# Knowledge Graph Extension by Entity Type Recognition

arXiv: https://arxiv.org/abs/2405.02463 · HTML: https://mlsanity.com/paper/2405.02463/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 151 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 3 of 3)

- [EAGER: Embedding-Assisted Entity Resolution for Knowledge Graphs](https://mlsanity.com/paper/2101.06126/) (2021)
- [LiveSchema: A Gateway Towards Learning on Knowledge Graph Schemas](https://mlsanity.com/paper/2207.06112/) (2022)
- [Recognizing Entity Types via Properties](https://mlsanity.com/paper/2304.07910/) (2023)

## Abstract (arXiv)

Knowledge graphs have emerged as a sophisticated advancement and refinement of semantic networks, and their deployment is one of the critical methodologies in contemporary artificial intelligence. The construction of knowledge graphs is a multifaceted process involving various techniques, where researchers aim to extract the knowledge from existing resources for the construction since building from scratch entails significant labor and time costs. However, due to the pervasive issue of heterogeneity, the description diversity across different knowledge graphs can lead to mismatches between concepts, thereby impacting the efficacy of knowledge extraction. This Ph.D. study focuses on automatic knowledge graph extension, i.e., properly extending the reference knowledge graph by extracting and integrating concepts from one or more candidate knowledge graphs. We propose a novel knowledge graph extension framework based on entity type recognition. The framework aims to achieve high-quality knowledge extraction by aligning the schemas and entities across different knowledge graphs, thereby enhancing the performance of the extension. This paper elucidates three major contributions: (i) we propose an entity type recognition method exploiting machine learning and property-based similarities to enhance knowledge extraction; (ii) we introduce a set of assessment metrics to validate the quality of the extended knowledge graphs; (iii) we develop a platform for knowledge graph acquisition, management, and extension to benefit knowledge engineers practically. Our evaluation comprehensively demonstrated the feasibility and effectiveness of the proposed extension framework and its functionalities through quantitative experiments and case studies.