Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2012.08492.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:31:06.771127Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-15T18:10:21.776804Z
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 d516a851-b94a-41f3-94d0-c4f893581c49 · inbound
Mixture Policy based Multi-Hop Reasoning over N-tuple Temporal Knowledge Graphs cites this paper.
Mixture Policy based Multi-Hop Reasoning over N-tuple Temporal Knowledge Graphs Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:31:06.771127Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dfc91ea2-3b91-4401-a2dc-5458a694f1a7 · inbound
Towards Improving Long-Tail Entity Predictions in Temporal Knowledge Graphs through Global Similarity and Weighted Sampling cites this paper.
Towards Improving Long-Tail Entity Predictions in Temporal Knowledge Graphs through Global Similarity and Weighted Sampling Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks
Reference 40
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T18:10:21.784272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.