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Paper Citation Record · LEDGER

Ontology Generation using Large Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2503.05388.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2503.05388 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:44:57.962579Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T14:06:02.942038Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f3a20721-6683-4d1e-9704-b3fd405a2727 · inbound

Streamlining Knowledge Graph Creation with PyRML cites this paper.

Streamlining Knowledge Graph Creation with PyRML Ontology Generation using Large Language Models

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T13:44:57.962579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:44:57.962579Z digest=sha256:1a44792d085b4509200d3c8a93dadc353cfe6cdaa648cd1bd21c5e4043e2f8a7

Observation 19c2b88d-fe6a-4f6e-a200-58a142d485e1 · inbound

Retrieval-Augmented Generation of Ontologies from Relational Databases cites this paper.

Retrieval-Augmented Generation of Ontologies from Relational Databases Ontology Generation using Large Language Models

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:26.100034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:26.100034Z digest=sha256:f90619ac07b71b43e50a79206e8346fcd95540e7d123d900f47dc5ddd479f39f

Observation 22d43290-70a1-4fd9-b601-e2bb6e82ab14 · inbound

Leveraging Large Language Models for Generating Research Topic Ontologies: A Multi-Disciplinary Study cites this paper.

Leveraging Large Language Models for Generating Research Topic Ontologies: A Multi-Disciplinary Study Ontology Generation using Large Language Models

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-05T14:57:56.503716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:57:56.503716Z digest=sha256:d92c614d511f3ffd585e886f127e38d884c60b8701de42dbbc98d0acba902a61

Observation 3e2d972a-50ab-4813-a75c-bd57d07051b6 · inbound

Automatic Ontology Construction Using LLMs as an External Layer of Memory, Verification, and Planning for Hybrid Intelligent Systems cites this paper.

Automatic Ontology Construction Using LLMs as an External Layer of Memory, Verification, and Planning for Hybrid Intelligent Systems Ontology Generation using Large Language Models

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-11T14:06:02.943797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-09T23:38:04.276214Z digest=sha256:2f0a18010c474f8dbf0611bf4d497cd3b2f1f26fd77345708c9cc9714a517a15

Observation da2d958a-a8d0-477a-a342-d34a5a561c03 · inbound

Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field cites this paper.

Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field Ontology Generation using Large Language Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-01T16:41:43.480960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:41:43.480960Z digest=sha256:c6d57a0c318c4460f80220c8d53ee24bbef2266164a05bab82239c779e73f7e2