InfoSeek fact coverage
이 문서는 InfoSeek 질문에서 필요한 positive fact를 어디서 복원할 수 있는지 정리한다.
현재 기준:
train/val:
gold QID exists via *_withkb.jsonl
fact coverage analysis is reliable
test/human:
no explicit *_withkb file locally
latest OVEN test/human files also have no entity_id
fact coverage analysis is not reliable yet
Raw structural stats:
dataset_explanation/infoseek/fact_coverage_structural.json
dataset_explanation/infoseek/infoseek_stats_full.json
Generated fact-store artifacts:
codebook-contrastive/build_fact_store/output_facts/infoseek_facts.jsonl
codebook-contrastive/build_fact_store/output_facts/qid_to_fact_ids.json
codebook-contrastive/build_fact_store/output_facts/pid_to_fact_ids.json
codebook-contrastive/build_fact_store/output_facts/qid_label_alias_cache.json
codebook-contrastive/build_fact_store/output_facts/infoseek_train_question_fact_map.jsonl
codebook-contrastive/build_fact_store/output_facts/infoseek_val_question_fact_map.jsonl
codebook-contrastive/build_fact_store/output_facts/infoseek_question_fact_map_summary.json
codebook-contrastive/build_fact_store/output_facts/fact_coverage_by_qtype.json
codebook-contrastive/build_fact_store/output_facts/infoseek_numeric_facts.jsonl
codebook-contrastive/build_fact_store/output_facts/infoseek_train_numeric_question_fact_map.jsonl
codebook-contrastive/build_fact_store/output_facts/infoseek_val_numeric_question_fact_map.jsonl
codebook-contrastive/build_fact_store/output_facts/infoseek_numeric_fact_summary.json
codebook-contrastive/build_fact_store/output_facts/wikidata_claim_cache/
Current label cache status:
qid_label_alias_cache entries: 16,786
entries with label: 16,605
fact store unique object QIDs: 18,584
object QIDs with label: 16,743
object QIDs without label: 1,841
fact rows missing object label: 28,785 / 1,141,235
1. What counts as fact coverage?
There are two different levels.
Entity-level KG coverage
Does the gold entity QID exist in our entity universe?
For train/val, all explicit InfoSeek entities are in KnowCoL:
train unique entities: 5,549 / 5,549 in KnowCoL
val unique entities: 1,794 / 1,794 in KnowCoL
Query-specific positive fact coverage
Does the KG contain the exact fact needed to answer this question?
Example:
question: What country does this river belong to?
gold entity: Q214924 / Rhine Falls
answer: Switzerland
positive fact:
(Q214924, P17, Q39)
Rhine Falls --country--> Switzerland
This is the useful coverage for FactHead training.
2. KnowCoL local KG limitation
KnowCoL local KG stores triples like:
(subject_qid, property_pid, object_qid)
So it is naturally useful for entity-valued facts:
(Q214924, P17, Q39)
Rhine Falls --country--> Switzerland
But it does not directly store literal quantity/time values such as:
height = 23 metre
date of birth = 1901-01-01
average flow = 600 m^3/s
For those, we need full Wikidata claims:
wikibase-item claims
quantity claims
time claims
Therefore:
STRING:
often recoverable from KnowCoL local KG if object label/alias matches answer_eval
NUMERICAL:
usually requires full Wikidata quantity claims
TIME:
usually requires full Wikidata time claims
3. Train structural coverage
Gold entity source:
infoseek_train_withkb.jsonl
rows: 934,048
resolved rows with entity: 934,048
unique entities: 5,549
entities in KnowCoL entity.txt: 5,549
entities not in KnowCoL entity.txt: 0
entities with local outgoing KnowCoL facts: 5,544
entities without local outgoing KnowCoL facts: 5
rows whose entity has no local head entry: 948
Answer type:
list[str] / string-or-time style: 743,065
numerical dict: 190,983
Interpretation:
Most train samples have a local entity-object fact pool.
But 190,983 numerical rows need full Wikidata quantity matching for real positive fact recovery.
