Fundamentals of Knowledge Graphs, Graph Databases, and Their Applications in AI and Big Data
This article introduces the basic concepts of knowledge graphs, explores their research dimensions across knowledge engineering, natural language processing, databases and machine learning, discusses graph database storage models and their integration with artificial intelligence and big data, and presents related projects and real‑world case studies.
The article begins by describing the evolution of search engines with Google’s 2012 Knowledge Graph launch, highlighting the shift from keyword matching to semantic graph‑based information retrieval.
It then defines a knowledge graph as a graph‑structured semantic network that represents entities and their relationships, and outlines four research dimensions: knowledge engineering (ontology construction, knowledge extraction, knowledge fusion), natural language processing (information extraction, semantic parsing), databases (RDF, property graphs, native graph stores), and machine learning (graph embedding, representation learning).
Various graph data models are examined, including RDF triples, RDF‑Schema, OWL, and property graphs such as Neo4j, with discussion of their advantages, limitations, and storage strategies (attribute tables, vertical partitioning, native graph stores).
The article connects knowledge graphs to artificial intelligence, noting that AI’s symbolic and connectionist approaches both benefit from graph‑based knowledge representation, and explains how knowledge graphs enable reasoning, question answering, and cognitive intelligence.
From a big‑data perspective, knowledge graphs are presented as a model for associative analysis, leveraging the 5V characteristics of big data (volume, velocity, variety, value, veracity) to uncover hidden relationships.
The authors describe their own work, including the gStore RDF graph database (sub‑graph matching, 50‑billion‑node scalability, C++ implementation), the gBuilder knowledge‑graph construction platform, and the gAnswer natural‑language QA system, along with ecosystem tools such as gStore Workbench, gCloud, and gMaster for distributed deployment.
Several application cases are showcased: financial technology (entity and risk analysis), government big‑data integration, smart disciplinary inspection, intelligent healthcare, AI‑driven chatbots, meteorology‑traffic alerts, and public security knowledge graphs.
Finally, the article concludes with acknowledgments and a call for readers to share, like, and follow the DataFunTalk platform.
DataFunTalk
Dedicated to sharing and discussing big data and AI technology applications, aiming to empower a million data scientists. Regularly hosts live tech talks and curates articles on big data, recommendation/search algorithms, advertising algorithms, NLP, intelligent risk control, autonomous driving, and machine learning/deep learning.
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