Papers by Dave Palfrey
Demand-Weighted Completeness Prediction for a Knowledge Base (N18-3)
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| Challenge: | Knowledge Bases (KBs) are widely used for representing information in a structured format. |
| Approach: | They propose a method to measure Demand-Weighted Completeness by defining an entity by its classes and using usage data to predict relation distributions. |
| Outcome: | The proposed method can be used to estimate completeness of knowledge bases based on how they are used and can quantify usage and completeness changes over time. |
Debiasing knowledge graph embeddings (2020.emnlp-main)
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| Challenge: | Existing methods to train knowledge graph embeddings to be neutral to sensitive attributes such as gender have been shown to increase training time by a factor of eight or more. |
| Approach: | They propose a method where all embeddings are trained to be neutral to sensitive attributes such as gender by default using an adversarial loss. |
| Outcome: | The proposed method reduces training time by eightfold and improves accuracy. |
Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets (D19-1)
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| Challenge: | Existing methods for knowledge graph completion require large batch sizes and memory constraints. |
| Approach: | They combine occurrences of entity-relation pairs to construct a joint learning model using a dataset containing 2 million entities and combine them to increase the quality of sampled negatives. |
| Outcome: | The proposed model outperforms the baseline model on a dataset containing 2 million entities by 2.8% absolute on hits@1. |