Publicação
Explaining Predictions from Node Classification with Knowledge Graph Embeddings
| datacite.subject.fos | Departamento de Informática | pt_PT |
| dc.contributor.advisor | Pesquita, Cátia, 1980- | |
| dc.contributor.author | Paulino, Filipe José Bastos Benvindo | |
| dc.date.accessioned | 2024-11-20T11:28:47Z | |
| dc.date.available | 2024-11-20T11:28:47Z | |
| dc.date.issued | 2024 | |
| dc.date.submitted | 2024 | |
| dc.description | Tese de mestrado, Ciência de Dados, 2024, Universidade de lisboa, Faculdade de Ciências | pt_PT |
| dc.description.abstract | Current AI solutions, especially black-box models, show a set of limitations that may include biases and lack of transparency. Especially in high-risk applications, such as medical diagnosis or infrastructure management, the need to understand the reasoning of such models gives rise to post-hoc explanation methods. Knowledge Graphs represent real-world entities and their relations in a graph structure with explicit semantics and support several applications from recommendation systems to question-answering and data mining. Knowledge Graph Embeddings have emerged as a solution for representing entities for downstream tasks, such as link prediction or node classification, by producing vector representations of entities in an embedding space that aims to conserve syntactic and structural properties while being amenable to further computation. However, they do so at the cost of the inherent explainability of knowledge graphs: most approaches result in meaningless vectors. This work builds on current approaches in order to explain node classification prediction models based on Knowledge Graph Embeddings, a problem not tackled by current methods. It applies Explainable AI paradigms to design, implement and test two novel methods, LOFI, and C-KEE, that are perturbation-based and that use counterfactuals, in the form of necessary and sufficient explanations, to identify the set of facts or entities present in the Knowledge Graph that more profoundly affect an entity’s classification. Extensive experiments were conducted using different benchmark datasets. In the end, LoFI showed mixed results, being able to successfully generate sufficient explanations only. With C-KEE, a superior solution was achieved, given that it significantly outperformed baselines in almost all scenarios, including several representative Knowledge Graph embedding methods, while being able to produce empirically sound and intuitive explanations in minimal computational processing time. | pt_PT |
| dc.identifier.tid | 203739892 | |
| dc.identifier.uri | http://hdl.handle.net/10400.5/95461 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | redes de conhecimento | pt_PT |
| dc.subject | vectores de redes de conhecimento | pt_PT |
| dc.subject | redes neuronais para grafos | pt_PT |
| dc.subject | aprendizagem automática | pt_PT |
| dc.subject | inteligência artificial explicável | pt_PT |
| dc.subject | Teses de mestrado - 2024 | pt_PT |
| dc.title | Explaining Predictions from Node Classification with Knowledge Graph Embeddings | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Tese de mestrado em Ciência de Dados | pt_PT |