Abstract
Subfertility in humans is the major problem in the technological world mostly by their habits and foods. There are many treatment methods for the subfertility of females, but the documentation in this field is mostly available in the local language, which cannot be understood by others, and time by time, this knowledge towards the future generation is diminished. The authors have depicted the knowledge using ontology and thereafter the knowledge management portal (KMP) for the indigenous knowledge was developed using the modeled ontology. They believe that the indigenous knowledge management portal (IKMP) will help future generations to get knowledge easily by using this system. They also strongly believe that the IKMP will serve as the experience-sharing tool for the subfertility-related indigenous knowledge.
TopIntroduction
Subfertility is described as the inability to conceive after twelve months of unprotected and regular sexual activity. Males are screened first for causes of subfertility in the medical field. If males are not having problems, females should investigate the cause of their subfertility. Female subfertility is a comparably vast area than male subfertility (Adamson & Baker, 2003). Knowledge Management (KM) about indigenous treatment methods for subfertility is a tedious task for people who are in the technical era. Therefore, we have planned to provide Indigenous Knowledge Management Portal (IKMP).
The main objective of the IKMP is to provide the most relevant information from the doctors’ data to reduce decision-making errors. The proposed KMP is the very first established indigenous system that complies with the knowledge based on Sri Lanka’s indigenous medicine treatment for the subfertility of females. This system adopts the rule-based technique using the current methods of indigenous knowledge about subfertility, which will help the future generation who does not have enough knowledge. This proposed system adopting to the rule-based technique, which is used for efficient decision-making for the subfertility treatment method (E. Kontopoulos, G. Martinopoulos, D. Lazarou, & N. Bassiliades, 2016).
This research aims to help ayurvedic hospitals handle awareness in the treatment of female subfertility by creating an ontology-based solution that identifies, organizes, and facilitates decision-making in the associated expertise by creating an ontology-driven solution that explains all treatment methods from the ground up (Shen, Colloc, Jacquet-Andrieu, Guo, & Liu, 2017). Decision-making and KM of the treatment method of subfertility are difficult concepts to grasp. To provide a solution for the above-mentioned problem, the creation of IKMP can be beneficial to ayurvedic doctors and medical students in terms of KM and decision-making. It will allow ayurvedic hospital doctors and medical students to gain knowledge of treatment options for female subfertility.
The above-mentioned points of inspiration that serve as the foundation for the female subfertility study. The following research questions are considered the research gaps for the selected topic or the analysis of the selected field. Many research questions about “An Ontology-based IKMP for female subfertility” must be investigated in detail to gain knowledge about how to develop ontology-based IKMP for female subfertility. There is currently no ontology-based IKMP for female subfertility. Our system is based on a newly established ontology of the indigenous treatment method of subfertility to improve the IKMP's reasoning ability and strengthen field understanding and data interconnection. The research gap is identified using the mapping study report.
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RQ1: In the sense of female subfertility, what is the actual state of indigenous treatment information dissemination?
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RQ2: How can an IKMP be used to know some information about female subfertility?
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RQ3: How can the evolved KMP’s assessment process can be carried out?
This subfertility concept is often expressed using various terminologies, incomplete, unstructured, and different data formats, records, and expertise do not reach everything. Furthermore, computers must have a firm grasp of the information domain or semantics' meaning. Computer comprehension is possible because of semantic web technology. Ontologies are a powerful framework for representing knowledge implemented in the semantic web. As a result, the ontology could be used to find answers to specific questions in the field of female subfertility (Anbarasi, Naveen, Selvaganapathi, & Mohamed Nowsath Ali, 2013; C.-T. Bau, R.-C. Chen, & C.-Y. Huang, 2014a).
Key Terms in this Chapter
Competency Questions: Interview questions that require candidates to present real-life examples as the foundation of their responses are known as competency-based questions. Candidates should explain why they made specific judgments, how they implemented those decisions, and what happened because of those actions.
Object Properties: The class hierarchy framework in an ontology can be extended with more open-ended relationships known as object and data properties. Object properties link two people (the subject and the object) to a predicate.
Subfertility: Subfertility is the inability to conceive after a year of trying, infertility is defined as the inability to conceive naturally. The chance of conceiving spontaneously exists with subfertility, but it takes longer than usual.
Class: Type, sort, category, and kind are concepts that can be specified as an extension or intension. They are abstract groups, sets, or collections of objects, according to an extensional definition.
Individuals: Individuals in an ontology can be concrete objects like humans, medicine, things, and so on.
Data Properties: The class hierarchy framework in an ontology can be extended with more open-ended relationships known as object and data properties. The predicate associates a single subject with some form of attribute data.
OOPS!: It is Ontology Pitfall Scanner. OOPS! is a web-based tool for detecting potential pitfalls that could lead to modeling errors, irrespective of any ontology development platform.
SPARQL Query: SPARQL is the standard query language and protocol. It can efficiently retrieve information hidden in non-uniform data and stored in diverse forms and sources because it was built to query a wide range of data.
Knowledge Management: Knowledge management (KM) is the process of an organization's knowledge. In here, knowledge is being created, stored, shared, used, and managed. It is a multidisciplinary approach to achieving organizational goals through the most effective use of knowledge.
Ontology: The branch of philosophy known as ontology studies concepts like existence, being, becoming, and reality. It addresses issues such as how entities are classified into fundamental categories and which of these entities exist at the most fundamental level.
DL Query: The DL Query tab allows you to search a classified ontology powerfully and simply. It is included in the standard Protégé Desktop distribution (versions 4, 5, and higher), both as a tab and as a view widget that can be positioned in any other tab.