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.

本文提出了一种基于实体类型识别的知识图谱扩展框架，通过对不同知识图谱的模式和实体进行对齐，提高了扩展的性能，从而实现高质量的知识提取。通过定量实验和案例研究，全面展示了所提出的扩展框架及其功能的可行性和有效性。