Health ontology system

Kiong, Yip Chi; Palaniappan, Sellappan; Yahaya, Nor Adnan

doi:10.1109/cita.2011.5999506

Cited by 16 publications

(8 citation statements)

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“…With the spread of the application of ontologies, previous studies have built ontologies in various domains. In addition, previous research has promoted automatic [21] or semi-automatic [22,23] construction methods to enhance the efficiency of ontology construction. One study [23] extracted health records from a database and encoded them into ontological structured data for constructing a health ontology system, which shares the information within the healthcare community.…”

Section: The Ontology Construction Methodsmentioning

confidence: 99%

“…In addition, previous research has promoted automatic [21] or semi-automatic [22,23] construction methods to enhance the efficiency of ontology construction. One study [23] extracted health records from a database and encoded them into ontological structured data for constructing a health ontology system, which shares the information within the healthcare community. Another work [24] constructed a law enforcement ontology by collecting thousands of sanitized emails between law enforcement investigators over a three-year period as its data source.…”

Section: The Ontology Construction Methodsmentioning

confidence: 99%

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Building a Taiwan Law Ontology Based on Automatic Legal Definition Extraction

Hwang

Hsueh

Chang

2018

ASI

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Term extraction is an important task that automatically extracts relative terms from the texts in a given domain. A significant number of web applications need to model information for specific topics. In particular, we have explored a Taiwan government website that maintains the Laws & Regulations Database of the Republic of China (R.O.C) to provide the current Chinese law text to the public. However, the main issue is that there is no efficient structured method to handle such a large number of law texts. Therefore, in this paper, we propose a novel approach to extract legal as well as domain-relative terms, and then build a law ontology. We used the current Chinese law text from the Laws & Regulations Database as the data source. We then utilized natural language processing tools and data mining techniques to extract legal keywords and their definitions automatically. Subsequently, we constructed a Taiwan law ontology with the legal keywords and relative definitions. We have extracted 1114 legal keywords with definitions. With the characteristics of an ontology, users can view one keyword with its information and the associated keywords. Furthermore, we provide a service, which includes both the graphical and text interfaces to users on the web, such that a user can readily access the legal information on the Internet.

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Section: The Ontology Construction Methodsmentioning

confidence: 99%

Section: The Ontology Construction Methodsmentioning

confidence: 99%

Building a Taiwan Law Ontology Based on Automatic Legal Definition Extraction

Hwang

Hsueh

Chang

2018

ASI

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“…Ontology is a semantic way to share the structure of information. Recently, ontologies have been adopted in many business and scientific communities [7]. The ontology integration is the state-of-the-art way to share or reuse ontologies which applied the process of ontology matching and ontology merging.…”

Section: Ontologymentioning

confidence: 99%

Heterogeneous Ontology Merging Using Formal Concept Analysis

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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The increasing of ontology usage caused some overlapping ontologies which motivates knowledge engineers to enable the collaborative use of similar ontologies. It introduces the concept of ontology merging. Unfortunately, there are a few researches on heterogeneous ontology merging algorithm and most of them operated in the same or similar domain ontologies. In this paper, we aimed to merge heterogeneous ontologies on relevant domain using Formal Concept Analysis. We took DrugBank and Disease ontologies as our case study. The relationships between drug and disease are presented in this paper in the form of formal lattice. The result of this paper is the merged ontology of the given use case. The result was evaluated by comparing with the medication suggestion. The evaluation shows that the proposed method yielded a correct suggestion accordingly and it will be useful to support knowledge engineer to merge ontologies using corresponded attributes between ontologies.

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“…This approach uses top-down structure to build ontology and performs faster as compare to previous approaches. Y. C. Kiong et al [21] developed an ontology system for Health domain. The Health ontology system provides a method to combine data related to healthcare institutes in the configuration of a shared progressive ontology.…”

Section: Literature Review 21 Usage Of Ontology In Various Domainsmentioning

confidence: 99%

Techniques of Ontology and its Usage in Indian Languages - A Review

Kaur¹,

Sharma²

2015

IJCA

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Ontology is presently an emerging research topic in the field of artificial intelligence, semantic web, and natural language processing, software engineering, and information architecture etc. Manual Ontology building is essentially a time consuming and tedious task. From the last few decades, different ontology building approaches are being used to build ontology either semi-automatically or automatically. This paper review has been carried out/done on the usage of different ontology building approaches in natural language processing and their usage on Indian languages. It is hereby concluded from the review that in Indian languages usage of ontology is very less because of the challenges to build ontology for minority languages.

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Health ontology system

Cited by 16 publications

References 0 publications

Building a Taiwan Law Ontology Based on Automatic Legal Definition Extraction

Building a Taiwan Law Ontology Based on Automatic Legal Definition Extraction

Heterogeneous Ontology Merging Using Formal Concept Analysis

Techniques of Ontology and its Usage in Indian Languages - A Review

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