An Intelligent Framework for Knowledge Graph Construction using Hierarchical Context-Aware Semantic Representations

Automated Tree Knowledge Graph Construction using Ontology Expansion and Retrieval from Vietnamese History Textbooks

Hierarchical Knowledge graph (KG)-based retrieval augmented generation (RAG) has emerged as a powerful approach for supporting large language models with structured knowledge. However, there are primary challenges: (i) the lack of methods for automatic KG construction using ontology expansion for low-resource languages such as Vietnamese, (ii) the absence of systematic evaluation for knowledge retrieval strategies leveraging the hierarchical structures. In this paper, we propose an end-to-end pipeline for KG construction and retrieval strategies evaluation. In the KG construction, we employ a three-phase hybrid relation extraction pipeline: intra-batch deduplication via Union-Find, approximate cross-batch search, and LLM extraction with a centroid filter that reduces prompts combined with a five-step dual-LLM validator to prevent bloated ontology. A two-tier architecture consists of unmergeable structural nodes to preserve the document structure and mergeable content nodes. The retrieval evaluation consists of three graph traversal strategies: Top-Down, Horizontal, and Bottom-Up, which are evaluated on a synthetically generated benchmark of 1,210 Vietnamese queries from 109 subgraphs, categorized by five query directions. In this paper, we construct the tree knowledge graph from Vietnamese high school History textbooks (nearly 400 pages) to produce 750 nodes and 4,341 semantic edges with controlled ontology growth from 40 to 41 types. Among experimental graph traversal strategies, the Top-Down strategy with structure surpasses the vector baseline by 4.7 percentage points in NDCG@10. As a result, tree-structural information provides valuable information beyond flat cosine similarity but degrades performance when the query does not require structural context.

An Ontology-Guided, Deduplication-Aware Extraction Layer for Knowledge Graph Construction from Heterogeneous Documents

A production extraction layer that converts a live document stream into a validated knowledge graph aligned to a formal ontology, and improved search recall from roughly 70 to 95 percent with no false merges, and corrected seven classes of silent quality defect.

Knowledge Graphs for Intelligent Data Integration Systems

The increasing presence of heterogeneous data sources in modern information systems has intensified the need for intelligent data integration processes capable of handling semantic complexity, structural diversity, and dynamic changes. Traditional data integration methods, primarily based on relational schemas and syntactic mappings, struggle to address semantic heterogeneity in large-scale distributed environments. Knowledge graphs have emerged as a powerful paradigm, enabling semantically rich, flexible, and scalable integration by representing data as interconnected entities with metadata, ontologies, and inference capabilities. Using technologies such as RDF and OWL, knowledge graphs support interoperability, contextual reasoning, and unified data views across systems. This paper examines knowledge graph-based intelligent data integration systems, focusing on their architecture, methodology, and practical applications. It highlights their advantages in schema alignment, entity resolution, and semantic enrichment over traditional ETL approaches. The integration of machine learning techniques further enhances automation in data mapping, anomaly detection, and knowledge discovery. A systematic framework is proposed, covering ontology design, data ingestion, graph construction, and query optimization. A conceptual case study demonstrates improved integration accuracy, scalability, and query performance. Evaluation results indicate enhanced data quality, interoperability, and reasoning capabilities, along with reduced integration latency. Overall, knowledge graphs serve as a key enabler for next-generation intelligent data integration, supporting complex relationships and data-driven decision-making. Future work includes improving scalability, real-time processing, and integration with deep learning models.

NORKE: A Framework for Normative Rule-Centric Knowledge Graph Construction using Language Models

AgentsKG: A Hierarchical Multi-Agent Framework for Open-Domain Knowledge Graph Construction

Knowledge Graph Construction (KGC) is essential for transforming unstructured text into structured knowledge representations. Despite advances in Large Language Models, existing methods treat KGC as a single-pass generation task, conflating extraction, normalization, and validation within a single forward pass. This leads to hallucinated facts, polysemous conflation, and fragmented triples, particularly in open-domain settings where predefined schemas are unavailable. In this work, we propose AgentsKG, a hierarchical multi-agent framework that decouples semantic perception from structural integration. In the Semantic Perception Layer, a multi-role Verification Committee filters hallucinated and invalid assertions through majority voting, while a Contextual Profiler resolves polysemous ambiguities by enriching mentions with context-dependent semantic descriptors. In the Structural Integration Layer, a Knowledge Linker merges redundant entities and relations based on semantic profiles, and an Ontological Logic Auditor enforces logical consistency across the graph. Extensive experiments demonstrate that AgentsKG outperforms state-of-the-art training-free baselines in both extraction accuracy and structural quality, offering a robust approach to open-domain knowledge graph construction without additional training. Source code is available at https://doi.org/10.5281/zenodo.20484211

A Synergistic Knowledge Graph and LLM-Driven Framework for Intelligent Process Decision-Making Systems

The proposed knowledge graph construction method for the workpiece machining distortion domain is proposed, together with an intelligent decision-making framework driven by the collaboration of knowledge graphs and large language models, providing a feasible pathway for the structured organization, intelligent retrieval, and decision support of workpiece machining distortion knowledge.