{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:7YTFDGFDICWWG2QOMR7VDT3Q47","short_pith_number":"pith:7YTFDGFD","canonical_record":{"source":{"id":"2412.00608","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-11-30T23:11:44Z","cross_cats_sorted":[],"title_canon_sha256":"262763385b50acbbd500008aa4643da420558b969df483f95c39671812770345","abstract_canon_sha256":"0ec36aa545dbe679462efee0004702737c95e7386ddca31353739d698b4c73ff"},"schema_version":"1.0"},"canonical_sha256":"fe265198a340ad636a0e647f51cf70e7e037d71a0316d6f3794fdef56a1e5c84","source":{"kind":"arxiv","id":"2412.00608","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.00608","created_at":"2026-07-05T09:46:55Z"},{"alias_kind":"arxiv_version","alias_value":"2412.00608v3","created_at":"2026-07-05T09:46:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.00608","created_at":"2026-07-05T09:46:55Z"},{"alias_kind":"pith_short_12","alias_value":"7YTFDGFDICWW","created_at":"2026-07-05T09:46:55Z"},{"alias_kind":"pith_short_16","alias_value":"7YTFDGFDICWWG2QO","created_at":"2026-07-05T09:46:55Z"},{"alias_kind":"pith_short_8","alias_value":"7YTFDGFD","created_at":"2026-07-05T09:46:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:7YTFDGFDICWWG2QOMR7VDT3Q47","target":"record","payload":{"canonical_record":{"source":{"id":"2412.00608","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-11-30T23:11:44Z","cross_cats_sorted":[],"title_canon_sha256":"262763385b50acbbd500008aa4643da420558b969df483f95c39671812770345","abstract_canon_sha256":"0ec36aa545dbe679462efee0004702737c95e7386ddca31353739d698b4c73ff"},"schema_version":"1.0"},"canonical_sha256":"fe265198a340ad636a0e647f51cf70e7e037d71a0316d6f3794fdef56a1e5c84","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:46:55.490461Z","signature_b64":"CPWlYlZOe+Dt4QNWazt6dVJ+5z4L4RUINTG3F5A3OcdLguQaKVjWyNiEFxyBcv3yLrINkJUkib+ba54Sc6JHCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe265198a340ad636a0e647f51cf70e7e037d71a0316d6f3794fdef56a1e5c84","last_reissued_at":"2026-07-05T09:46:55.489968Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:46:55.489968Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.00608","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:46:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HblI7kiomlTJhc2an9AtQdLO2Hd2B3H2ZEeUL3DQXgzOarajCY25mbZ3L1zTK38NtUemvbjmyOLgVR1i1Mt2Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T17:09:25.989055Z"},"content_sha256":"19f247965b7513dd7e00d06cdb2d74669dfe0313d5ac308c5af7b11f941b3605","schema_version":"1.0","event_id":"sha256:19f247965b7513dd7e00d06cdb2d74669dfe0313d5ac308c5af7b11f941b3605"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:7YTFDGFDICWWG2QOMR7VDT3Q47","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Leveraging LLM for Automated Ontology Extraction and Knowledge Graph Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Mohammad Sadeq Abolhasani, Rong Pan","submitted_at":"2024-11-30T23:11:44Z","abstract_excerpt":"Extracting relevant and structured knowledge from large, complex technical documents within the Reliability and Maintainability (RAM) domain is labor-intensive and prone to errors. Our work addresses this challenge by presenting OntoKGen, a genuine pipeline for ontology extraction and Knowledge Graph (KG) generation. OntoKGen leverages Large Language Models (LLMs) through an interactive user interface guided by our adaptive iterative Chain of Thought (CoT) algorithm to ensure that the ontology extraction process and, thus, KG generation align with user-specific requirements. Although KG genera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.00608","kind":"arxiv","version":3},"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/2412.00608/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:46:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CXaS+2lfARJ/ReHa5RhY+JNtQgMi6+xgw9bpOR0q3lTAVPD2IuXq7fjwAeo7sSnx7emb8sQjfF+T27dSPPzfDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T17:09:25.989449Z"},"content_sha256":"95e23ba813e5872842f853e18d290b6b65e41ff6edce0e4d0818f7af010cd7f3","schema_version":"1.0","event_id":"sha256:95e23ba813e5872842f853e18d290b6b65e41ff6edce0e4d0818f7af010cd7f3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7YTFDGFDICWWG2QOMR7VDT3Q47/bundle.json","state_url":"https