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Paper Citation Record · LEDGER

BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2206.14268.

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

pith.paper-citation-record.v1
2206.14268 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:20:45.491115Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T08:38:10.295490Z

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 7e66def0-e749-4a04-8b56-dc056abdb3fa · inbound

From Models to Microtheories: Distilling a Model's Topical Knowledge for Grounded Question Answering cites this paper.

From Models to Microtheories: Distilling a Model's Topical Knowledge for Grounded Question Answering BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-11T05:20:45.491115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:20:45.491115Z digest=sha256:dad03b42646ffbe49b802535c78237e4b584850f1bc72ffb8325fefb8c28526a

Observation 545ce805-a73a-4d12-8dbf-1be1b3df9443 · inbound

Construction of Knowledge Graph based on Language Model cites this paper.

Construction of Knowledge Graph based on Language Model BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models

Reference 7

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T12:46:23.374964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-10T02:58:06.245937Z digest=sha256:b923a180c89f5bbaa6748df57a578f399b9f275b5ff18135f009de0cbf90edcf

Observation 1165e640-9885-494f-a3a7-7d8a1f86371b · inbound

BOOKMARKS: Efficient Active Storyline Memory for Role-playing cites this paper.

BOOKMARKS: Efficient Active Storyline Memory for Role-playing BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-15T04:55:03.815343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-15T04:51:44.394368Z digest=sha256:5cdd359416a1a46469c5c75c5b63feb846638b509f46080894eff0035d4039b0

Observation e63177fd-86f7-4542-a3c0-f5fab45fba7c · inbound

Restructure This: Using AI to Restructure Onboarding Documents to Reduce Cognitive Overload cites this paper.

Restructure This: Using AI to Restructure Onboarding Documents to Reduce Cognitive Overload BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models

Reference 108

Resolution
verified exact
arxiv_id, observed 2026-05-20T08:38:10.298033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-20T08:37:41.455802Z digest=sha256:e2412bf3ad3aa48aeceeb7d012b5fc8113a24d0eed82695d07ad60be049b77ee