# InfoSeek fact coverage 이 문서는 InfoSeek 질문에서 필요한 positive fact를 어디서 복원할 수 있는지 정리한다. 현재 기준: ```text 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: ```text dataset_explanation/infoseek/fact_coverage_structural.json dataset_explanation/infoseek/infoseek_stats_full.json ``` Generated fact-store artifacts: ```text 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: ```text 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 ```text Does the gold entity QID exist in our entity universe? ``` For train/val, all explicit InfoSeek entities are in KnowCoL: ```text train unique entities: 5,549 / 5,549 in KnowCoL val unique entities: 1,794 / 1,794 in KnowCoL ``` ### Query-specific positive fact coverage ```text Does the KG contain the exact fact needed to answer this question? ``` Example: ```text 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: ```text (subject_qid, property_pid, object_qid) ``` So it is naturally useful for entity-valued facts: ```text (Q214924, P17, Q39) Rhine Falls --country--> Switzerland ``` But it does not directly store literal quantity/time values such as: ```text height = 23 metre date of birth = 1901-01-01 average flow = 600 m^3/s ``` For those, we need full Wikidata claims: ```text wikibase-item claims quantity claims time claims ``` Therefore: ```text 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: ```text infoseek_train_withkb.jsonl ``` ```text 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: ```text list[str] / string-or-time style: 743,065 numerical dict: 190,983 ``` Interpretation: ```text 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: ```text 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: ```text 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: ```text infoseek_val_withkb.jsonl ``` Question type source: ```text infoseek_val_qtype.jsonl ``` Overall: ```text 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: ```text String: 54,319 Numerical: 15,896 Time: 3,405 ``` ### val_unseen_entity ```text 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 ```text rows: 18,656 unique entities: 602 entities with local outgoing KnowCoL facts: 602 question type: String: 13,455 Numerical: 4,387 Time: 814 ``` Interpretation: ```text 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: ```text 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: ```text 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: ```text 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. ```text numeric-like = quantity claims + time claims ``` Reason: ```text 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: ```text 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: ```text 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: ```text Numerical: match Wikidata quantity amount to InfoSeek answer_eval range Time: match Wikidata time year to answer_eval year variants ``` Negative facts: ```text 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: ```text 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. ```text 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. ```text 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: ```text Numerical: rows: 15,896 matched: 15,526 coverage: 97.67% Time: rows: 3,405 matched: 3,386 coverage: 99.44% ``` Interpretation: ```text 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: ```text 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: ```text 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: ```text 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: ```text 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: ```text input: gold entity QID S question q answer / answer_eval A ``` Candidate facts: ```text local KnowCoL facts: all triples with subject S full Wikidata claims: all wikibase-item / quantity / time claims for S ``` Positive fact matching: ```text 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: ```text Use property label/description and question text to rank or filter candidates. ``` Example: ```text 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: ```text 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: ```text the matched claim/triple answering the sample ``` Negatives: ```text 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: ```text 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: ```text 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: ```text InfoSeek train/val only gold QID from *_withkb.jsonl qtype from infoseek_val_qtype.jsonl for val ``` Then build: ```text 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.