Abstract
A two dimensional graph is a powerful method for representing a set of objects that usually appears in many sources of literature. Numerous efforts have been made to discover image semantics based on contents of literature. However, conventional methods have not been fully able to satisfy users because a wide variety of techniques are being developed, and each is very useful for enhancing system capabilities in their own way. In this paper, we have developed a method to automatically extract relationships from graphs on the basic of their captions and image content, particularly from graph titles. Furthermore, we improved our idea by applying several technologies such as ontology and a dependency parser. The relationships discovered in a graph are presented in the form of a triple (subject, predicate, object). Our objectives are to find implicit and explicit information in the graph and reduce the semantic gap between an image and literature context. Accuracy was manually estimated to identify the most reliable triple. Based on our results, we concluded that the accuracy via our method was acceptable. Therefore, our method is dependable and worthy of future development.
| Original language | English |
|---|---|
| Title of host publication | KEOD |
| Publisher | SciTePress |
| Pages | 231-238 |
| Number of pages | 8 |
| Volume | 2 |
| ISBN (Print) | 9789897581588 |
| Publication status | Published - 2015 |
| Event | 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015 - Lisbon, Portugal Duration: 2015 Nov 12 → 2015 Nov 14 |
Other
| Other | 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015 |
|---|---|
| Country | Portugal |
| City | Lisbon |
| Period | 15/11/12 → 15/11/14 |
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Keywords
- Dependency Parser
- Edit Distance
- OCR
- Ontology
- Relationship
- Triple
ASJC Scopus subject areas
- Software
Cite this
A proposal for a method of graph ontology by automatically extracting relationships between captions and X- and Y-axis titles. / Kanjanawattana, Sarunya; Kimura, Masaomi.
KEOD. Vol. 2 SciTePress, 2015. p. 231-238.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A proposal for a method of graph ontology by automatically extracting relationships between captions and X- and Y-axis titles
AU - Kanjanawattana, Sarunya
AU - Kimura, Masaomi
PY - 2015
Y1 - 2015
N2 - A two dimensional graph is a powerful method for representing a set of objects that usually appears in many sources of literature. Numerous efforts have been made to discover image semantics based on contents of literature. However, conventional methods have not been fully able to satisfy users because a wide variety of techniques are being developed, and each is very useful for enhancing system capabilities in their own way. In this paper, we have developed a method to automatically extract relationships from graphs on the basic of their captions and image content, particularly from graph titles. Furthermore, we improved our idea by applying several technologies such as ontology and a dependency parser. The relationships discovered in a graph are presented in the form of a triple (subject, predicate, object). Our objectives are to find implicit and explicit information in the graph and reduce the semantic gap between an image and literature context. Accuracy was manually estimated to identify the most reliable triple. Based on our results, we concluded that the accuracy via our method was acceptable. Therefore, our method is dependable and worthy of future development.
AB - A two dimensional graph is a powerful method for representing a set of objects that usually appears in many sources of literature. Numerous efforts have been made to discover image semantics based on contents of literature. However, conventional methods have not been fully able to satisfy users because a wide variety of techniques are being developed, and each is very useful for enhancing system capabilities in their own way. In this paper, we have developed a method to automatically extract relationships from graphs on the basic of their captions and image content, particularly from graph titles. Furthermore, we improved our idea by applying several technologies such as ontology and a dependency parser. The relationships discovered in a graph are presented in the form of a triple (subject, predicate, object). Our objectives are to find implicit and explicit information in the graph and reduce the semantic gap between an image and literature context. Accuracy was manually estimated to identify the most reliable triple. Based on our results, we concluded that the accuracy via our method was acceptable. Therefore, our method is dependable and worthy of future development.
KW - Dependency Parser
KW - Edit Distance
KW - OCR
KW - Ontology
KW - Relationship
KW - Triple
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M3 - Conference contribution
AN - SCOPUS:84961135233
SN - 9789897581588
VL - 2
SP - 231
EP - 238
BT - KEOD
PB - SciTePress
ER -