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Novel Perspectives and Applications of Knowledge Graph Embeddings: From Link Prediction to Risk Assessment and Explainability
Conference proceeding   Open access

Novel Perspectives and Applications of Knowledge Graph Embeddings: From Link Prediction to Risk Assessment and Explainability

Hegler Correa Tissot
RESEARCH CHALLENGES IN INFORMATION SCIENCE (RCIS 2021), v 415, pp 91-106
01 Jan 2021
DOI: https://doi.org/10.1007/978-3-030-75018-3_6
url
https://discovery.ucl.ac.uk/10141163/1/_PAPER__RCIS_2021__KRAL__Knowledge_Graph_Embeddings__From_Link_Prediction_to_Risk_Assessment__FINAL_.pdfView
SubmittedOpen Access (License Unspecified),  Open

Abstract

Computer Science, Information Systems Science & Technology Computer Science Technology
Knowledge graph representation is an important embedding technology that supports a variety of machine learning related applications. By learning the distributed representation of multi-relational data, knowledge embedding models are supposed to efficiently deal with the semantic relatedness of their constituents. However, failing in the fundamental task of creating an appropriate form to represent knowledge harms any attempt of designing subsequent machine learning tasks. Several knowledge embedding methods have been proposed in the last decade. Although there is a consensus on the idea that enhanced approaches are more efficient, more complex projections in the hyperspace that indeed favor link prediction (or knowledge graph completion) can result in a loss of semantic similarity. We propose a new evaluation task that aims at performing risk assessment on domain-specific categorized multi-relational datasets, designed as a classification problem based on the resulting embeddings. We assess the quality of embedding representations based on the synergy of the resulting clusters of target subjects. We show that more sophisticated embedding approaches do not necessarily favor embedding quality, and the traditional link prediction validation protocol is a weak metric to measure the quality of embedding representation. Finally, we present insights about using the synergy analysis to provide risk assessment explainability based on the probability distribution of feature-value pairs within embedded clusters.

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Web of Science research areas
Computer Science, Information Systems
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