Article

Towards a standard upper ontology

Authors:

Adam PeaseAuthors Info & Claims

FOIS '01: Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001

Pages 2 - 9

https://doi.org/10.1145/505168.505170

Published: 17 October 2001 Publication History

Abstract

The Suggested Upper Merged Ontology (SUMO) is an upper level ontology that has been proposed as a starter document for The Standard Upper Ontology Working Group, an IEEE-sanctioned working group of collaborators from the fields of engineering, philosophy, and information science. The SUMO provides definitions for general-purpose terms and acts as a foundation for more specific domain ontologies. In this paper we outline the strategy used to create the current version of the SUMO, discuss some of the challenges that we faced in constructing the ontology, and describe in detail its most general concepts and the relations between them.

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https://doi.org/10.3390/electronics13183666
Laukaitis A(2024)Reversal of the Word Sense Disambiguation Task Using a Deep Learning ModelApplied Sciences10.3390/app1413555014:13(5550)Online publication date: 26-Jun-2024
https://doi.org/10.3390/app14135550
Umbrico ACesta AOrlandini A(2024)Enhancing awareness of industrial robots in collaborative manufacturingSemantic Web10.3233/SW-23339415:2(389-428)Online publication date: 30-Apr-2024
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Index Terms

Towards a standard upper ontology
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Semantic networks
  2. Machine learning
    1. Machine learning approaches
      1. Rule learning

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cover image ACM Conferences

FOIS '01: Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001

October 2001

362 pages

ISBN:1581133774

DOI:10.1145/505168

Conference Chairs:
Nicola Guarino,
Barry Smith
University at Buffalo, Buffalo, NY
,
Christopher Welty
Vassar College, Poughkeepsie, NY

Copyright © 2001 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 October 2001

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FOIS01: 2nd International Conference on Formal Ontology in Information Systems

October 17 - 19, 2001

Maine, Ogunquit, USA

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https://doi.org/10.3390/electronics13183666
Laukaitis A(2024)Reversal of the Word Sense Disambiguation Task Using a Deep Learning ModelApplied Sciences10.3390/app1413555014:13(5550)Online publication date: 26-Jun-2024
https://doi.org/10.3390/app14135550
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https://doi.org/10.3233/SW-233394
Hughes NChang YHu STalak RAbdulhai RStrader JCarlone L(2024)Foundations of spatial perception for robotics: Hierarchical representations and real-time systemsThe International Journal of Robotics Research10.1177/02783649241229725Online publication date: 12-Feb-2024
https://doi.org/10.1177/02783649241229725
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