Current local positive matching result:
rows: 934,048
rows with matched local positive fact: 240,946
positive rate: 25.80%
positive_status:
matched_local_tail_surface: 240,946
no_match_local_tail_surface: 692,154
no_local_head_entry: 948
entity-level:
unique entities: 5,549
entities with at least one local positive: 1,921
entity-major status:
matched_local_tail_surface: 1,394
no_match_local_tail_surface: 4,150
no_local_head_entry: 5
average negatives per row:
same_entity_different_fact: 15.62
same_property_different_subject_object: 15.90
This is still conservative. A local positive is counted only when an object QID label/alias exactly matches answer_eval.
Type-level breakdown:
train total:
rows: 934,048
matched: 240,946
rate: 25.80%
Numerical:
rows: 190,983
matched: 0
rate: 0.00%
reason: local KnowCoL KG does not encode quantity literals
StringOrTime:
rows: 743,065
matched: 240,946
rate: 32.43%
note: train qtype file is not used here, so String and Time are not separated
4. Validation structural coverage
Gold entity source:
infoseek_val_withkb.jsonl
Question type source:
infoseek_val_qtype.jsonl
Overall:
rows: 73,620
resolved rows with entity: 73,620
unique entities: 1,794
entities in KnowCoL entity.txt: 1,794
entities not in KnowCoL entity.txt: 0
entities with local outgoing KnowCoL facts: 1,794
entities without local outgoing KnowCoL facts: 0
rows whose entity has no local head entry: 0
Question type:
String: 54,319
Numerical: 15,896
Time: 3,405
val_unseen_entity
rows: 54,964
unique entities: 1,192
entities with local outgoing KnowCoL facts: 1,192
question type:
String: 40,864
Numerical: 11,509
Time: 2,591
val_unseen_question
rows: 18,656
unique entities: 602
entities with local outgoing KnowCoL facts: 602
question type:
String: 13,455
Numerical: 4,387
Time: 814
Interpretation:
Val is fully usable for fact coverage evaluation because gold QID exists.
Local KnowCoL gives a fact pool for every val entity.
However, query-specific positive matching still requires answer-to-claim matching.
Current local positive matching result:
rows: 73,620
rows with matched local positive fact: 14,445
positive rate: 19.62%
positive_status:
matched_local_tail_surface: 14,445
no_match_local_tail_surface: 59,175
entity-level:
unique entities: 1,794
entities with at least one local positive: 813
entity-major status:
matched_local_tail_surface: 371
no_match_local_tail_surface: 1,423
average negatives per row:
same_entity_different_fact: 15.78
same_property_different_subject_object: 15.95
The previous no_object_label_cache issue is mostly resolved for InfoSeek train/val tails. Remaining unmatched rows are now mostly true no_match_local_tail_surface: the local entity-object KG fact pool exists, but no object label/alias exactly matches the answer.
Type-level breakdown:
val total:
rows: 73,620
matched: 14,445
rate: 19.62%
String:
rows: 54,319
matched: 14,445
rate: 26.59%
Numerical:
rows: 15,896
matched: 0
rate: 0.00%
Time:
rows: 3,405
matched: 0
rate: 0.00%
Split-level type breakdown:
val_unseen_entity:
String: 10,864 / 40,864 = 26.59%
Numerical: 0 / 11,509 = 0.00%
Time: 0 / 2,591 = 0.00%
val_unseen_question:
String: 3,581 / 13,455 = 26.61%
Numerical: 0 / 4,387 = 0.00%
Time: 0 / 814 = 0.00%
5. Full Wikidata numeric-like claim coverage
For the next stage, Numerical and Time are merged into one numeric-like group.
numeric-like = quantity claims + time claims
Reason:
The retriever does not need to treat time and quantity as fundamentally different classes.
Both are structured value facts attached to a subject entity and property.
Generated artifacts:
infoseek_numeric_facts.jsonl
infoseek_train_numeric_question_fact_map.jsonl
infoseek_val_numeric_question_fact_map.jsonl
infoseek_numeric_fact_summary.json
wikidata_claim_cache/{QID}.json
Numeric-like fact schema:
quantity fact:
subject_qid
property_id
amount
unit / unit_qid
lower_bound / upper_bound if available
time fact:
subject_qid
property_id
time
year
precision
Positive matching:
Numerical:
match Wikidata quantity amount to InfoSeek answer_eval range
Time:
match Wikidata time year to answer_eval year variants
Negative facts:
same_entity_different_numeric_time_fact:
another quantity/time claim from the same gold entity
same_property_different_subject_value:
same property, different subject/value
Combined train+val Wikidata claim store:
unique InfoSeek train/val entities: 6,741
numeric-like facts: 7,939
quantity: 6,296
time: 1,643
Train numeric-like coverage
Train currently uses only explicit Numerical rows because no train qtype file is available locally.
