Weighted Ontology and weighted tree similarity algorithm for diagnosing Diabetes Mellitus

Widhy Hayuhardhika; Nugraha Putra; Sugiyanto; Riyanarto Sarno; Mohamad Sidiq

doi:10.1109/IC3INA.2013.6819185

Weighted Ontology and weighted tree similarity algorithm for diagnosing Diabetes Mellitus

Widhy Hayuhardhika
, Nugraha Putra
, Sugiyanto
, Riyanarto Sarno
, Mohamad Sidiq

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Citations (Scopus)

Abstract

Application knowledge base for diabetes such as expert systems has been developed, but generally using conventional methods that have limitations in representing knowledge. Ontology supports the search of data / information by defining the concept of convergent intended by the user. This study using Diabetes Mellitus Classification based diabetes disease diagnosis from World Health Organization Geneva. This system receives input patient data from user. Then, system will build the patient ontology to represent patient knowledge. We are connecting Java applications to Protégé using OWL API. Then, system will calculate the weight of an ontology based on density. This system use JENA Inference Engine and working memory area for reasoning. The system would then do process similarity matching with Ontology Diabetes Mellitus using weighted tree similarity algorithm. Ontology has the highest similarity value will be the proposed diagnosis. Results of this study show that the representation in the form of OWL ontology using weighted ontology and weighted tree similarity algorithm can be used to represent knowledge about diabetes mellitus.

Original language	English
Title of host publication	Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications
Subtitle of host publication	"Recent Challenges in Computer, Control and Informatics", IC3INA 2013
Publisher	IEEE Computer Society
Pages	267-272
Number of pages	6
ISBN (Print)	9781479910786
DOIs	https://doi.org/10.1109/IC3INA.2013.6819185
Publication status	Published - 2013
Event	2013 International Conference on Computer, Control, Information and Its Applications, IC3INA 2013 - Jakarta, Indonesia Duration: 19 Nov 2013 → 21 Nov 2013

Publication series

Name	Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: "Recent Challenges in Computer, Control and Informatics", IC3INA 2013

Conference

Conference	2013 International Conference on Computer, Control, Information and Its Applications, IC3INA 2013
Country/Territory	Indonesia
City	Jakarta
Period	19/11/13 → 21/11/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

diabetes mellitus
semantic search
weighted ontology
weighted tree similarity

Access to Document

10.1109/IC3INA.2013.6819185

Cite this

Hayuhardhika, W., Putra, N., Sugiyanto, Sarno, R., & Sidiq, M. (2013). Weighted Ontology and weighted tree similarity algorithm for diagnosing Diabetes Mellitus. In Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: "Recent Challenges in Computer, Control and Informatics", IC3INA 2013 (pp. 267-272). Article 6819185 (Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: "Recent Challenges in Computer, Control and Informatics", IC3INA 2013). IEEE Computer Society. https://doi.org/10.1109/IC3INA.2013.6819185

Hayuhardhika, Widhy ; Putra, Nugraha ; Sugiyanto et al. / Weighted Ontology and weighted tree similarity algorithm for diagnosing Diabetes Mellitus. Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: "Recent Challenges in Computer, Control and Informatics", IC3INA 2013. IEEE Computer Society, 2013. pp. 267-272 (Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: "Recent Challenges in Computer, Control and Informatics", IC3INA 2013).

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title = "Weighted Ontology and weighted tree similarity algorithm for diagnosing Diabetes Mellitus",

abstract = "Application knowledge base for diabetes such as expert systems has been developed, but generally using conventional methods that have limitations in representing knowledge. Ontology supports the search of data / information by defining the concept of convergent intended by the user. This study using Diabetes Mellitus Classification based diabetes disease diagnosis from World Health Organization Geneva. This system receives input patient data from user. Then, system will build the patient ontology to represent patient knowledge. We are connecting Java applications to Prot{\'e}g{\'e} using OWL API. Then, system will calculate the weight of an ontology based on density. This system use JENA Inference Engine and working memory area for reasoning. The system would then do process similarity matching with Ontology Diabetes Mellitus using weighted tree similarity algorithm. Ontology has the highest similarity value will be the proposed diagnosis. Results of this study show that the representation in the form of OWL ontology using weighted ontology and weighted tree similarity algorithm can be used to represent knowledge about diabetes mellitus.",

