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Given multiple input signals, how can we infer node importance in a knowledge graph (KG)? ... In this paper, we develop an end-to-end model MultiImport, which infers latent node importance from multiple, potentially overlapping, input signals. ... We formulate the problem of inferring node importance in a KG from multiple input signals. • Algorithm. ...doi:10.1145/3394486.3403093 arXiv:2006.12001v1 fatcat:tcmlgpa57vdltjfda4bmt3b4ee
The challenge of jointly processing multiple types of data is addressed by designing a principled graph-based approach for justification generation. ... Existing post-hoc methods are often limited in providing diverse justifications, as they either use only one of many available types of input data, or rely on the predefined templates. ... GENI  and MultiImport  are semi-supervised techniques to estimate node importance by considering both the graph structure and real-world signals of node popularity. ...arXiv:2011.05928v1 fatcat:3dqm6aqazzbc5fcmwiapv7ewza
We first propose novel graph-regularized semi-supervised algorithms for estimating node importance in a knowledge graph, which achieve up to 25% higher accuracy than the best baseline.Then we develop distributed ... In addition, we develop a method that explains product recommendations, up to 21% more accurately than the best baseline, by performing personalized inference over a product graph. ... Chapter 3 Inferring Node Importance in a Knowledge Graph from Multiple Input Signals 2 Chapter based on work published in KDD 2020 [PKD + 20]. Definition 3. ...doi:10.1184/r1/19891765.v1 fatcat:w47xj5l3snehjkrdmp77yqphve