Large-Scale Knowledge Graph Construction for Domain-Specific AI
DOI:
https://doi.org/10.15662/IJRAI.2023.0601001Keywords:
Knowledge Graph, Domain-Specific AI, Information Extraction, Entity Resolution, Ontology Alignment, Graph Embedding, Semantic Representation, Large-Scale Data Integration, Natural Language Processing, Knowledge Graph ConstructionAbstract
Knowledge Graphs (KGs) have emerged as powerful tools for organizing and representing complex information in structured formats, enabling advanced reasoning and semantic understanding. In domain-specific Artificial Intelligence (AI), large-scale knowledge graphs play a crucial role by providing rich contextual information tailored to specialized fields such as healthcare, finance, and manufacturing. This paper explores the methodologies, challenges, and benefits of constructing large-scale domain-specific knowledge graphs to support AI applications. Constructing such knowledge graphs involves extracting entities, relationships, and attributes from heterogeneous data sources, including structured databases, unstructured texts, and semi-structured resources. The process typically employs natural language processing (NLP), information extraction, entity resolution, and ontology alignment techniques. Ensuring data quality, consistency, and scalability is essential given the vast and diverse datasets involved. The paper reviews various research efforts addressing key challenges such as schema design, entity disambiguation, and incremental updating. It also discusses the integration of domain ontologies to enhance semantic richness and reasoning capabilities. Moreover, it highlights the use of graph embedding and representation learning to improve knowledge graph completion and AI model performance. Advantages of large-scale domain-specific KGs include improved AI interpretability, enhanced decision-making, and the ability to uncover hidden insights through link prediction and reasoning. However, challenges remain in handling noisy data, evolving knowledge, and computational complexities. The paper concludes by presenting future research directions focused on automated KG construction, better integration with AI pipelines, and methods to maintain up-to-date and accurate knowledge bases. Overall, large-scale knowledge graphs represent a foundational component for advancing domain-specific AI, facilitating smarter, context-aware systems.
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