A proposal for a method of graph ontology by automatically extracting relationships between captions and X- and Y-axis titles

Sarunya Kanjanawattana, Masaomi Kimura

研究成果: Conference contribution

1 引用 (Scopus)

抄録

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.

元の言語English
ホスト出版物のタイトルKEOD
出版者SciTePress
ページ231-238
ページ数8
2
ISBN(印刷物)9789897581588
出版物ステータスPublished - 2015
イベント7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015 - Lisbon, Portugal
継続期間: 2015 11 122015 11 14

Other

Other7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015
Portugal
Lisbon
期間15/11/1215/11/14

Fingerprint

Ontology
Semantics

ASJC Scopus subject areas

  • Software

これを引用

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. 巻 2 SciTePress, 2015. p. 231-238.

研究成果: Conference contribution

Kanjanawattana, S & Kimura, M 2015, A proposal for a method of graph ontology by automatically extracting relationships between captions and X- and Y-axis titles. : KEOD. 巻. 2, SciTePress, pp. 231-238, 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015, Lisbon, Portugal, 15/11/12.
@inproceedings{a3aad3c4070e472ba0552b6ab7c0f631,
title = "A proposal for a method of graph ontology by automatically extracting relationships between captions and X- and Y-axis titles",
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.",
keywords = "Dependency Parser, Edit Distance, OCR, Ontology, Relationship, Triple",
author = "Sarunya Kanjanawattana and Masaomi Kimura",
year = "2015",
language = "English",
isbn = "9789897581588",
volume = "2",
pages = "231--238",
booktitle = "KEOD",
publisher = "SciTePress",

}

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

UR - http://www.scopus.com/inward/record.url?scp=84961135233&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84961135233&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84961135233

SN - 9789897581588

VL - 2

SP - 231

EP - 238

BT - KEOD

PB - SciTePress

ER -