Towards a Soft Evaluation and Refinement of Tagging in Digital Humanities

Aranda-Corral, Gonzalo A.; Borrego-Dí­az, Joaquín; Galán-Páez, Juan

doi:10.1007/978-3-319-19719-7_7

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Partial versions of the framework have been presented by authors and applied in several case studies of a different Fig. 1 FCA-based model for (qualitative) reasoning with Complex Systems nature [14][15][16][17][18]. However, until now we had not presented a unified vision of the model, as well as its contextualization within the problem of explanation.…”

Section: Towards An Abstract Epistemological Modelmentioning

confidence: 99%

Knowledge representation for explainable artificial intelligence

Borrego-Díaz

Galán-Páez

2022

Complex Intell. Syst.

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Alongside the particular need to explain the behavior of black box artificial intelligence (AI) systems, there is a general need to explain the behavior of any type of AI-based system (the explainable AI, XAI) or complex system that integrates this type of technology, due to the importance of its economic, political or industrial rights impact. The unstoppable development of AI-based applications in sensitive areas has led to what could be seen, from a formal and philosophical point of view, as some sort of crisis in the foundations, for which it is necessary both to provide models of the fundamentals of explainability as well as to discuss the advantages and disadvantages of different proposals. The need for foundations is also linked to the permanent challenge that the notion of explainability represents in Philosophy of Science. 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.

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Section: Towards An Abstract Epistemological Modelmentioning

confidence: 99%

Knowledge representation for explainable artificial intelligence

Borrego-Díaz

Galán-Páez

2022

Complex Intell. Syst.

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Concept learning consistency under three-way decision paradigm

Aranda-Corral

Borrego-Díaz

Galán-Páez

2022

Int. J. Mach. Learn. & Cyber.

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Concept Mining is one of the main challenges both in Cognitive Computing and in Machine Learning. The ongoing improvement of solutions to address this issue raises the need to analyze whether the consistency of the learning process is preserved. This paper addresses a particular problem, namely, how the concept mining capability changes under the reconsideration of the hypothesis class. The issue will be raised from the point of view of the so-called Three-Way Decision (3WD) paradigm. The paradigm provides a sound framework to reconsider decision-making processes, including those assisted by Machine Learning. Thus, the paper aims to analyze the influence of 3WD techniques in the Concept Learning Process itself. For this purpose, we introduce new versions of the Vapnik-Chervonenkis dimension. Likewise, to illustrate how the formal approach can be instantiated in a particular model, the case of concept learning in (Fuzzy) Formal Concept Analysis is considered.

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Towards a Soft Evaluation and Refinement of Tagging in Digital Humanities

Cited by 2 publications

References 13 publications

Knowledge representation for explainable artificial intelligence

Knowledge representation for explainable artificial intelligence

Concept learning consistency under three-way decision paradigm

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