# Named Entity Resolution in Personal Knowledge Graphs

arXiv: https://arxiv.org/abs/2307.12173 · HTML: https://mlsanity.com/paper/2307.12173/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 127 references that are in the map.
- First posted on arXiv 22 Jul 2023.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Zero-Shot Learning -- The Good, the Bad and the Ugly](https://mlsanity.com/paper/1703.04394/) (2017)
- [Anaphora and Coreference Resolution: A Review](https://mlsanity.com/paper/1805.11824/) (2018)
- [Do Fine-tuned Commonsense Language Models Really Generalize?](https://mlsanity.com/paper/2011.09159/) (2020)
- [Supervised Typing of Big Graphs using Semantic Embeddings](https://mlsanity.com/paper/1703.07805/) (2017)
- [An Ensemble Blocking Scheme for Entity Resolution of Large and Sparse Datasets](https://mlsanity.com/paper/1609.06265/) (2016)
- [Always Lurking: Understanding and Mitigating Bias in Online Human Trafficking Detection](https://mlsanity.com/paper/1712.00846/) (2017)
- [Can Language Representation Models Think in Bets?](https://mlsanity.com/paper/2210.07519/) (2022)
- [Low-supervision urgency detection and transfer in short crisis messages](https://mlsanity.com/paper/1907.06745/) (2019)
- [On using Product-Specific Schema.org from Web Data Commons: An Empirical Set of Best Practices](https://mlsanity.com/paper/2007.13829/) (2020)
- [Can Scale-free Network Growth with Triad Formation Capture Simplicial Complex Distributions in Real Communication Networks?](https://mlsanity.com/paper/2203.06491/) (2022)
- [Understanding Prior Bias and Choice Paralysis in Transformer-based Language Representation Models through Four Experimental Probes](https://mlsanity.com/paper/2210.01258/) (2022)
- [Understanding Substructures in Commonsense Relations in ConceptNet](https://mlsanity.com/paper/2210.01263/) (2022)
- [A DNF Blocking Scheme Learner for Heterogeneous Datasets](https://mlsanity.com/paper/1501.01694/) (2015)
- [On the Complexity of Sorted Neighborhood](https://mlsanity.com/paper/1501.01696/) (2015)
- [Adaptive Candidate Generation for Scalable Edge-discovery Tasks on Data Graphs](https://mlsanity.com/paper/1605.00686/) (2016)
- [Predicting Zip Code-Level Vaccine Hesitancy in US Metropolitan Areas Using Machine Learning Models on Public Tweets](https://mlsanity.com/paper/2108.01699/) (2021)
- [Understanding COVID-19 Vaccine Reaction through Comparative Analysis on Twitter](https://mlsanity.com/paper/2111.05823/) (2021)
- [On the Empirical Association between Trade Network Complexity and Global Gross Domestic Product](https://mlsanity.com/paper/2211.13117/) (2022)

## Built on by (most cited first, 1 of 1)

- [Cost-Efficient Prompt Engineering for Unsupervised Entity Resolution](https://mlsanity.com/paper/2310.06174/) (2023)

## Abstract (arXiv)

Entity Resolution (ER) is the problem of determining when two entities refer to the same underlying entity. The problem has been studied for over 50 years, and most recently, has taken on new importance in an era of large, heterogeneous 'knowledge graphs' published on the Web and used widely in domains as wide ranging as social media, e-commerce and search. This chapter will discuss the specific problem of named ER in the context of personal knowledge graphs (PKGs). We begin with a formal definition of the problem, and the components necessary for doing high-quality and efficient ER. We also discuss some challenges that are expected to arise for Web-scale data. Next, we provide a brief literature review, with a special focus on how existing techniques can potentially apply to PKGs. We conclude the chapter by covering some applications, as well as promising directions for future research.