knowledge graphs

This paper addresses two critical gaps in the evolution of the Semantic Web: (1) the theoretical lag behind practical applications, and (2) insufficient integration of trustworthiness mechanisms with AI. To bridge these gaps, we propose a unified analytical framework that synergistically integrates classical semantic technologies with modern AI. Methodologically, we extend the canonical “layered cake” model into a novel three-dimensional paradigm encompassing trustworthy computing, industrial validation, and LLM–KG co-adaptation—systematically unifying RDF/OWL representation, rule-based reasoning, distributed SPARQL query processing, knowledge graph embedding, graph neural networks, and LLM–KG alignment techniques. Our contributions include: (1) a comprehensive technology landscape charting 50 years of Semantic Web development; (2) a clarified integration roadmap for knowledge graphs and AI—particularly large language models; and (3) theoretical foundations and practical guidelines for building next-generation semantic infrastructure that is trustworthy, interpretable, and adaptive.

To address the challenges of efficiently organizing, storing, and querying evolving data in dynamic knowledge graphs, this paper proposes a condensation-based representation model for RDF graphs, formally defining a compact cross-temporal structure. Methodologically, it integrates graph compression with delta encoding to construct a temporal graph storage framework supporting multi-source, heterogeneous version management. Its key contributions are: (i) the first application of condensation-based representation to knowledge graph versioning, enabling explicit modeling of inter-version relationships while preserving semantic integrity; and (ii) substantial improvements in storage efficiency—achieving 60%–85% compression—and query performance—reducing cross-version traceability and incremental query latency by an average factor of 3.2. This work establishes both a novel theoretical foundation and a practical technical pathway for scalable version management of dynamic knowledge graphs.

Existing graph databases lack effective support for the tree-shaped substructures commonly found in property graphs. This work addresses this limitation by treating such tree substructures as first-class citizens and proposes a systematic management framework encompassing modeling, indexing, and query optimization. Drawing inspiration from XML structural indexing techniques, the approach enables efficient path queries within a relational graph database backend. Experimental evaluation demonstrates that the proposed method significantly improves path query performance, thereby validating the potential of structural indexing to enhance graph data management.

Entity disambiguation and linking in IT domains suffer from poor domain adaptability and difficulty in incorporating domain-specific knowledge when relying solely on general-purpose knowledge graphs (e.g., Wikidata, DBpedia). Method: This paper proposes a lightweight, extensible ontology construction method tailored for the IT domain. Starting from general Linked Open Data (LOD) resources, it employs a domain-agnostic pipeline and—novelly—integrates an IT-specific terminology lexicon to drive ontology schema expansion. The approach synergistically combines SPARQL querying, RDF reasoning, and ontology alignment. Contribution/Results: The resulting paradigm balances generality and domain specificity, significantly improving accuracy in IT entity disambiguation and linking. It establishes a low-barrier, reusable ontology engineering framework that supports continuous injection of proprietary domain knowledge, thereby enabling sustainable, scalable domain ontology development.

RDF/OWL exhibits insufficient semantic flexibility and weak cognitive interoperability for scientific knowledge representation, hindering compliance with FAIR principles. Method: This paper proposes the Semantic Unit framework, structuring knowledge graphs into semantically explicit, identifiable subgraphs. It introduces novel resource types (e.g., *some*, *most*, *every*, *all-instances*), supports assertions, conditionals, negation, cardinality constraints, and executable question-answering. It defines fine-grained subclasses—Statement Unit, Composite Unit, and Question-Answering Unit—eliminating blank node dependencies entirely. Modeling extends RDF/OWL, incorporates semantic subgraph partitioning, blank-node-free formalization, and graph-query-driven QA encoding. Contribution/Results: The framework systematically resolves 11 categories of FAIR implementation barriers. It achieves, for the first time, unified semantic modeling of assertions, prototypes, universality, negation, directives, and logical arguments, while enabling native graph-query execution.

This work addresses the limitations of traditional knowledge graph construction approaches, wherein structural decisions are hard-coded into rigid pipelines, resulting in tight coupling between schema and construction process and hindering support for ontology-level tasks. To overcome this, the authors propose an ontology-oriented construction framework featuring a novel intrinsic-relational routing mechanism. This mechanism dynamically assigns attributes to corresponding schema modules through iterative attribute classification, enabling a declarative and reusable decoupled design. The pipeline integrates rule-based cleaning, tool-augmented large language model–assisted annotation, and human review. Evaluated on Wikidata (January 2026), the resulting graph comprises 34 million nodes and 61.2 million edges, achieving 93.3% schema coverage and 98.0% module assignment accuracy, effectively supporting five ontology-level applications.

This work presents the first systematic approach to instance-free schema inference under property graph query transformations. Given a ProGS input schema and a G-CORE query, the authors propose a multi-layer mapping technique that translates property graphs, schemas, and queries into RDF, SHACL, and SPARQL CONSTRUCT representations, respectively, enabling automatic derivation of structural constraints on the output graph via description logic reasoning. By leveraging RDF reification and cross-language semantic bridging, the method establishes a sound and semantically equivalent metatheoretical foundation. This enables generic output schema inference applicable to any input graph conforming to the given schema, while formally verifying both the correctness of the derived constraints and the semantic fidelity of the mappings.

To address the challenge non-expert users face in directly querying knowledge graphs, this paper proposes a natural language-driven interactive query construction method. The approach employs a two-stage constrained language model that integrates ontology-based semantic constraints to generate syntactically and semantically valid query prototypes—thereby avoiding invalid classes, relations, and grammatical errors. A visual editor enables users to iteratively refine queries via natural language descriptions and graphical adjustments. Finally, an interpretable SPARQL translation pipeline converts the refined prototype into standard SPARQL. Evaluated across multiple ontologies and language models, the system consistently produces correct SPARQL queries without manual intervention, outperforming existing baselines in both retrieval accuracy and efficiency. Validation through synthetic data experiments and an initial user study confirms the method’s effectiveness, usability, and practical applicability.

This study addresses the challenge in attributed graph schema design of whether repeatedly occurring descriptive attributes should be embedded within nodes or externalized as reusable metadata. Building upon Fifth Normal Form (5NF), the authors propose a principled decision framework that systematically identifies metadata candidates based on semantic criteria rather than mere repetition frequency. The approach classifies attributes into characteristic nodes, embedded properties, or borderline cases using five key principles: cross-element occurrence frequency, conceptual independence, lossless externalizability, reuse potential, and governance relevance. Empirical validation through a library domain case study and an entity classification task demonstrates that repetition alone is insufficient for externalization decisions—semantic judgment is essential. The proposed method significantly enhances the accuracy, consistency, and reusability of metadata modeling in graph-based systems.

This work addresses the limited expressiveness of existing graph query languages—such as GQL and SQL/PGQ—which lack full compositionality and cannot capture complex path queries within the NLOGSPACE complexity class. To overcome this limitation, the paper introduces a novel query language that unifies graph pattern matching with relational querying through two key innovations: regular path queries enriched with variables and data-value comparisons, and a #Datalog-based graph transformation mechanism capable of constructing nodes, edges, and paths. This combination enables, for the first time, a systematically compositional approach to graph querying that precisely captures the full expressive power of NLOGSPACE. The proposed language not only resolves fundamental expressiveness gaps in current standards but also offers a practical and theoretically grounded extension pathway for both GQL and SQL/PGQ.