://pith.science/pith/7YTFDGFDICWWG2QOMR7VDT3Q47/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7YTFDGFDICWWG2QOMR7VDT3Q47/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-24T17:09:25Z","links":{"resolver":"https://pith.science/pith/7YTFDGFDICWWG2QOMR7VDT3Q47","bundle":"https://pith.science/pith/7YTFDGFDICWWG2QOMR7VDT3Q47/bundle.json","state":"https://pith.science/pith/7YTFDGFDICWWG2QOMR7VDT3Q47/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7YTFDGFDICWWG2QOMR7VDT3Q47/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:7YTFDGFDICWWG2QOMR7VDT3Q47","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0ec36aa545dbe679462efee0004702737c95e7386ddca31353739d698b4c73ff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-11-30T23:11:44Z","title_canon_sha256":"262763385b50acbbd500008aa4643da420558b969df483f95c39671812770345"},"schema_version":"1.0","source":{"id":"2412.00608","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.00608","created_at":"2026-07-05T09:46:55Z"},{"alias_kind":"arxiv_version","alias_value":"2412.00608v3","created_at":"2026-07-05T09:46:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.00608","created_at":"2026-07-05T09:46:55Z"},{"alias_kind":"pith_short_12","alias_value":"7YTFDGFDICWW","created_at":"2026-07-05T09:46:55Z"},{"alias_kind":"pith_short_16","alias_value":"7YTFDGFDICWWG2QO","created_at":"2026-07-05T09:46:55Z"},{"alias_kind":"pith_short_8","alias_value":"7YTFDGFD","created_at":"2026-07-05T09:46:55Z"}],"graph_snapshots":[{"event_id":"sha256:95e23ba813e5872842f853e18d290b6b65e41ff6edce0e4d0818f7af010cd7f3","target":"graph","created_at":"2026-07-05T09:46:55Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2412.00608/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Extracting relevant and structured knowledge from large, complex technical documents within the Reliability and Maintainability (RAM) domain is labor-intensive and prone to errors. Our work addresses this challenge by presenting OntoKGen, a genuine pipeline for ontology extraction and Knowledge Graph (KG) generation. OntoKGen leverages Large Language Models (LLMs) through an interactive user interface guided by our adaptive iterative Chain of Thought (CoT) algorithm to ensure that the ontology extraction process and, thus, KG generation align with user-specific requirements. Although KG genera","authors_text":"Mohammad Sadeq Abolhasani, Rong Pan","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-11-30T23:11:44Z","title":"Leveraging LLM for Automated Ontology Extraction and Knowledge Graph Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.00608","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:19f247965b7513dd7e00d06cdb2d74669dfe0313d5ac308c5af7b11f941b3605","target":"record","created_at":"2026-07-05T09:46:55Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0ec36aa545dbe679462efee0004702737c95e7386ddca31353739d698b4c73ff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-11-30T23:11:44Z","title_canon_sha256":"262763385b50acbbd500008aa4643da420558b969df483f95c39671812770345"},"schema_version":"1.0","source":{"id":"2412.00608","kind":"arxiv","version":3}},"canonical_sha256":"fe265198a340ad636a0e647f51cf70e7e037d71a0316d6f3794fdef56a1e5c84","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fe265198a340ad636a0e647f51cf70e7e037d71a0316d6f3794fdef56a1e5c84","first_computed_at":"2026-07-05T09:46:55.489968Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:46:55.489968Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CPWlYlZOe+Dt4QNWazt6dVJ+5z4L4RUINTG3F5A3OcdLguQaKVjWyNiEFxyBcv3yLrINkJUkib+ba54Sc6JHCw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:46:55.490461Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.00608","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:19f247965b7513dd7e00d06cdb2d74669dfe0313d5ac308c5af7b11f941b3605","sha256:95e23ba813e5872842f853e18d290b6b65e41ff6edce0e4d0818f7af010cd7f3"],"state_sha256":"ec20c059c23d766a01306b2457222d0c14de2303b0f1ac117c3837be2a0a02bd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Lqac35udLr1WnW5LVi8ZSGLnVRYXmw/Jh4Wxar+qtCljX5M/Ao7G4tG8q8IWJqpE4geOoBE4CgLX489+VPItCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-24T17:09:25.991629Z","bundle_sha256":"8e47870398c505c7b51158a8c3e1c2d866cf3d8412e9b5286e2ae15b45fd2f18"}}