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The paper aims to elaborate a general theoretical framework to discuss foundational characteristics of explaining, as well as how solutions (events) would be justified (explained). The approach, epistemological in nature, is based on the phenomenological-based approach to complex systems reconstruction (which encompasses complex AI-based systems). The formalized perspective is close to ideas from argumentation and induction (as learning). The soundness and limitations of the approach are addressed from Knowledge representation and reasoning paradigm and, in particular, from Computational Logic point of view. With regard to the latter, the proposal is intertwined with several related notions of explanation coming from the Philosophy of Science.<\/jats:p>","DOI":"10.1007\/s40747-021-00613-5","type":"journal-article","created":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T07:03:04Z","timestamp":1641279784000},"page":"1579-1601","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Knowledge representation for explainable artificial intelligence"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0528-9459","authenticated-orcid":false,"given":"Joaqu\u00edn","family":"Borrego-D\u00edaz","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6622-271X","authenticated-orcid":false,"given":"Juan","family":"Gal\u00e1n P\u00e1ez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"key":"613_CR1","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"A\u00a0Barredo Arrieta","year":"2020","unstructured":"Arrieta A\u00a0Barredo, D\u00edaz-Rodr\u00edguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, Garcia S, Gil-Lopez S, Molina D, Benjamins R, Chatila R, Herrera F (2020) Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. 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