Assisting Pictogram Selection with Categorized Semantics

Heeryon CHO; Toru ISHIDA; Satoshi OYAMA; Rieko INABA; Toshiyuki TAKASAKI

doi:10.1093/ietisy/e91-d.11.2638

Assisting Pictogram Selection with Categorized Semantics

Heeryon CHO, Toru ISHIDA, Satoshi OYAMA, Rieko INABA, Toshiyuki TAKASAKI

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Since participants at both end of the communication channel must share common pictogram interpretation to communicate, the pictogram selection task must consider both participants' pictogram interpretations. Pictogram interpretation, however, can be ambiguous. To assist the selection of pictograms more likely to be interpreted as intended, we propose a categorical semantic relevance measure which calculates how relevant a pictogram is to a given interpretation in terms of a given category. The proposed measure defines similarity measurement and probability of interpretation words using pictogram interpretations and frequencies gathered from a web survey. Moreover, the proposed measure is applied to categorized pictogram interpretations to enhance pictogram retrieval performance. Five pictogram categories used for categorizing pictogram interpretations are defined based on the five first-level classifications defined in the Concept Dictionary of the EDR Electronic Dictionary. Retrieval performances among not-categorized interpretations, categorized interpretations, and categorized and weighted interpretations using semantic relevance measure were compared, and the categorized semantic relevance approaches showed more stable performances than the not-categorized approach.

Publication: IEICE TRANSACTIONS on Information Vol.E91-D No.11 pp.2638-2646

Publication Date: 2008/11/01

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Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e91-d.11.2638

Type of Manuscript: Special Section PAPER (Special Section on Knowledge, Information and Creativity Support System)

Category: Knowledge Applications and Intelligent User Interfaces

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Heeryon CHO, Toru ISHIDA, Satoshi OYAMA, Rieko INABA, Toshiyuki TAKASAKI, "Assisting Pictogram Selection with Categorized Semantics" in IEICE TRANSACTIONS on Information, vol. E91-D, no. 11, pp. 2638-2646, November 2008, doi: 10.1093/ietisy/e91-d.11.2638.
Abstract: Since participants at both end of the communication channel must share common pictogram interpretation to communicate, the pictogram selection task must consider both participants' pictogram interpretations. Pictogram interpretation, however, can be ambiguous. To assist the selection of pictograms more likely to be interpreted as intended, we propose a categorical semantic relevance measure which calculates how relevant a pictogram is to a given interpretation in terms of a given category. The proposed measure defines similarity measurement and probability of interpretation words using pictogram interpretations and frequencies gathered from a web survey. Moreover, the proposed measure is applied to categorized pictogram interpretations to enhance pictogram retrieval performance. Five pictogram categories used for categorizing pictogram interpretations are defined based on the five first-level classifications defined in the Concept Dictionary of the EDR Electronic Dictionary. Retrieval performances among not-categorized interpretations, categorized interpretations, and categorized and weighted interpretations using semantic relevance measure were compared, and the categorized semantic relevance approaches showed more stable performances than the not-categorized approach.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.11.2638/_p

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@ARTICLE{e91-d_11_2638,
author={Heeryon CHO, Toru ISHIDA, Satoshi OYAMA, Rieko INABA, Toshiyuki TAKASAKI, },
journal={IEICE TRANSACTIONS on Information},
title={Assisting Pictogram Selection with Categorized Semantics},
year={2008},
volume={E91-D},
number={11},
pages={2638-2646},
abstract={Since participants at both end of the communication channel must share common pictogram interpretation to communicate, the pictogram selection task must consider both participants' pictogram interpretations. Pictogram interpretation, however, can be ambiguous. To assist the selection of pictograms more likely to be interpreted as intended, we propose a categorical semantic relevance measure which calculates how relevant a pictogram is to a given interpretation in terms of a given category. The proposed measure defines similarity measurement and probability of interpretation words using pictogram interpretations and frequencies gathered from a web survey. Moreover, the proposed measure is applied to categorized pictogram interpretations to enhance pictogram retrieval performance. Five pictogram categories used for categorizing pictogram interpretations are defined based on the five first-level classifications defined in the Concept Dictionary of the EDR Electronic Dictionary. Retrieval performances among not-categorized interpretations, categorized interpretations, and categorized and weighted interpretations using semantic relevance measure were compared, and the categorized semantic relevance approaches showed more stable performances than the not-categorized approach.},
keywords={},
doi={10.1093/ietisy/e91-d.11.2638},
ISSN={1745-1361},
month={November},}

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TY - JOUR
TI - Assisting Pictogram Selection with Categorized Semantics
T2 - IEICE TRANSACTIONS on Information
SP - 2638
EP - 2646
AU - Heeryon CHO
AU - Toru ISHIDA
AU - Satoshi OYAMA
AU - Rieko INABA
AU - Toshiyuki TAKASAKI
PY - 2008
DO - 10.1093/ietisy/e91-d.11.2638
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2008
AB - Since participants at both end of the communication channel must share common pictogram interpretation to communicate, the pictogram selection task must consider both participants' pictogram interpretations. Pictogram interpretation, however, can be ambiguous. To assist the selection of pictograms more likely to be interpreted as intended, we propose a categorical semantic relevance measure which calculates how relevant a pictogram is to a given interpretation in terms of a given category. The proposed measure defines similarity measurement and probability of interpretation words using pictogram interpretations and frequencies gathered from a web survey. Moreover, the proposed measure is applied to categorized pictogram interpretations to enhance pictogram retrieval performance. Five pictogram categories used for categorizing pictogram interpretations are defined based on the five first-level classifications defined in the Concept Dictionary of the EDR Electronic Dictionary. Retrieval performances among not-categorized interpretations, categorized interpretations, and categorized and weighted interpretations using semantic relevance measure were compared, and the categorized semantic relevance approaches showed more stable performances than the not-categorized approach.
ER -