Name
Abstract | ||
|---|---|---|
Participants at both end of the communication channel must share common pictogram interpretation to communicate. However, because pictogram interpretation can be ambiguous, pictogram communication can sometimes be difficult. To assist human task of selecting pictograms more likely to be interpreted as intended, we propose a semantic relevance measure which calculates how relevant a pictogram is to a given interpretation. The proposed measure uses pictogram interpretations and frequencies gathered from a web survey to define probability and similarity measurement of interpretation words. Moreover, the proposed measure is applied to categorized pictogram interpretations to enhance retrieval performance. Five pictogram categories are created using the five first level categories defined in the Concept Dictionary of EDR Electronic Dictionary. Retrieval performance among not-categorized interpretations, categorized and not-weighted interpretations, and categorized and weighted interpretations using semantic relevance measure were compared, and the categorized and weighted semantic relevance retrieval approach exhibited the highest F1 measure and recall. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-68234-9_8 | ESWC |
Keywords | Field | DocType |
pictogram communication,common pictogram interpretation,not-categorized interpretation,retrieval performance,pictogram interpretation,interpretation word,proposed measure,f1 measure,semantic relevance measure,pictogram category,semantic interpretation,pictogram selection,communication channels | Web survey,Data mining,Pictogram,Information retrieval,Computer science,Semantic relevance,Semantic interpretation,Natural language processing,Artificial intelligence,Electronic dictionary,Recall | Conference |
Volume | ISSN | ISBN |
5021 | 0302-9743 | 3-540-68233-3 |
Citations | PageRank | References |
3 | 0.48 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heeryon Cho | 1 | 70 | 9.38 |
| Ishida, Toru | 2 | 3021 | 490.20 |
| Toshiyuki Takasaki | 3 | 31 | 7.59 |
| Satoshi Oyama | 4 | 120 | 10.02 |