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    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Developing Ontologies Cheat Sheet}}}} \\
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Main Tasks in Ontology Development}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{1) {\bf{Conceptualization:}} Determine the subject and extent of the data model. Outline fundamental principles and determine relationships among the notions.} \tn 
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\mymulticolumn{1}{x{5.377cm}}{2) {\bf{Requirements Analysis:}} Analyze requirements with a focus on purpose, use cases, expressiveness, types of queries, and reasoning services needed.} \tn 
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\mymulticolumn{1}{x{5.377cm}}{3) {\bf{Ontology Architecture:}} Design an ontology architecture, considering modularity and the choice of a distributed or non-distributed framework.} \tn 
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\mymulticolumn{1}{x{5.377cm}}{4) {\bf{Representation Language:}} The primary representation language selected for the ontology is the OWL (Web Ontology Language). When encoding the conceptual framework using OWL, particular characteristics within the language, like utilizing classifications, attributes, and entities, must be considered.} \tn 
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\mymulticolumn{1}{x{5.377cm}}{5) {\bf{Foundational Ontology and Modeling Decisions:}} Consider the use of a foundational ontology and make modeling decisions regarding attributes and the representation of n-aries as relations or classes.} \tn 
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\mymulticolumn{1}{x{5.377cm}}{6) {\bf{Ontology Reuse and Alignment:}} Consider reusing existing domain ontology, top-domain level ontology, and ontology design patterns, using ontology matching techniques for alignment if necessary.} \tn 
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\mymulticolumn{1}{x{5.377cm}}{7) {\bf{Semi-Automated Approaches:}} Explore semi-automated bottom-up approaches, tools, and language transformations, and modify the ontology to align with decisions made in previous steps.} \tn 
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Main Tasks in Ontology Development (cont)}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{8) {\bf{Formalization and Reasoning:}} For the formal representation of the ontology, classifications, object characteristics, restrictions, guidelines, and comments. Utilize automated reasoners for detecting and maintaining consistency, and make use of ontological reasoning services for ensuring high quality.} \tn 
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\mymulticolumn{1}{x{5.377cm}}{9) {\bf{Ontology Versions and Deployment:}} Generate versions of the ontology in other ontology languages or create "lite" versions as required. Deploy the ontology and establish maintenance and update procedures.} \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Overview of Approach}}  \tn
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{\bf{Conceptualization}} & - Determine the domain and range of the ontology via domain analysis and involving stakeholders in the analysis. \tn 
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 & - Specify the key ideas and links within them employing conceptual modeling methods. \tn 
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{\bf{Requirements Analysis}} & - Examine the needs related to the conceptual framework, taking into account its objective, possible scenarios of application, and articulate features. \tn 
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 & - Identify the categories for inquiries and logical operations that depend on the ontology. \tn 
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{\bf{Ontology Architecture}} & - Creating the ontology process involves taking into account factors for example, modularity and deciding between a centralised or dispersed approach. \tn 
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 & - Decide on the logical framework or language for expressing that will be utilized, like Web Ontology Language or RDF format. \tn 
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{\bf{Representation Language}} & - Select a main notation system according to the needs of the ontology and the abilities of the tools and reasoners that are accessible. \tn 
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 & - Take into account encoding unique characteristics of the selected language, like modeling chronological or fuzzy facets. \tn 
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{\bf{Foundational Ontology and Modelling Decisions}} & - Assess the appropriateness of a basic knowledge structure and establish design choices concerning properties, connections, and multi-valued relationships. \tn 
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 & - Think about whether to illustrate n-ary relationships as categories or connections. Decide on the suitable degree of specificity for representation. \tn 
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{\bf{Ontology Reuse and Alignment}} & - Investigate the utilization of current ontologies, specialized ontologies, overarching ontologies, or ontology blueprint. \tn 
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 & - Utilize ontology matching methods for aligning and combine the ontologies, dealing with conflicts and contradictions. \tn 
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{\bf{Semi-Automated Approaches}} & - Use partially automated ascending methods, instruments, and linguistic changes to support in the creation procedure. \tn 
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 & - Refine and adjust the structure according to the choices selected in previous stages. \tn 
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{\bf{Formalization and Reasoning}} & - Officially depict the information structure through the inclusion of categories, attributes, limitations, and regulations. \tn 
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 & - Use automated reasoners for identifying inconsistencies, confirm the logical theory, and conduct ontological reasoning services for quality inspections \tn 
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{\bf{Ontology Versions and Deployment}} & - Contemplate making different forms of the knowledge base using different knowledge representation systems. Moreover, it is possible to generate simplified for the ontology that exhibit lower complexity. \tn 
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 & - Develop a deployment plan, that consist of maintenance and update methods to ensure the ontology remains updated. Furthermore, contemplate converting the ontology to different ontology languages or generating "compact" versions that are less complex. \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Foundational Ontologies}}  \tn
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1. {\bf{Formal and High-Level}} & - Basic ontologies are created using a structured representation language. Additionally, they exhibit a high abstraction level. Their goal to acquire wide-ranging, essential information that is applicable in different fields. \tn 
