{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:VHSJENT3SLMS4O3DLHZ2BIH7L4","short_pith_number":"pith:VHSJENT3","schema_version":"1.0","canonical_sha256":"a9e492367b92d92e3b6359f3a0a0ff5f0678edf7fd70530db10c70361d4167fa","source":{"kind":"arxiv","id":"1907.01417","version":2},"attestation_state":"computed","paper":{"title":"Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ashok Thillaisundaram, Julien Fauqueur, Theodosia Togia","submitted_at":"2019-07-02T14:53:30Z","abstract_excerpt":"Knowledge base construction is crucial for summarising, understanding and inferring relationships between biomedical entities. However, for many practical applications such as drug discovery, the scarcity of relevant facts (e.g. gene X is therapeutic target for disease Y) severely limits a domain expert's ability to create a usable knowledge base, either directly or by training a relation extraction model.\n  In this paper, we present a simple and effective method of extracting new facts with a pre-specified binary relationship type from the biomedical literature, without requiring any training"},"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":"1907.01417","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2019-07-02T14:53:30Z","cross_cats_sorted":[],"title_canon_sha256":"b03ada40008f6b3b1aa3c8d4fd6090ff97d9cf436913390b4d4ef06bcfdf592c","abstract_canon_sha256":"f45cf9a01c5f45eda6580d4a9183280be14a85b0ca655f795935ae002ee783bd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:36.001489Z","signature_b64":"YroUF0VvY2OKCrxVR1cqtv6Tztki7ZXwRyK3Cn6ZTOEZRlQLgrI1Ingd0GksFshC87fpnHv6uVoG+/eXa5gjBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a9e492367b92d92e3b6359f3a0a0ff5f0678edf7fd70530db10c70361d4167fa","last_reissued_at":"2026-05-17T23:41:36.000910Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:36.000910Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ashok Thillaisundaram, Julien Fauqueur, Theodosia Togia","submitted_at":"2019-07-02T14:53:30Z","abstract_excerpt":"Knowledge base construction is crucial for summarising, understanding and inferring relationships between biomedical entities. However, for many practical applications such as drug discovery, the scarcity of relevant facts (e.g. gene X is therapeutic target for disease Y) severely limits a domain expert's ability to create a usable knowledge base, either directly or by training a relation extraction model.\n  In this paper, we present a simple and effective method of extracting new facts with a pre-specified binary relationship type from the biomedical literature, without requiring any training"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.01417","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1907.01417","created_at":"2026-05-17T23:41:36.001002+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.01417v2","created_at":"2026-05-17T23:41:36.001002+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.01417","created_at":"2026-05-17T23:41:36.001002+00:00"},{"alias_kind":"pith_short_12","alias_value":"VHSJENT3SLMS","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"VHSJENT3SLMS4O3D","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"VHSJENT3","created_at":"2026-05-18T12:33:30.264802+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/VHSJENT3SLMS4O3DLHZ2BIH7L4","json":"https://pith.science/pith/VHSJENT3SLMS4O3DLHZ2BIH7L4.json","graph_json":"https://pith.science/api/pith-number/VHSJENT3SLMS4O3DLHZ2BIH7L4/graph.json","events_json":"https://pith.science/api/pith-number/VHSJENT3SLMS4O3DLHZ2BIH7L4/events.json","paper":"https://pith.science/paper/VHSJENT3"},"agent_actions":{"view_html":"https://pith.science/pith/VHSJENT3SLMS4O3DLHZ2BIH7L4","download_json":"https://pith.science/pith/VHSJENT3SLMS4O3DLHZ2BIH7L4.json","view_paper":"https://pith.science/paper/VHSJENT3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.01417&json=true","fetch_graph":"https://pith.science/api/pith-number/VHSJENT3SLMS4O3DLHZ2BIH7L4/graph.json","fetch_events":"https://pith.science/api/pith-number/VHSJENT3SLMS4O3DLHZ2BIH7L4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VHSJENT3SLMS4O3DLHZ2BIH7L4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VHSJENT3SLMS4O3DLHZ2BIH7L4/action/storage_attestation","attest_author":"https://pith.science/pith/VHSJENT3SLMS4O3DLHZ2BIH7L4/action/author_attestation","sign_citation":"https://pith.science/pith/VHSJENT3SLMS4O3DLHZ2BIH7L4/action/citation_signature","submit_replication":"https://pith.science/pith/VHSJENT3SLMS4O3DLHZ2BIH7L4/action/replication_record"}},"created_at":"2026-05-17T23:41:36.001002+00:00","updated_at":"2026-05-17T23:41:36.001002+00:00"}