal ArticleDOI
TL;DR: Two algorithms are presented that can help terminology developers and users to identify potential areas of improvement and provide evidence for the thesis that both formal logical and linguistic tools should be used in the development and quality-assurance process of large terminologies.
Abstract: Quality assurance in large terminologies is a difficult issue. We present two algorithms that can help terminology developers and users to identify potential areas of improvement. We demonstrate the methodology by applying the algorithms to one of the most popular terminologies, SNOMED-CT. Analysis of the results provides evidence for the thesis that both formal logical and linguistic tools should be used in the development and quality-assurance process of large terminologies.

105 citations

Dissertation
07 Nov 2002
TL;DR: This work concerns multi-agents systems for the management of a corporate semantic web based on an ontology O'CoMMA focusing on two application scenarios: support technology monitoring activities and assist the integration of a new employee to the organisation.
Abstract: This work concerns multi-agents systems for the management of a corporate semantic web based on an ontology. It was carried out in the context of the European project CoMMA focusing on two application scenarios: support technology monitoring activities and assist the integration of a new employee to the organisation. Three aspects were essentially developed in this work: the design of a multi-agents architecture supporting both scenarios, and the organisational top-down approach followed to identify the societies, the roles and the interactions of agents; the construction of the ontology O'CoMMA and the structuring of a corporate memory exploiting semantic Web technologies; the design and implementation of the sub-societies of agents dedicated to the management of the annotations and the ontology and of the protocols underlying these groups of agents, in particular techniques for distributing annotations and queries between the agents.

105 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202343
2022155
20219
20205
20199
201838