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This variability makes it difficult to reuse existing explanation techniques and systems when facing new problems. The paper introduces a formal model that orchestrates the elements of an explanation system and presents its formalisation as the iSeeOnto ontology, enabling reuse of explanation experiences across domains via case-based reasoning and supporting validation through literature classification.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/conceptual-modelling-of-explanation-experiences-through-the-iseeonto-ontology/270940/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/conceptual-modelling-of-explanation-experiences-through-the-iseeonto-ontology/270940.png","ImageObject",300,407,{"name":92,"@type":93},"Liam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is building an explanation system in XAI challenging?","Question",{"text":113,"@type":114},"Explanation needs vary across domains, users, goals, available data, and suitable explainers, making the adaptation of existing solutions difficult.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What is the iSeeOnto ontology intended to do?",{"text":118,"@type":114},"It formalises a conceptual model of explanation experiences, representing key dimensions such as the AI model to explain, the explainer required, and the user who needs the explanation.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the work support reusing explanation experiences?",{"text":122,"@type":114},"By enabling explanation experiences to be described as cases, iSeeOnto supports case-based reasoning to transfer knowledge and reuse prior solutions from other domains.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},270940,1789458692,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":105,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":41,"preview_page_count":4},8796095461564,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Conceptual Modelling of Explanation Experiences Through the iSeeOnto Ontology  \nMarta Caro-Martínez1, * , Anjana Wijekoon2, Juan A. Recio-García 1 , David Corsar2 and Ikechukwu Nkisi-Orji2  \n1 Department of Software Engineering and Artificial Intelligence, Instituto de Tecnologías del Conocimiento, Universidad Complutense de Madrid, Spain  \n2 School of Computing, Robert Gordon University, Aberdeen, Scotland  \nAbstract  \nExplainable Artificial Intelligence is a big research field required in many situations where we need to understand Artificial Intelligence behaviour. However, each explanation need is unique which makes it difficult to apply explanation techniques and solutions that are already implemented when faced with anew problem. Therefore, the task to implement an explanation system can be very challenging because we need to take the AI model into account, user’s needs and goals, available data, suitable explainers, etc. In this work, we propose a formal model to define and orchestrate all the elements involved in an explanation system, and make a novel contribution regarding the formalisation of this model as the iSeeOnto ontology. This ontology not only enables the conceptualisation of a wide range of explanation systems, but also supports the application of Case-Based Reasoning as a knowledge transfer approach that reuses previous explanation experiences from unrelated domains. To demonstrate the suitability of the proposed model, we present an exhaustive validation by classifying reference explanation systems found in the literature into the iSeeOnto ontology.  \nKeywords  \nXAI, Ontology, Conceptual Model, CBR  \n1. Introduction  \nXAI or eXplainable Artificial Intelligence is one of the most remarkable fields in computer science and Artificial Intelligence (AI) due to its application in many critical domains such as medicine, defence or industry [? ] . Understanding the reason behind the behaviour of an AI can make a difference in the effectiveness of that model. A central challenge to provisioning XAI is the variety of situations where we need explanations, regarding different domains, users, goals or problems to solve [1] . This exacerbates the decision making task of selecting the right explanation approaches to apply. The iSee (Intelligent Sharing of Explanation Experience by Users for Users) project1 was proposed with the objective of tackling this challenge. The final goal of the iSee project is to build a platform where users can share and reuse explanation  \nICCBR XCBR’22: 4th Workshop on XCBR: Case-based Reasoning for the Explanation of Intelligent Systems at ICCBR-2022, September, 2022, Nancy, France  \n* Corresponding author.  \n$ [martcaro@ucm.es](martcaro@ucm.es) (M. Caro-Martínez); [a.wijekoon1@rgu.ac.uk](a.wijekoon1@rgu.ac.uk) (A. Wijekoon); [jareciog@ucm.es](jareciog@ucm.es)  \n(J. A. Recio-García); [d.corsar1@rgu.ac.uk](d.corsar1@rgu.ac.uk) (D. Corsar); [i.nkisi-orji@rgu.ac.uk](i.nkisi-orji@rgu.ac.uk) (I. Nkisi-Orji)  \n © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) . CWPEURorkroceshopedings http://ceurISSN 1613-ws-0073.org CEUR Workshop Proceedings ([CEUR-WS.org](CEUR-WS.org))  \n1[http://isee4xai.com](http://isee4xai.com)  \nexperiences, a concept that comprises all the elements involved in the explanation process: input data, user’s goals, human-computer interaction, AI model, explanation algorithms, explanation performance, etc. Here, Case-Based Reasoning (CBR) appears as a natural approach to reuse explanation experiences developed by other users for different models and domains.  \nTo support this CBR process, one of the main results we want to achieve in iSee is the formalisation of a conceptual model that defines an explanation experience. This model has been instantiated into the iSeeOnto ontology, that is able to represent the different conceptual dimensions that describe an explanation experience, mainly: (1) ","cbCaiioaJGPkFcpC","https://ap.wps.com/l/cbCaiioaJGPkFcpC","pdf",3581129,"English","# Introduction\n## Background\n# iSeeOnto ontology and conceptual model\n## Validation and case-based reasoning use\n## Conclusions","[{\"question\":\"Why is building an explanation system in XAI challenging?\",\"answer\":\"Explanation needs vary across domains, users, goals, available data, and suitable explainers, making the adaptation of existing solutions difficult.\"},{\"question\":\"What is the iSeeOnto ontology intended to do?\",\"answer\":\"It formalises a conceptual model of explanation experiences, representing key dimensions such as the AI model to explain, the explainer required, and the user who needs the explanation.\"},{\"question\":\"How does the work support reusing explanation experiences?\",\"answer\":\"By enabling explanation experiences to be described as cases, iSeeOnto supports case-based reasoning to transfer knowledge and reuse prior solutions from other domains.\"}]","Conceptual Modelling of Explanation Experiences Through the iSeeOnto Ontology | PDF"]