arXiv:2604. 03496v2 Announce Type: replace Abstract: Knowledge graph generation typically relies either on predefined ontologies or on schema-free extraction.
arXiv:2508. 02548v3 Announce Type: replace-cross Abstract: We propose KG-ER, a conceptual schema language for knowledge graphs that describes the structure of knowledge graphs independently of their representation (relational databases, property graphs, RDF) while helping to capture the semantics of the information stored in a knowledge graph.
arXiv:2506. 01232v2 Announce Type: replace-cross Abstract: Deriving OWL ontologies from relational database schemas supports semantic interoperability and downstream tasks such as knowledge graph population, ontology-based data access, graph-based learning, and automated reasoning.
arXiv:2608. 14228v1 Announce Type: new Abstract: Life science knowledge graphs make large collections of structured data available through SPARQL, but each resource uses its own schema, identifiers, and links.
arXiv:2607. 10212v1 Announce Type: new Abstract: Knowledge Graphs (KGs) are increasingly constructed through automated extraction pipelines; however, such systems often introduce spurious or incomplete triples, which degrade downstream performance.
arXiv:2507. 21438v2 Announce Type: replace Abstract: Ontologies and knowledge graphs require continuous evolution to remain comprehensive and accurate, but manual curation is labor intensive.
Knowledge Graphs (KGs) are increasingly constructed through automated extraction pipelines; however, such systems often introduce spurious or incomplete triples, which degrade downstream performance. Existing evaluation practices rely heavily on task-specific metrics or small-scale manual verification, offering limited insight into the structural and semantic fidelity of extracted graphs.
arXiv:2606. 05415v1 Announce Type: cross Abstract: Real-world data spans tables, documents, and semi-structured files with implicit semantics.
arXiv:2607. 21610v1 Announce Type: cross Abstract: Schema graphs are an upstream bottleneck of schema-grounded information extraction and knowledge graph construction, yet most extraction systems assume the schema is already available.
The paper investigates whether large language models (LLMs) can construct knowledge graphs (KGs) from documents in a zero‑shot, schema‑free manner. It introduces the Detail‑to‑Abstract Hierarchical Knowledge Graph (D2A‑HKG) framework, which splits KG construction into extraction, splitting, and abstraction stages, and evaluates the resulting graphs semantically and structurally. Using seven leading LLMs, the authors benchmark zero‑shot KG construction on CMW‑Lit—a dataset of pediatric mental‑wellness research—and find that state‑of‑the‑art LLMs generally produce relevant, document‑faithful triples with limited hallucination, though their extraction behaviors vary across stages. The study releases CMW‑Lit and the generated graphs as resources for future research and downstream knowledge‑intensive applications.
arXiv:2606. 13669v1 Announce Type: new Abstract: Current LLM-based research agents have advanced through agent orchestration, yet largely overlook scientific knowledge orchestration.
The paper introduces a neuro‑symbolic framework for constructing knowledge graphs (KGs) that are grounded in an ontology. It combines open‑domain extraction, embedding‑based canonicalization of types and predicates, and a post‑extraction LLM‑based correction step to fix ontology violations, thereby reducing token usage and improving KG consistency. The resulting KGs support symbolic querying, as evidenced by the prevalence of SPARQL graph patterns in the extracted data.
arXiv:2605. 22093v3 Announce Type: replace Abstract: Knowledge graphs have become the primary vehicle for data integration and are critical to the success of modern AI, but the diversity of KG modelling practices, from lightweight vocabularies to richly axiomatised ontologies, makes integration and reuse expensive and brittle.