{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:ZU5G2L6PJAYQKU44U4QAJPVQ6P","short_pith_number":"pith:ZU5G2L6P","canonical_record":{"source":{"id":"1206.1794","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2012-06-08T15:41:03Z","cross_cats_sorted":[],"title_canon_sha256":"5f66165baed17bcf61fbf778209adca302a90ff9c1b8992afc4e3357d7a46b90","abstract_canon_sha256":"39ee09d96b710bc8f10c71a0129184cbf5c9ca9cb3bc3db3bde50251a8f720b1"},"schema_version":"1.0"},"canonical_sha256":"cd3a6d2fcf483105539ca72004beb0f3c19e82950cc6470c642b9b282c5368f0","source":{"kind":"arxiv","id":"1206.1794","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1206.1794","created_at":"2026-05-18T03:53:55Z"},{"alias_kind":"arxiv_version","alias_value":"1206.1794v1","created_at":"2026-05-18T03:53:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1206.1794","created_at":"2026-05-18T03:53:55Z"},{"alias_kind":"pith_short_12","alias_value":"ZU5G2L6PJAYQ","created_at":"2026-05-18T12:27:30Z"},{"alias_kind":"pith_short_16","alias_value":"ZU5G2L6PJAYQKU44","created_at":"2026-05-18T12:27:30Z"},{"alias_kind":"pith_short_8","alias_value":"ZU5G2L6P","created_at":"2026-05-18T12:27:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:ZU5G2L6PJAYQKU44U4QAJPVQ6P","target":"record","payload":{"canonical_record":{"source":{"id":"1206.1794","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2012-06-08T15:41:03Z","cross_cats_sorted":[],"title_canon_sha256":"5f66165baed17bcf61fbf778209adca302a90ff9c1b8992afc4e3357d7a46b90","abstract_canon_sha256":"39ee09d96b710bc8f10c71a0129184cbf5c9ca9cb3bc3db3bde50251a8f720b1"},"schema_version":"1.0"},"canonical_sha256":"cd3a6d2fcf483105539ca72004beb0f3c19e82950cc6470c642b9b282c5368f0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:53:55.450304Z","signature_b64":"oMDyOQsVvaOPfkXTWdUK3StDCIHNPaHVUUk7g1LF7hHgSP0J/whdA/qONAWyLcjH1AWglLz/M6/QgiHRKuwOCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd3a6d2fcf483105539ca72004beb0f3c19e82950cc6470c642b9b282c5368f0","last_reissued_at":"2026-05-18T03:53:55.449698Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:53:55.449698Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1206.1794","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:53:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ThxYAgG0zEYCm1REbkq1DId5/OtWJv++384QUOY8yZYfqjeu+MDJwYHh47Bp3rnv0ALhRTkAijUoEtplNv4iAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T15:48:03.040487Z"},"content_sha256":"9a7e4947af7857ece55fae8f9155af1bfa319166930eea349970e5afef200469","schema_version":"1.0","event_id":"sha256:9a7e4947af7857ece55fae8f9155af1bfa319166930eea349970e5afef200469"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:ZU5G2L6PJAYQKU44U4QAJPVQ6P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fuzzy Knowledge Representation, Learning and Optimization with Bayesian Analysis in Fuzzy Semantic Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Mohamed Nazih Omri","submitted_at":"2012-06-08T15:41:03Z","abstract_excerpt":"This paper presents a method of optimization, based on both Bayesian Analysis technical and Gallois Lattice, of a Fuzzy Semantic Networks. The technical System we use learn by interpreting an unknown word using the links created between this new word and known words. The main link is provided by the context of the query. When novice's query is confused with an unknown verb (goal) applied to a known noun denoting either an object in the ideal user's Network or an object in the user's Network, the system infer that this new verb corresponds to one of the known goal. With the learning of new word"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1206.1794","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":""},"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":"2026-05-18T03:53:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zc6gT5TQAYaaOt+MSDj+oMC/jSjeuyZjHts+TSPqtUw4P5kFjXmToLXles/Cng+T7TIDOZT2NmxrpNQB9urcBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T15:48:03.040831Z"},"content_sha256":"2aad030cd093d70b29b6367bae7641c8eebaa2e1c29579ea7126367b8f8c7fd0","schema_version":"1.0","event_id":"sha256:2aad030cd093d70b29b6367bae7641c8eebaa2e1c29579ea7126367b8f8c7fd0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZU5G2L6PJAYQKU44U4QAJPVQ6P/bundle.json","state_url":"https://pith.science/pith/ZU