Knowledge Graph Extension by Entity Type Recognition
Daqian Shi

TL;DR
This paper presents a novel framework for automatically extending knowledge graphs by recognizing entity types using machine learning, aligning schemas, and validating quality, thereby improving knowledge extraction and management.
Contribution
The paper introduces a new entity type recognition method, assessment metrics, and a practical platform for knowledge graph extension, advancing the automation and quality of knowledge graph construction.
Findings
The proposed framework effectively improves knowledge graph extension quality.
Quantitative experiments demonstrate the framework's feasibility and effectiveness.
Case studies validate practical benefits in knowledge management.
Abstract
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…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Semantic Web and Ontologies · Data Quality and Management
MethodsSparse Evolutionary Training
