knowledge graphs

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.

To address insufficient utilization of ontology information in knowledge graph (KG) navigation and exploration, this paper proposes an ontology-based multimodal interactive exploration framework. Methodologically, we design a context-aware, ontology-driven view mechanism that integrates the schema layer, instance layer (types and neighborhoods), and geospatial dimensions, implemented via a modular frontend architecture incorporating ontology parsing, dynamic neighborhood extraction, geospatial mapping, and scalable visualization. Our key contribution is the first deep integration of ontological semantics into a multi-granularity interactive pipeline, enabling consistent browsing across schema, instance, and spatial layers. User evaluation demonstrates that the framework significantly reduces navigational cognitive load (p < 0.01), improves target entity discovery efficiency by 37%, and supports real-time interaction over KGs containing up to ten million triples, with demonstrated cross-domain deployability.

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.

To address low automation, poor interpretability, and semantic incompatibility with Wikidata in knowledge graph (KG) construction, this paper proposes an ontology-driven large language model (LLM) approach. First, domain scope and relations are lightweightly extracted from Competency Questions (CQs). Second, extracted relations are bidirectionally mapped to the Wikidata ontology to achieve semantic alignment. Third, LLMs are guided—under ontology constraints—to generate structured subject-predicate-object triples. The key contribution is the first CQ-driven ontology construction paradigm, uniquely balancing automation and interpretability. Experiments on standard benchmarks demonstrate state-of-the-art performance; the resulting KG exhibits high logical consistency and cross-system interoperability, significantly reducing reliance on manual annotation. The method enables a scalable, reusable KG construction pipeline.

To address the challenges of semantic understanding and interactive analysis in open academic big data exploration, this paper pioneers the modeling of knowledge graph (KG) embedding spaces as interpretable semantic vector spaces—extending beyond their conventional use solely for link prediction. We propose a novel vector-algebraic paradigm for semantic querying—including analogical reasoning and similarity-based retrieval—that systematically uncovers structured semantic regularities among entities and relations in the embedding space. Our method integrates state-of-the-art KG embedding models (e.g., TransE, ComplEx) with word-embedding-style analogy analysis techniques to construct an explainable query framework tailored for scholarly KGs. Extensive evaluation on multiple public academic KGs demonstrates substantial improvements in deep relational pattern mining for entities such as papers and authors, and effectively supports cross-domain analogical inference and interactive exploratory tasks.

This work addresses the limitations of existing ontology documentation tools in supporting modular modeling and human readability, particularly in handling cross-module entities and annotations. To overcome these challenges, the authors refactor and extend the LODE framework by introducing a modular Reader-Model-Viewer architecture that decouples parsing, modeling, and rendering components. Implemented as a web service, the new framework provides enhanced capabilities for generating OWL ontology documentation, featuring dedicated entity pages, RDF provenance tracking, and Markdown-based rendering. These improvements significantly increase the intelligibility and reusability of modular scientific knowledge graph ontologies. The framework has been successfully applied to the documentation of the SKG-O ontology, demonstrating its practical utility and effectiveness.

This work addresses the need for unified and efficient knowledge provisioning in large language models by proposing a novel architecture that integrates relational and property graph data models. The approach leverages record addresses from log files as immutable reference values in place of traditional foreign keys, enabling efficient graph-style link traversal instead of costly join queries while natively supporting triple-based knowledge representation. The resulting unified knowledge service framework combines the structural rigor of relational models with the flexible associative capabilities of graph models, significantly enhancing knowledge retrieval efficiency and effectively supporting knowledge integration and invocation in generative AI systems.

Traditional knowledge graph construction methods struggle to balance structural consistency and contextual richness, as they are either constrained by the high cost of predefined ontologies or suffer from fragmentation due to schema-free extraction. This work proposes TRACE-KG, a novel framework that, for the first time, jointly generates a knowledge graph and a data-driven semantic schema without requiring any pre-specified ontology. The approach leverages multimodal joint modeling and structured qualifiers to represent conditional relationships, enabling end-to-end traceable knowledge extraction. Experimental results on complex technical documents demonstrate that the generated graphs significantly outperform existing ontology-driven and schema-free methods in terms of structural coherence, semantic richness, and traceability back to the source documents.

This work addresses the lack of a unified evaluation framework for knowledge graph integration pipelines, which hinders systematic comparison and selection of methods. To bridge this gap, the paper introduces KGI-Bench, the first comprehensive benchmark specifically designed for evaluating knowledge graph data integration. KGI-Bench assesses integration performance across three key dimensions—coverage, correctness, and consistency—when incorporating heterogeneous input data (structured, semi-structured, and unstructured) into a target knowledge graph. Using a curated dataset in the movie domain, the benchmark evaluates twelve representative integration pipelines, revealing significant performance variations attributable to input data types and architectural choices. The results demonstrate the effectiveness and practical utility of KGI-Bench in enabling rigorous, reproducible evaluation of knowledge graph integration approaches.