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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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01 Jan 2007
TL;DR: This work has developed a set of tools and services to support the process of achieving consensus on such a common shared ontologies by geographically distributed groups and describes applications using these tools to achieve consensus on ontologies and to integrate information.
Abstract: Information integration is enabled by having a precisely defined common terminology. We call this combination of terminology and definitions an ontology . We have developed a set of tools and services to support the process of achieving consensus on such a common shared ontologies by geographically distributed groups. These tools make use of the world-wide web to enable wide access and provide users with the ability to publish, browse, create, and edit ontologies stored on an ontology server. Users can quickly assemble a new ontology from a library of modules. We discuss how our system was constructed, how it exploits existing protocols and browsing tools, and our experience supporting hundreds of users. We describe applications using our tools to achieve consensus on ontologies and to integrate information. The ontology server may be accessed through the URL http://www-ksl-svc.stanford.edu:5915/

98 citations

Journal ArticleDOI
TL;DR: A method for corpus-driven ontology design: extracting conceptual hierarchies from arbitrary domain-specific collections of texts, employing statistical techniques initially to elicit a conceptual hierarchy, which is then augmented through linguistic analysis.
Abstract: This paper discusses a method for corpus-driven ontology design: extracting conceptual hierarchies from arbitrary domain-specific collections of texts. These hierarchies can form the basis for a concept-oriented (onomasiological) terminology collection, and hence may be used as the basis for developing knowledge-based systems using ontology editors. This reference to ontology is explored in the context of collections of terms. The method presented is a hybrid of statistical and linguistic techniques, employing statistical techniques initially to elicit a conceptual hierarchy, which is then augmented through linguistic analysis. The result of such an extraction may be useful in information retrieval, knowledge management, or in the discipline of terminology science itself.

98 citations

Proceedings ArticleDOI
15 Dec 2008
TL;DR: The proposed ontology, named OntoDM, is a deep/heavy-weight ontology and follows best practices in ontology engineering, such as not allowing multiple inheritance of classes, using a predefined set of relations and using a top level ontology.
Abstract: Motivated by the need for unification of the field of data mining and the growing demand for formalized representation of outcomes of research, we address the task of constructing an ontology of data mining. The proposed ontology, named OntoDM, is based on a recent proposal of a general framework for data mining, and includes definitions of basic data mining entities, such as datatype and dataset, data mining task, data mining algorithm and components thereof (e.g., distance function), etc. It also allows for the definition of more complex entities, e.g., constraints in constraint-based data mining, sets of such constraints (inductive queries) and data mining scenarios (sequences of inductive queries). Unlike most existing approaches to constructing ontologies of data mining, OntoDM is a deep/heavy-weight ontology and follows best practices in ontology engineering, such as not allowing multiple inheritance of classes, using a predefined set of relations and using a top level ontology.

97 citations

Proceedings ArticleDOI
03 Dec 2006
TL;DR: The role of ontologies in facilitating simulation modeling and the technical challenges in distributed simulation modeling are outlined and how ontology-based methods may be applied to address these challenges are described.
Abstract: Ontological analysis has been shown to be an effective first step in the construction of robust knowledge based systems. However, the modeling and simulation community has not taken advantage of the benefits of ontology management methods and tools. Moreover, the popularity of semantic technologies and the semantic web has provided several beneficial opportunities for the modeling and simulation communities of interest. This paper describes the role of ontologies in facilitating simulation modeling. It outlines the technical challenges in distributed simulation modeling and describes how ontology-based methods may be applied to address these challenges. The paper concludes by describing an ontology-based solution framework for simulation modeling and analysis and outlining the benefits of this solution approach.

97 citations

01 Jan 2002
TL;DR: In this paper, the similarities between database-schema evolution and ontology evolution will allow us to build on the extensive research in schema evolution, but there are also important differences between database schemas and ontologies, such as different usage paradigms, the presence of explicit semantics and different knowledge models.
Abstract: As ontology development becomes a more ubiquitous and collaborative process, ontology versioning and evolution becomes an important area of ontology research. The many similarities between database-schema evolution and ontology evolution will allow us to build on the extensive research in schema evolution. However, there are also important differences between database schemas and ontologies. The differences stem from different usage paradigms, the presence of explicit semantics and different knowledge models. A lot of problems that existed only in theory in database research come to the forefront as practical problems in ontology evolution. These differences have important implications for the development of ontology-evolution frameworks: The traditional distinction between versioning and evolution is not applicable to ontologies. There are several dimensions along which compatibility between versions must be considered. The set of change operations for ontologies is different. We must develop automatic techniques for finding similarities and differences between versions.

97 citations


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