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 & - These are not connected to any specific software or topic realm. These individuals offer a broad conceptual structure that can function as a groundwork for developing specialized ontologies \tn 
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2. {\bf{General and Core Concepts}} & - Base ontologies prioritize capturing the fundamental, universal ideas and connections that are the foundation of diverse domains. \tn 
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 & - These offer a shared language and series of interactions that can be passed on within diverse disciplines. \tn 
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3. {\bf{Philosophical and Theoretical Grounding}} & - Foundational ontologies frequently rely on conceptual and theoretical principles to create their conceptual basis. But, it is crucial to understand that these concepts may differ contingent upon the specialized structure in progress. \tn 
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 & - It is possible that they include ideas from philosophical, rationality, the science of cognition, and additional pertinent fields. \tn 
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4. {\bf{Ontological Commitment}} & - Basic ontologies possess a strong ontological obligation. Their goal to depict the basic essence of existence and the classifications of things that are in existence in the realm. \tn 
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 & - They aim for logical coherence and consistency. \tn 
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5. {\bf{Interoperability and Integration}} & - Basic ontologies enable the compatibility and merging of diverse ontologies. \tn 
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 & - Through offering a mutual reference point, they facilitate the synchronization and integration of domain ontologies. \tn 
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6. {\bf{Reusability and Extensibility}} & - Core ontologies are created to be adaptable elements \tn 
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 & - Offers a strong base that can be prolonged or customized to fulfill the requirements of specialized areas. \tn 
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 & - Area ontologies are able to align with and extend core ontologies. \tn 
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7. {\bf{Reasoning and Inference}} & - Core knowledge structures regularly facilitate complex reasoning and inference skills. \tn 
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 & - These allow automated deduction across knowledge bases via delivering a precisely defined logical structure and inference regulations. \tn 
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\mymulticolumn{2}{x{5.377cm}}{Primary goal of core ontologies is to establish collective understanding of essential ideas, connections, and logical principles across diverse areas, ensuring consistency and compatibility in knowledge representation and reasoning systems. Serves as  guide \& foundation for developing ontologies, providing a strong base for modeling and integrating specialized domain expertise. Core ontologies enhance ontology interoperability, consistency, and quality in specialized domains.}  \tn 
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1. {\bf{Knowledge Acquisition Bottleneck}} & - The knowledge acquisition bottleneck refers to the difficulty of obtaining domain knowledge and converting it into a formal ontology representation. This can be time-consuming and challenging to collect and encode all the needed details by manual means. \tn 
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2. {\bf{Bottom-Up Approach}} & - Top-down ontology development involves commencing with pre-existing data sources. These resources might consist of papers, data stores, and historical systems. As an alternative to putting all your trust in manual learning process. The emphasis is focused on gathering pertinent data from these resources. \tn 
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3. **Semi-Automated Techniques & - Incremental methods utilize different partially automated methods to support knowledge retrieval and ontology creation. These methods might involve NLP, text extraction, data extraction, and ML algorithms. \tn 
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4.{\bf{Language Transformations}} & - Gradual techniques often involve converting the acquired information into an appropriate conceptual model. Instances of similar languages comprise SPARQL or RDFS. The transformation enables the extracted data to be incorporated inside the ontology. \tn 
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5. {\bf{Ontology Refinement}} & - After creating the initial ontology using the bottom-up strategy, it may be necessary to make additional adjustments and align it with the desired conceptual framework. This involves identifying any inconsistencies or gaps in the knowledge base and making necessary modifications to ensure accuracy and completeness. Furthermore, the ontology should be synchronized with the chosen conceptual model to enable compatibility and interoperability with other systems or ontologies. The knowledge representation can be modified to adhere to conceptual principles and align with existing ontologies. \tn 
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6. {\bf{Iterative Process}} & - Ontology development from the ground up is a continuous method that entails improving the ontology through recurring phases of extracting, transforming, and constructing knowledge. Every cycle adds to the betterment and enhancement of the knowledge base, guaranteeing its precision and importance. Every cycle strives to improve the ontology's level of excellence and scope \tn 
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7. {\bf{Expert Involvement}} & - Although grassroots methods reduce certain amount of the load in acquiring knowledge. Subject matter experts play an important part in checking and perfecting the acquired information. They guarantee the precision and significance. \tn 
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8. {\bf{Integration with Top-Level Ontologies}} & - Base-up ontologies can be linked with top-level ontologies. This includes fundamental ontologies or advanced ontologies, which help establish a expanded conceptual framework. The combination supports integration and stability throughout ontologies. \tn 
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\mymulticolumn{2}{x{5.377cm}}{Bottom-up development overcomes knowledge acquisition challenges by leveraging available data sources and employing semi-automated methods. It facilitates efficient data retrieval and integration, making ontology creation more flexible and less reliant on manual effort. It improves hierarchical techniques and supports the creation of richer semantic models, particularly in domains with abundant information but limited explicit domain models.}  \tn 
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\mymulticolumn{1}{x{5.377cm}}{1.	Keet, C. M. (2020) An Introduction to Ontology Engineering. v1,5. College Publications.} \tn 
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% That's all folks
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