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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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Journal ArticleDOI
TL;DR: This technique aims to reduce the work-load of knowledge engineers and domain experts by suggesting candidate relationships that might become part of the ontology as well as prospective labels for them.
Abstract: Ontology learning (OL) from texts has been suggested as a technology that helps to reduce the bottleneck of knowledge acquisition in the construction of domain ontologies. In this learning process, the discovery, and possibly also labeling, of non-taxonomic relationships has been identified as one of the most difficult and often neglected problems. In this paper, we propose a technique that addresses this issue by analyzing a domain text corpus to extract verbs frequently applied for linking certain pairs of concepts. Integrated in an ontology building process, this technique aims to reduce the work-load of knowledge engineers and domain experts by suggesting candidate relationships that might become part of the ontology as well as prospective labels for them.

47 citations

Proceedings ArticleDOI
07 Apr 2008
TL;DR: A plug-in for the standard ontology editor Protege that allows users to model ontologies with mappings to data sources in order to perform OBDA, and it is argued that this plug- in, together with an OBDA-Enabled reasoner, allow users to build, test, and deploy OBDA Systems in academic or industrial settings.
Abstract: In ontology-based data access (OBDA), the aim is to use an ontology to mediate the access to data sources. We present a plug-in for the standard ontology editor Protege that allows users to model ontologies with mappings to data sources in order to perform OBDA. We argue that our plug-in, together with an OBDA-Enabled reasoner, allows users to build, test, and deploy OBDA Systems in academic or industrial settings.

47 citations

Proceedings ArticleDOI
16 Jun 2014
TL;DR: The high accuracy achieved in the tests demonstrates the effectiveness of the proposed method, as well as the applicability of Wikipedia for semantic text categorization purposes, and allows dynamically changing the classification topics without retraining of the classifier.
Abstract: We present a method for the automatic classification of text documents into a dynamically defined set of topics of interest. The proposed approach requires only a domain ontology and a set of user-defined classification topics, specified as contexts in the ontology. Our method is based on measuring the semantic similarity of the thematic graph created from a text document and the ontology sub-graphs resulting from the projection of the defined contexts. The domain ontology effectively becomes the classifier, where classification topics are expressed using the defined ontological contexts. In contrast to the traditional supervised categorization methods, the proposed method does not require a training set of documents. More importantly, our approach allows dynamically changing the classification topics without retraining of the classifier. In our experiments, we used the English language Wikipedia converted to an RDF ontology to categorize a corpus of current Web news documents into selection of topics of interest. The high accuracy achieved in our tests demonstrates the effectiveness of the proposed method, as well as the applicability of Wikipedia for semantic text categorization purposes.

47 citations

Book ChapterDOI
01 Jan 2011
TL;DR: This chapter focuses on instance matching by providing an accurate classification of the matching techniques proposed in the literature, and a comparison of the recent instance matching tools according to the results achieved in the OAEI 2009 contest.
Abstract: The growing need of sharing data and digital resources within and across organizations has produced a novel attention on issues related to ontology and instance matching. After an introductory classification of the main techniques and tools for ontology matching, the chapter focuses on instance matching by providing an accurate classification of the matching techniques proposed in the literature, and a comparison of the recent instance matching tools according to the results achieved in the OAEI 2009 contest. Ontology and instance matching solutions developed in the BOEMIE project for multimedia resource management and ontology evolution are finally presented.

47 citations

Posted Content
TL;DR: A new version of the OntoSpec methodology for ontology building is described, endowing it with a new resource, the DOLCE top-level ontology defined at the LOA (IST-CNR, Trento, Italy), to provide modellers with additional help in structuring application ontologies while maintaining independence vis-a-vis formal representation languages.
Abstract: This report describes a new version of the OntoSpec methodology for ontology building Defined by the LaRIA Knowledge Engineering Team (University of Picardie Jules Verne, Amiens, France), OntoSpec aims at helping builders to model ontological knowledge (upstream of formal representation) The methodology relies on a set of rigorously-defined modelling primitives and principles Its application leads to the elaboration of a semi-informal ontology, which is independent of knowledge representation languages We recently enriched the OntoSpec methodology by endowing it with a new resource, the DOLCE top-level ontology defined at the LOA (IST-CNR, Trento, Italy) The goal of this integration is to provide modellers with additional help in structuring application ontologies, while maintaining independence vis-a-vis formal representation languages In this report, we first provide an overview of the OntoSpec methodology's general principles and then describe the DOLCE re-engineering process A complete version of DOLCE-OS (ie a specification of DOLCE in the semi-informal OntoSpec language) is presented in an appendix

47 citations


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