Numerical rows: 190,983
matched Wikidata numeric-like positive: 158,870
coverage: 83.19%
status:
matched_wikidata_numeric_like_claim: 158,870
no_wikidata_numeric_time_claims: 27,796
no_match_wikidata_numeric_like_claim: 4,317
Validation numeric-like coverage
Validation uses infoseek_val_qtype.jsonl, so Numerical and Time are both included.
Numeric-like rows: 19,301
matched Wikidata numeric-like positive: 18,912
coverage: 97.98%
status:
matched_wikidata_numeric_like_claim: 18,912
no_match_wikidata_numeric_like_claim: 369
no_wikidata_numeric_time_claims: 20
Breakdown:
Numerical:
rows: 15,896
matched: 15,526
coverage: 97.67%
Time:
rows: 3,405
matched: 3,386
coverage: 99.44%
Interpretation:
Local KnowCoL KG cannot cover Numerical/Time because it mostly stores QID-object triples.
Full Wikidata claims recover most numeric-like positives.
Remaining misses are mostly:
1. the entity has no quantity/time claim of the required kind
2. the answer value is present in Wikipedia text but not structured Wikidata
3. the matching rule is too strict for ranges, units, or temporal precision
6. Test and human status
Current local files do not support reliable fact coverage for test/human.
Reason:
InfoSeek test/human:
no local test_withkb / human_withkb file
latest OVEN test/human:
no entity_id field
Weak image-id overlap with labeled OVEN train/val resolves only a tiny subset:
test:
rows: 347,980
weakly resolved rows: 5,679
missing entity rows: 342,301
human:
rows: 8,931
weakly resolved rows: 116
missing entity rows: 8,815
Therefore:
Do not report test/human fact coverage from current local files.
Use test/human for QA-only evaluation unless official QID mappings are restored.
7. Positive fact recovery logic
For each InfoSeek train/val sample:
input:
gold entity QID S
question q
answer / answer_eval A
Candidate facts:
local KnowCoL facts:
all triples with subject S
full Wikidata claims:
all wikibase-item / quantity / time claims for S
Positive fact matching:
STRING:
match answer_eval against object label / alias / normalized surface
NUMERICAL:
match answer_eval numeric range against quantity claim amount
TIME:
match answer_eval string/date/year against time claim value
Property-question filtering:
Use property label/description and question text to rank or filter candidates.
Example:
question:
What is the height of this river in metre?
gold entity:
Q214924 / Rhine Falls
answer_eval:
{"wikidata": 23.0, "range": [20.7, 25.3]}
positive fact source:
full Wikidata quantity claim, not KnowCoL local entity-object KG
8. FactHead training data
For reliable MVP construction, use train/val with gold QID.
Fact item input:
subject:
QID
title / label
short text metadata if available
property:
PID
property description from KnowCoL relation file when available
Wikidata property label/description when using full claims
object/value:
if wikibase-item:
object QID
object label / aliases
if quantity:
amount
unit QID if available
normalized numeric value
if time:
timestamp
precision
Positive:
the matched claim/triple answering the sample
Negatives:
same entity, different fact
same property, different subject/object or different value
same datatype, different value
same-code-prefix hard fact
9. What to report in experiments
Report fact recovery separately from final QA.
Recommended metrics:
Fact Recall@K:
whether a matched positive fact appears in top-K retrieved facts
Positive construction coverage:
how many samples have at least one high-confidence positive fact
Coverage by question type:
String
Numerical
Time
Coverage by source:
KnowCoL local KG
full Wikidata claims
not matched / text evidence needed
Important:
KnowCoL local KG coverage is not the same as positive fact coverage.
The real metric is whether the query-specific answer fact can be matched.
10. Immediate next implementation target
Do this first:
InfoSeek train/val only
gold QID from *_withkb.jsonl
qtype from infoseek_val_qtype.jsonl for val
Then build:
1. object QID -> label/alias cache
2. property PID -> label/description cache
3. full Wikidata claim cache for unique train/val entities
4. positive_fact_candidates.jsonl
5. fact coverage report by qtype and source
Do not spend time on test/human fact coverage until QID mappings are restored.