Supporting Explainable AI on Semantic Constraint Validation

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dc.contributor.advisor Rohde, Philipp D.
dc.contributor.advisor Vidal, Maria-Ester Gercke, Julian Alexander eng 2022-07-18T10:49:23Z 2022-07-18T10:49:23Z 2022-06-14
dc.identifier.citation Gercke, Julian Alexander: Supporting Explainable AI on Semantic Constraint Validation. Hannover : Gottfried Wilhelm Leibniz Universität, Master Thesis, 2022, 126 S. DOI: eng
dc.description.abstract There is a rising number of knowledge graphs available published through various sources. The enormous amount of linked data strives to give entities a semantic context. Using SHACL, the entities can be validated with respect to their context. On the other hand, an increasing usage of AI models in productive systems comes with a great responsibility in various areas. Predictive models like linear, logistic regression, and tree-based models, are still frequently used as they come with a simple structure, which allows for interpretability. However, explaining models includes verifying whether the model makes predictions based on human constraints or scientific facts. This work proposes to use the semantic context of the entities in knowledge graphs to validate predictive models with respect to user-defined constraints; therefore, providing a theoretical framework for a model-agnostic validation engine based on SHACL. In a second step, the model validation results are summarized in the case of a decision tree and visualized model-coherently. Finally, the performance of the framework is evaluated based on a Python implementation. eng
dc.language.iso eng eng
dc.publisher Hannover : Gottfried Wilhelm Leibniz Universität
dc.rights CC BY 3.0 DE eng
dc.rights.uri eng
dc.subject Artificial Intelligence eng
dc.subject Explainable AI eng
dc.subject Semantic Constraint Validation eng
dc.subject Machine Learning eng
dc.subject Decision Trees eng
dc.subject SHACL ger
dc.subject Künstliche Intelligenz ger
dc.subject SPARQL ger
dc.subject Maschinelles Lernen ger
dc.subject Entscheidungsbäume ger
dc.subject Integritätsconstraints ger
dc.subject Validierung ger
dc.subject.ddc 004 | Informatik eng
dc.title Supporting Explainable AI on Semantic Constraint Validation eng
dc.type MasterThesis eng
dc.type Text eng
dcterms.extent 126 S.
dc.description.version publishedVersion eng
tib.accessRights frei zug�nglich eng

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