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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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Proceedings ArticleDOI
04 Sep 2001
TL;DR: An ontology is proposed that defines the concepts needed for object-oriented modeling and gives a graphical example and allows the definition of development methods, modeling languages and tools that are applicable to complex systems.
Abstract: The development of business and information systems requires a significant amount of modeling. The current modeling languages and tools have difficulties supporting the modeling of systems spanning through multiple organizational levels. The use of inadequate modeling abstractions is one of the important causes for these difficulties. The paper proposes an ontology that defines the concepts needed for object-oriented modeling and gives a graphical example. The ontology is based on RM-ODP and relies on constructivism and system theory. The proposed ontology allows the definition of development methods, modeling languages and tools that are applicable to complex systems. This can lead to significant productivity improvements in the business and software development communities.

46 citations

01 Jun 2004
TL;DR: This document shows how biomedical resources can be linked into a Semantic Web using Protege, a widely-used open-source ontology modeling environment with support for the Web Ontology Language (OWL).
Abstract: In this document we show how biomedical resources can be linked into a Semantic Web using Protege Protege is a widely-used open-source ontology modeling environment with support for the Web Ontology Language (OWL) With the example domain of brain cortex anatomy we demonstrate how Protege can be used to build an OWL ontology and to maintain ontology consistency with a description logic classifier We also show how Protege can be used to link existing Web resources such as biomedical articles and images into a Semantic Web

46 citations

Journal ArticleDOI
TL;DR: An ontology learning and population system that combines both statistical and semantic methodologies is presented that achieves good performances on standard datasets.
Abstract: The success of Semantic Web will heavily rely on the availability of formal ontologies to structure machine understanding data. However, there is still a lack of general methodologies for ontology automatic learning and population, i.e. the generation of domain ontologies from various kinds of resources by applying natural language processing and machine learning techniques In this paper, the authors present an ontology learning and population system that combines both statistical and semantic methodologies. Several experiments have been carried out, demonstrating the effectiveness of the proposed approach. HighlightsA graph of terms can be effectively used for ontology building.Such a graph is extracted from documents thanks to a LDA based methodology.Ontology learning involves the use of annotated lexicons (WordNet).Proposed method achieves good performances on standard datasets.

46 citations

Book ChapterDOI
TL;DR: This work argues for a theoretical framework that favours the analysis and implementation of semantic interoperability scenarios relative to particular understandings of semantics and presents an example case of such a scenario where the framework has been applied as well as variations of it in the domain of ontology mapping.
Abstract: We discuss approaches to semantic heterogeneity and propose a formalisation of semantic interoperability based on the Barwise-Seligman theory of information flow. We argue for a theoretical framework that favours the analysis and implementation of semantic interoperability scenarios relative to particular understandings of semantics. We present an example case of such a scenario where our framework has been applied as well as variations of it in the domain of ontology mapping.

46 citations

Journal ArticleDOI
TL;DR: An automated method to construct the domain ontology by calculating a TF-IDF to find the weight of terms, using a recursive ART network to cluster terms, and outputs an ontology in a Jena package using an RDF format is proposed.
Abstract: Ontology describes data about data and offers a group of glossaries with a definition that encompasses them in their entire. It not only transfers syntax of words but also accurately transfers semantic data between human users and the network. Hence, the usefulness of the semantic web depends on whether the domain ontology can be constructed effectively and correctly. In this paper we propose an automated method to construct the domain ontology. First, we collected domain-related web pages from the Internet. Secondly, we use the HTML tag labels to choose meaningful terms from the web pages. Next, we use these terms to construct the domain ontology by calculating a TF-IDF to find the weight of terms, using a recursive ART network (Adaptive Resonance Theory Network) to cluster terms. Each group of terms will find a candidate keyword for ontology construction. Boolean operations locate individual keywords in a hierarchy. Finally, the system outputs an ontology in a Jena package using an RDF format. The primary experiment indicates that our method is useful for domain ontology creation.

46 citations


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