Semantic Web Dog Food

Presented at: 5th European Semantic Web Conference (ESWC2008)

by Heeryon Cho, Toru Ishida, Toshiyuki Takasaki, Satoshi Oyama

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 concepts defined the Concept Dictionary of EDR Electronic Dictionary. Retrieval performace among non-categorized interpretations, categorized and non-weighted interpretations, and categorized and weighted interpretations using semantic relevance measure were compared, and the weighted and categorized semantic relevance approach exhibited highest precision and recall.

Keywords: categorization, pictogram, semantic tagging, Multimedia, Semantic Web, User Interface

Resource URI on the dog food server: http://data.semanticweb.org/conference/eswc/2008/paper/130
Same as: http://revyu.com/things/eswc-2008-paper-assisting-pictogram-interpretation
Same as: http://semanticweb.org/id/Assisting_Pictogram_Selection_with_Semantic_Interpretation

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