{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:53LYGPKLQJ223HRZUONMSR5LAF","short_pith_number":"pith:53LYGPKL","schema_version":"1.0","canonical_sha256":"eed7833d4b8275ad9e39a39ac947ab01781c984036ba47b9e117efbbf8cd87d4","source":{"kind":"arxiv","id":"2605.22734","version":1},"attestation_state":"computed","paper":{"title":"ChronoMedKG: A Temporally-Grounded Biomedical Knowledge Graph and Benchmark for Clinical Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Farzaneh Firoozbakht, Jan Baumbach, Lukas Galke Poech, Md Shamim Ahmed, Richard R\\\"ottger","submitted_at":"2026-05-21T17:04:28Z","abstract_excerpt":"Biomedical knowledge graphs (KGs) treat disease associations as static facts, but temporal information is crucial for clinical reasoning, e.g., a symptom diagnostic of one disease at age 3 may imply a different disease at age 13. Existing KGs such as PrimeKG, Hetionet, and iKraph do not encode when a finding becomes clinically relevant over the course of a disease. This limits their usefulness for longitudinal clinical reasoning and retrieval augmentation.\n  We introduce ChronoMedKG, a temporal biomedical knowledge graph that contains 460,497 evidence-linked triples (filtered from 13M raw extr"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.22734","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-21T17:04:28Z","cross_cats_sorted":[],"title_canon_sha256":"bf9e29d8cdda081ec3a78c793a306046bb879a602ae332d057a6a60071aff3ed","abstract_canon_sha256":"a128bb2080de2a46ec7c5cda49adbd1b2ce6c0501cd8557ab2774f7932353be6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T02:04:52.442664Z","signature_b64":"w/E8+IFB6NcdVzk7GQbAgG5tejpp1n5HeoYM7GPYL0Yx/NpFIhLwZe5tqfpGBG/7sZph8GLTRNesKu+XVfd7AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eed7833d4b8275ad9e39a39ac947ab01781c984036ba47b9e117efbbf8cd87d4","last_reissued_at":"2026-05-22T02:04:52.441949Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T02:04:52.441949Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ChronoMedKG: A Temporally-Grounded Biomedical Knowledge Graph and Benchmark for Clinical Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Farzaneh Firoozbakht, Jan Baumbach, Lukas Galke Poech, Md Shamim Ahmed, Richard R\\\"ottger","submitted_at":"2026-05-21T17:04:28Z","abstract_excerpt":"Biomedical knowledge graphs (KGs) treat disease associations as static facts, but temporal information is crucial for clinical reasoning, e.g., a symptom diagnostic of one disease at age 3 may imply a different disease at age 13. Existing KGs such as PrimeKG, Hetionet, and iKraph do not encode when a finding becomes clinically relevant over the course of a disease. This limits their usefulness for longitudinal clinical reasoning and retrieval augmentation.\n  We introduce ChronoMedKG, a temporal biomedical knowledge graph that contains 460,497 evidence-linked triples (filtered from 13M raw extr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.22734","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.22734/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.22734","created_at":"2026-05-22T02:04:52.442058+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.22734v1","created_at":"2026-05-22T02:04:52.442058+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.22734","created_at":"2026-05-22T02:04:52.442058+00:00"},{"alias_kind":"pith_short_12","alias_value":"53LYGPKLQJ22","created_at":"2026-05-22T02:04:52.442058+00:00"},{"alias_kind":"pith_short_16","alias_value":"53LYGPKLQJ223HRZ","created_at":"2026-05-22T02:04:52.442058+00:00"},{"alias_kind":"pith_short_8","alias_value":"53LYGPKL","created_at":"2026-05-22T02:04:52.442058+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/53LYGPKLQJ223HRZUONMSR5LAF","json":"https://pith.science/pith/53LYGPKLQJ223HRZUONMSR5LAF.json","graph_json":"https://pith.science/api/pith-number/53LYGPKLQJ223HRZUONMSR5LAF/graph.json","events_json":"https://pith.science/api/pith-number/53LYGPKLQJ223HRZUONMSR5LAF/events.json","paper":"https://pith.science/paper/53LYGPKL"},"agent_actions":{"view_html":"https://pith.science/pith/53LYGPKLQJ223HRZUONMSR5LAF","download_json":"https://pith.science/pith/53LYGPKLQJ223HRZUONMSR5LAF.json","view_paper":"https://pith.science/paper/53LYGPKL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.22734&json=true","fetch_graph":"https://pith.science/api/pith-number/53LYGPKLQJ223HRZUONMSR5LAF/graph.json","fetch_events":"https://pith.science/api/pith-number/53LYGPKLQJ223HRZUONMSR5LAF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/53LYGPKLQJ223HRZUONMSR5LAF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/53LYGPKLQJ223HRZUONMSR5LAF/action/storage_attestation","attest_author":"https://pith.science/pith/53LYGPKLQJ223HRZUONMSR5LAF/action/author_attestation","sign_citation":"https://pith.science/pith/53LYGPKLQJ223HRZUONMSR5LAF/action/citation_signature","submit_replication":"https://pith.science/pith/53LYGPKLQJ223HRZUONMSR5LAF/action/replication_record"}},"created_at":"2026-05-22T02:04:52.442058+00:00","updated_at":"2026-05-22T02:04:52.442058+00:00"}