# Leveraging Knowledge Graphs for Orphan Entity Allocation in Resume Processing

arXiv: https://arxiv.org/abs/2310.14093 · HTML: https://mlsanity.com/paper/2310.14093/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 21 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [K-BERT: Enabling Language Representation with Knowledge Graph](https://mlsanity.com/paper/1909.07606/) (2019)
- [A General Framework for Information Extraction using Dynamic Span Graphs](https://mlsanity.com/paper/1904.03296/) (2019)
- [Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward](https://mlsanity.com/paper/2005.01159/) (2020)
- [Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.06804/) (2021)

## Abstract (arXiv)

Significant challenges are posed in talent acquisition and recruitment by processing and analyzing unstructured data, particularly resumes. This research presents a novel approach for orphan entity allocation in resume processing using knowledge graphs. Techniques of association mining, concept extraction, external knowledge linking, named entity recognition, and knowledge graph construction are integrated into our pipeline. By leveraging these techniques, the aim is to automate and enhance the efficiency of the job screening process by successfully bucketing orphan entities within resumes. This allows for more effective matching between candidates and job positions, streamlining the resume screening process, and enhancing the accuracy of candidate-job matching. The approach's exceptional effectiveness and resilience are highlighted through extensive experimentation and evaluation, ensuring that alternative measures can be relied upon for seamless processing and orphan entity allocation in case of any component failure. The capabilities of knowledge graphs in generating valuable insights through intelligent information extraction and representation, specifically in the domain of categorizing orphan entities, are highlighted by the results of our research.