5G2L6PJAYQKU44U4QAJPVQ6P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZU5G2L6PJAYQKU44U4QAJPVQ6P/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":"2026-07-03T15:48:03Z","links":{"resolver":"https://pith.science/pith/ZU5G2L6PJAYQKU44U4QAJPVQ6P","bundle":"https://pith.science/pith/ZU5G2L6PJAYQKU44U4QAJPVQ6P/bundle.json","state":"https://pith.science/pith/ZU5G2L6PJAYQKU44U4QAJPVQ6P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZU5G2L6PJAYQKU44U4QAJPVQ6P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:ZU5G2L6PJAYQKU44U4QAJPVQ6P","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":"39ee09d96b710bc8f10c71a0129184cbf5c9ca9cb3bc3db3bde50251a8f720b1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2012-06-08T15:41:03Z","title_canon_sha256":"5f66165baed17bcf61fbf778209adca302a90ff9c1b8992afc4e3357d7a46b90"},"schema_version":"1.0","source":{"id":"1206.1794","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1206.1794","created_at":"2026-05-18T03:53:55Z"},{"alias_kind":"arxiv_version","alias_value":"1206.1794v1","created_at":"2026-05-18T03:53:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1206.1794","created_at":"2026-05-18T03:53:55Z"},{"alias_kind":"pith_short_12","alias_value":"ZU5G2L6PJAYQ","created_at":"2026-05-18T12:27:30Z"},{"alias_kind":"pith_short_16","alias_value":"ZU5G2L6PJAYQKU44","created_at":"2026-05-18T12:27:30Z"},{"alias_kind":"pith_short_8","alias_value":"ZU5G2L6P","created_at":"2026-05-18T12:27:30Z"}],"graph_snapshots":[{"event_id":"sha256:2aad030cd093d70b29b6367bae7641c8eebaa2e1c29579ea7126367b8f8c7fd0","target":"graph","created_at":"2026-05-18T03:53:55Z","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"},"paper":{"abstract_excerpt":"This paper presents a method of optimization, based on both Bayesian Analysis technical and Gallois Lattice, of a Fuzzy Semantic Networks. The technical System we use learn by interpreting an unknown word using the links created between this new word and known words. The main link is provided by the context of the query. When novice's query is confused with an unknown verb (goal) applied to a known noun denoting either an object in the ideal user's Network or an object in the user's Network, the system infer that this new verb corresponds to one of the known goal. With the learning of new word","authors_text":"Mohamed Nazih Omri","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2012-06-08T15:41:03Z","title":"Fuzzy Knowledge Representation, Learning and Optimization with Bayesian Analysis in Fuzzy Semantic Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1206.1794","kind":"arxiv","version":1},"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":"sha256:9a7e4947af7857ece55fae8f9155af1bfa319166930eea349970e5afef200469","target":"record","created_at":"2026-05-18T03:53:55Z","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":"39ee09d96b710bc8f10c71a0129184cbf5c9ca9cb3bc3db3bde50251a8f720b1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2012-06-08T15:41:03Z","title_canon_sha256":"5f66165baed17bcf61fbf778209adca302a90ff9c1b8992afc4e3357d7a46b90"},"schema_version":"1.0","source":{"id":"1206.1794","kind":"arxiv","version":1}},"canonical_sha256":"cd3a6d2fcf483105539ca72004beb0f3c19e82950cc6470c642b9b282c5368f0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cd3a6d2fcf483105539ca72004beb0f3c19e82950cc6470c642b9b282c5368f0","first_computed_at":"2026-05-18T03:53:55.449698Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:53:55.449698Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oMDyOQsVvaOPfkXTWdUK3StDCIHNPaHVUUk7g1LF7hHgSP0J/whdA/qONAWyLcjH1AWglLz/M6/QgiHRKuwOCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T03:53:55.450304Z","signed_message":"canonical_sha256_bytes"},"source_id":"1206.1794","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a7e4947af7857ece55fae8f9155af1bfa319166930eea349970e5afef200469","sha256:2aad030cd093d70b29b6367bae7641c8eebaa2e1c29579ea7126367b8f8c7fd0"],"state_sha256":"99186d6d0253534200e9d773075ded1c800260d291f61cef673b7c1175b0968d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k2wNt3TgRinjbbUaVlcY8y+tR40X/y3MhZf2LIiRtufLqfQVDCLb9SNl/rPSGYLJakrja036Xn+h7SUwPfg/Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T15:48:03.042818Z","bundle_sha256":"dfd1f12ff09e27e39fb79a52017d3ba93a1e6a73abbe616691d7aaa14c2fd759"}}