{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3SUODX5KFNN565UECOI46AEQST","short_pith_number":"pith:3SUODX5K","schema_version":"1.0","canonical_sha256":"dca8e1dfaa2b5bdf76841391cf009094dada2ad38caf3abc2c4eaa4b74d0fc8a","source":{"kind":"arxiv","id":"2509.05393","version":1},"attestation_state":"computed","paper":{"title":"Inferring Prerequisite Knowledge Concepts in Educational Knowledge Graphs: A Multi-criteria Approach","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CY","authors_text":"Mohamed Amine Chatti, Nasha Wibowo, Qurat Ul Ain, Rawaa Alatrash","submitted_at":"2025-09-05T10:37:58Z","abstract_excerpt":"Educational Knowledge Graphs (EduKGs) organize various learning entities and their relationships to support structured and adaptive learning. Prerequisite relationships (PRs) are critical in EduKGs for defining the logical order in which concepts should be learned. However, the current EduKG in the MOOC platform CourseMapper lacks explicit PR links, and manually annotating them is time-consuming and inconsistent. To address this, we propose an unsupervised method for automatically inferring concept PRs without relying on labeled data. We define ten criteria based on document-based, Wikipedia h"},"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":"2509.05393","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CY","submitted_at":"2025-09-05T10:37:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1cdb84e3971853524ee569c6a78e89c9397115f28841337c9abbc98b3e1a5e7b","abstract_canon_sha256":"14f2677d011eb181847bf5700b7f1f8fca5b0b5564a403debd2fcd34139d0891"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:05:46.445511Z","signature_b64":"laREn0l7wReQJPap8wd8C9EC+V8ZbCr3ZmS5rsYEqlglmJZrNH2IcB4ft3zMTWR0uYjMTTn/6hpaqE4TcXwzCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dca8e1dfaa2b5bdf76841391cf009094dada2ad38caf3abc2c4eaa4b74d0fc8a","last_reissued_at":"2026-07-05T12:05:46.445104Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:05:46.445104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Inferring Prerequisite Knowledge Concepts in Educational Knowledge Graphs: A Multi-criteria Approach","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CY","authors_text":"Mohamed Amine Chatti, Nasha Wibowo, Qurat Ul Ain, Rawaa Alatrash","submitted_at":"2025-09-05T10:37:58Z","abstract_excerpt":"Educational Knowledge Graphs (EduKGs) organize various learning entities and their relationships to support structured and adaptive learning. Prerequisite relationships (PRs) are critical in EduKGs for defining the logical order in which concepts should be learned. However, the current EduKG in the MOOC platform CourseMapper lacks explicit PR links, and manually annotating them is time-consuming and inconsistent. To address this, we propose an unsupervised method for automatically inferring concept PRs without relying on labeled data. We define ten criteria based on document-based, Wikipedia h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.05393","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/2509.05393/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":"2509.05393","created_at":"2026-07-05T12:05:46.445162+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.05393v1","created_at":"2026-07-05T12:05:46.445162+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.05393","created_at":"2026-07-05T12:05:46.445162+00:00"},{"alias_kind":"pith_short_12","alias_value":"3SUODX5KFNN5","created_at":"2026-07-05T12:05:46.445162+00:00"},{"alias_kind":"pith_short_16","alias_value":"3SUODX5KFNN565UE","created_at":"2026-07-05T12:05:46.445162+00:00"},{"alias_kind":"pith_short_8","alias_value":"3SUODX5K","created_at":"2026-07-05T12:05:46.445162+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/3SUODX5KFNN565UECOI46AEQST","json":"https://pith.science/pith/3SUODX5KFNN565UECOI46AEQST.json","graph_json":"https://pith.science/api/pith-number/3SUODX5KFNN565UECOI46AEQST/graph.json","events_json":"https://pith.science/api/pith-number/3SUODX5KFNN565UECOI46AEQST/events.json","paper":"https://pith.science/paper/3SUODX5K"},"agent_actions":{"view_html":"https://pith.science/pith/3SUODX5KFNN565UECOI46AEQST","download_json":"https://pith.science/pith/3SUODX5KFNN565UECOI46AEQST.json","view_paper":"https://pith.science/paper/3SUODX5K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.05393&json=true","fetch_graph":"https://pith.science/api/pith-number/3SUODX5KFNN565UECOI46AEQST/graph.json","fetch_events":"https://pith.science/api/pith-number/3SUODX5KFNN565UECOI46AEQST/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3SUODX5KFNN565UECOI46AEQST/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3SUODX5KFNN565UECOI46AEQST/action/storage_attestation","attest_author":"https://pith.science/pith/3SUODX5KFNN565UECOI46AEQST/action/author_attestation","sign_citation":"https://pith.science/pith/3SUODX5KFNN565UECOI46AEQST/action/citation_signature","submit_replication":"https://pith.science/pith/3SUODX5KFNN565UECOI46AEQST/action/replication_record"}},"created_at":"2026-07-05T12:05:46.445162+00:00","updated_at":"2026-07-05T12:05:46.445162+00:00"}