keywords = "diabetes mellitus, semantic search, weighted ontology, weighted tree similarity",

author = "Widhy Hayuhardhika and Nugraha Putra and Sugiyanto and Riyanarto Sarno and Mohamad Sidiq",

year = "2013",

doi = "10.1109/IC3INA.2013.6819185",

language = "English",

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Hayuhardhika, W, Putra, N, Sugiyanto, , Sarno, R & Sidiq, M 2013, Weighted Ontology and weighted tree similarity algorithm for diagnosing Diabetes Mellitus. in Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: "Recent Challenges in Computer, Control and Informatics", IC3INA 2013., 6819185, Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: "Recent Challenges in Computer, Control and Informatics", IC3INA 2013, IEEE Computer Society, pp. 267-272, 2013 International Conference on Computer, Control, Information and Its Applications, IC3INA 2013, Jakarta, Indonesia, 19/11/13. https://doi.org/10.1109/IC3INA.2013.6819185

Weighted Ontology and weighted tree similarity algorithm for diagnosing Diabetes Mellitus. / Hayuhardhika, Widhy; Putra, Nugraha; Sugiyanto et al.
Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: "Recent Challenges in Computer, Control and Informatics", IC3INA 2013. IEEE Computer Society, 2013. p. 267-272 6819185 (Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: "Recent Challenges in Computer, Control and Informatics", IC3INA 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Weighted Ontology and weighted tree similarity algorithm for diagnosing Diabetes Mellitus

AU - Hayuhardhika, Widhy

AU - Putra, Nugraha

AU - Sugiyanto,

AU - Sarno, Riyanarto

AU - Sidiq, Mohamad

PY - 2013

Y1 - 2013

N2 - Application knowledge base for diabetes such as expert systems has been developed, but generally using conventional methods that have limitations in representing knowledge. Ontology supports the search of data / information by defining the concept of convergent intended by the user. This study using Diabetes Mellitus Classification based diabetes disease diagnosis from World Health Organization Geneva. This system receives input patient data from user. Then, system will build the patient ontology to represent patient knowledge. We are connecting Java applications to Protégé using OWL API. Then, system will calculate the weight of an ontology based on density. This system use JENA Inference Engine and working memory area for reasoning. The system would then do process similarity matching with Ontology Diabetes Mellitus using weighted tree similarity algorithm. Ontology has the highest similarity value will be the proposed diagnosis. Results of this study show that the representation in the form of OWL ontology using weighted ontology and weighted tree similarity algorithm can be used to represent knowledge about diabetes mellitus.

AB - Application knowledge base for diabetes such as expert systems has been developed, but generally using conventional methods that have limitations in representing knowledge. Ontology supports the search of data / information by defining the concept of convergent intended by the user. This study using Diabetes Mellitus Classification based diabetes disease diagnosis from World Health Organization Geneva. This system receives input patient data from user. Then, system will build the patient ontology to represent patient knowledge. We are connecting Java applications to Protégé using OWL API. Then, system will calculate the weight of an ontology based on density. This system use JENA Inference Engine and working memory area for reasoning. The system would then do process similarity matching with Ontology Diabetes Mellitus using weighted tree similarity algorithm. Ontology has the highest similarity value will be the proposed diagnosis. Results of this study show that the representation in the form of OWL ontology using weighted ontology and weighted tree similarity algorithm can be used to represent knowledge about diabetes mellitus.

KW - diabetes mellitus

KW - semantic search

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KW - weighted tree similarity

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M3 - Conference contribution

AN - SCOPUS:84902491444

SN - 9781479910786

T3 - Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: "Recent Challenges in Computer, Control and Informatics", IC3INA 2013

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BT - Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications

PB - IEEE Computer Society

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Y2 - 19 November 2013 through 21 November 2013

ER -

Hayuhardhika W, Putra N, Sugiyanto , Sarno R, Sidiq M. Weighted Ontology and weighted tree similarity algorithm for diagnosing Diabetes Mellitus. In Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: "Recent Challenges in Computer, Control and Informatics", IC3INA 2013. IEEE Computer Society. 2013. p. 267-272. 6819185. (Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: "Recent Challenges in Computer, Control and Informatics", IC3INA 2013). doi: 10.1109/IC3INA.2013.6819185

Weighted Ontology and weighted tree similarity algorithm for diagnosing Diabetes Mellitus

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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