# Knowledge-based Refinement of Scientific Publication Knowledge Graphs

arXiv: https://arxiv.org/abs/2309.05681 · HTML: https://mlsanity.com/paper/2309.05681/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 51 references that are in the map.
- First posted on arXiv 10 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [S2AND: A Benchmark and Evaluation System for Author Name Disambiguation](https://mlsanity.com/paper/2103.07534/) (2021)
- [Ethnicity sensitive author disambiguation using semi-supervised learning](https://mlsanity.com/paper/1508.07744/) (2015)
- [Author Name Disambiguation by Using Deep Neural Network](https://mlsanity.com/paper/1502.08030/) (2015)
- [Explainable Models via Compression of Tree Ensembles](https://mlsanity.com/paper/2206.07904/) (2022)

## Abstract (arXiv)

We consider the problem of identifying authorship by posing it as a knowledge graph construction and refinement. To this effect, we model this problem as learning a probabilistic logic model in the presence of human guidance (knowledge-based learning). Specifically, we learn relational regression trees using functional gradient boosting that outputs explainable rules. To incorporate human knowledge, advice in the form of first-order clauses is injected to refine the trees. We demonstrate the usefulness of human knowledge both quantitatively and qualitatively in seven authorship domains.