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Found 6 papers, 0 papers with code
Fast Record Linkage for Company Entities
These insights guide further analytic approaches to the data and support data visualization.
Siamese Graph Neural Networks for Data Integration
In this work, we propose a general approach to modeling and integrating entities from structured data, such as relational databases, as well as unstructured sources, such as free text from news articles.
Knowledge Graph Embedding using Graph Convolutional Networks with Relation-Aware Attention
Knowledge graph embedding methods learn embeddings of entities and relations in a low dimensional space which can be used for various downstream machine learning tasks such as link prediction and entity matching.
Business Entity Matching with Siamese Graph Convolutional Networks
Data integration has been studied extensively for decades and approached from different angles.
Learning MR-Sort Models from Non-Monotone Data
The Majority Rule Sorting (MR-Sort) method assigns alternatives evaluated on multiple criteria to one of the predefined ordered categories.
Attention-based Interpretability with Concept Transformers
In particular, we design the Concept Transformer, a deep learning module that exposes explanations of the output of a model in which it is embedded in terms of attention over user-defined high-level concepts.
