Visual Logics Help People: An Evaluation of Diagrammatic, Textual and Symbolic Notations

Eisa Alharbi; John Howse; Gem Stapleton; Ali Hamie; Anestis Touloumis

Visual Logics Help People: An Evaluation of Diagrammatic, Textual and Symbolic Notations

Eisa Alharbi, John Howse, Gem Stapleton, Ali Hamie, Anestis Touloumis

University of Brighton

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

Abstract

Our aim is to provide empirical evidence that diagrammatic logics are more effective than symbolic and textual logics in allowing people to better understand information. Ontologies provide an important focus for such an empirical study: people need to understand the axioms of which ontologies comprise. A between-groups study compared six frequently-used axiom types using the (textual) Manchester OWL Syntax (MOS), (symbolic) description logic (DL) and concept diagrams. Concept diagrams yielded significantly better task performance than DL for all six, and MOS for four, axiom types. MOS outperformed concept diagrams for just one axiom type and DL for only three axiom types. Thus diagrams could ensure ontologies are developed more robustly.

Original language	English
Title of host publication	IEEE Symposium on Visual Languages and Human-Centric Computing 2017
Place of Publication	USA
Publisher	IEEE
Pages	0-0
Number of pages	1
ISBN (Print)	9781538604434
Publication status	Published - 17 Dec 2017
Event	IEEE Symposium on Visual Languages and Human-Centric Computing 2017 - Raleigh, North Carolina, USA, 11-14 October 2017 Duration: 13 Nov 2017 → …

Conference

Conference	IEEE Symposium on Visual Languages and Human-Centric Computing 2017
Period	13/11/17 → …

Bibliographical note

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

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Anestis Touloumis
- A.Touloumisbrighton.acuk
- School of Arch, Tech and Eng - Principal Lecturer
- Computing and Mathematical Sciences Research Excellence Group
Person: Academic

Cite this

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title = "Visual Logics Help People: An Evaluation of Diagrammatic, Textual and Symbolic Notations",

abstract = "Our aim is to provide empirical evidence that diagrammatic logics are more effective than symbolic and textual logics in allowing people to better understand information. Ontologies provide an important focus for such an empirical study: people need to understand the axioms of which ontologies comprise. A between-groups study compared six frequently-used axiom types using the (textual) Manchester OWL Syntax (MOS), (symbolic) description logic (DL) and concept diagrams. Concept diagrams yielded significantly better task performance than DL for all six, and MOS for four, axiom types. MOS outperformed concept diagrams for just one axiom type and DL for only three axiom types. Thus diagrams could ensure ontologies are developed more robustly.",

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Alharbi, E, Howse, J, Stapleton, G, Hamie, A & Touloumis, A 2017, Visual Logics Help People: An Evaluation of Diagrammatic, Textual and Symbolic Notations. in IEEE Symposium on Visual Languages and Human-Centric Computing 2017. IEEE, USA, pp. 0-0, IEEE Symposium on Visual Languages and Human-Centric Computing 2017, 13/11/17. <http://ieeexplore.ieee.org/document/8103475/>

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AU - Touloumis, Anestis

N1 - © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

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N2 - Our aim is to provide empirical evidence that diagrammatic logics are more effective than symbolic and textual logics in allowing people to better understand information. Ontologies provide an important focus for such an empirical study: people need to understand the axioms of which ontologies comprise. A between-groups study compared six frequently-used axiom types using the (textual) Manchester OWL Syntax (MOS), (symbolic) description logic (DL) and concept diagrams. Concept diagrams yielded significantly better task performance than DL for all six, and MOS for four, axiom types. MOS outperformed concept diagrams for just one axiom type and DL for only three axiom types. Thus diagrams could ensure ontologies are developed more robustly.

AB - Our aim is to provide empirical evidence that diagrammatic logics are more effective than symbolic and textual logics in allowing people to better understand information. Ontologies provide an important focus for such an empirical study: people need to understand the axioms of which ontologies comprise. A between-groups study compared six frequently-used axiom types using the (textual) Manchester OWL Syntax (MOS), (symbolic) description logic (DL) and concept diagrams. Concept diagrams yielded significantly better task performance than DL for all six, and MOS for four, axiom types. MOS outperformed concept diagrams for just one axiom type and DL for only three axiom types. Thus diagrams could ensure ontologies are developed more robustly.

M3 - Conference contribution with ISSN or ISBN

SN - 9781538604434

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EP - 0

BT - IEEE Symposium on Visual Languages and Human-Centric Computing 2017

PB - IEEE

CY - USA

T2 - IEEE Symposium on Visual Languages and Human-Centric Computing 2017

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ER -

Visual Logics Help People: An Evaluation of Diagrammatic, Textual and Symbolic Notations

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Anestis Touloumis

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