{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:CVOYTSQQS43DV6HMSYCQZ5Q4PN","short_pith_number":"pith:CVOYTSQQ","schema_version":"1.0","canonical_sha256":"155d89ca1097363af8ec96050cf61c7b5f38cdc7d1cf320d739aeb29b838d9e4","source":{"kind":"arxiv","id":"2009.13407","version":1},"attestation_state":"computed","paper":{"title":"The Probabilistic Description Logic $\\mathcal{BALC}$","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LO","authors_text":"Leonard Botha, Rafael Pe\\~naloza, Thomas Meyer","submitted_at":"2020-09-28T15:22:52Z","abstract_excerpt":"Description logics (DLs) are well-known knowledge representation formalisms focused on the representation of terminological knowledge. Due to their first-order semantics, these languages (in their classical form) are not suitable for representing and handling uncertainty. A probabilistic extension of a light-weight DL was recently proposed for dealing with certain knowledge occurring in uncertain contexts. In this paper, we continue that line of research by introducing the Bayesian extension \\BALC of the propositionally closed DL \\ALC. We present a tableau-based procedure for deciding consiste"},"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":"2009.13407","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LO","submitted_at":"2020-09-28T15:22:52Z","cross_cats_sorted":[],"title_canon_sha256":"867b8a46ec2b9498967bcfb52ebea4a318bae38347851d69eb31f2601b659ee5","abstract_canon_sha256":"464598b5075435bc5d652079b3cb4505b9b6feee638984b458d5e55244ee22ca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:38:30.011047Z","signature_b64":"+kE3q7BZY0nFkwsgTOmCNQlZfEPPsyF6RKlwSJfvSzPU4GBvODDBe6GGey8odOX0iKqUJkbGa7FPAfN8Vn3IAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"155d89ca1097363af8ec96050cf61c7b5f38cdc7d1cf320d739aeb29b838d9e4","last_reissued_at":"2026-07-05T01:38:30.010650Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:38:30.010650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Probabilistic Description Logic $\\mathcal{BALC}$","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LO","authors_text":"Leonard Botha, Rafael Pe\\~naloza, Thomas Meyer","submitted_at":"2020-09-28T15:22:52Z","abstract_excerpt":"Description logics (DLs) are well-known knowledge representation formalisms focused on the representation of terminological knowledge. Due to their first-order semantics, these languages (in their classical form) are not suitable for representing and handling uncertainty. A probabilistic extension of a light-weight DL was recently proposed for dealing with certain knowledge occurring in uncertain contexts. In this paper, we continue that line of research by introducing the Bayesian extension \\BALC of the propositionally closed DL \\ALC. We present a tableau-based procedure for deciding consiste"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.13407","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/2009.13407/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":"2009.13407","created_at":"2026-07-05T01:38:30.010712+00:00"},{"alias_kind":"arxiv_version","alias_value":"2009.13407v1","created_at":"2026-07-05T01:38:30.010712+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.13407","created_at":"2026-07-05T01:38:30.010712+00:00"},{"alias_kind":"pith_short_12","alias_value":"CVOYTSQQS43D","created_at":"2026-07-05T01:38:30.010712+00:00"},{"alias_kind":"pith_short_16","alias_value":"CVOYTSQQS43DV6HM","created_at":"2026-07-05T01:38:30.010712+00:00"},{"alias_kind":"pith_short_8","alias_value":"CVOYTSQQ","created_at":"2026-07-05T01:38:30.010712+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.18559","citing_title":"T-CPDL: A Temporal Causal Probabilistic Description Logic for Developing Logic-RAG Agent","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CVOYTSQQS43DV6HMSYCQZ5Q4PN","json":"https://pith.science/pith/CVOYTSQQS43DV6HMSYCQZ5Q4PN.json","graph_json":"https://pith.science/api/pith-number/CVOYTSQQS43DV6HMSYCQZ5Q4PN/graph.json","events_json":"https://pith.science/api/pith-number/CVOYTSQQS43DV6HMSYCQZ5Q4PN/events.json","paper":"https://pith.science/paper/CVOYTSQQ"},"agent_actions":{"view_html":"https://pith.science/pith/CVOYTSQQS43DV6HMSYCQZ5Q4PN","download_json":"https://pith.science/pith/CVOYTSQQS43DV6HMSYCQZ5Q4PN.json","view_paper":"https://pith.science/paper/CVOYTSQQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2009.13407&json=true","fetch_graph":"https://pith.science/api/pith-number/CVOYTSQQS43DV6HMSYCQZ5Q4PN/graph.json","fetch_events":"https://pith.science/api/pith-number/CVOYTSQQS43DV6HMSYCQZ5Q4PN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CVOYTSQQS43DV6HMSYCQZ5Q4PN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CVOYTSQQS43DV6HMSYCQZ5Q4PN/action/storage_attestation","attest_author":"https://pith.science/pith/CVOYTSQQS43DV6HMSYCQZ5Q4PN/action/author_attestation","sign_citation":"https://pith.science/pith/CVOYTSQQS43DV6HMSYCQZ5Q4PN/action/citation_signature","submit_replication":"https://pith.science/pith/CVOYTSQQS43DV6HMSYCQZ5Q4PN/action/replication_record"}},"created_at":"2026-07-05T01:38:30.010712+00:00","updated_at":"2026-07-05T01:38:30.010712+00:00"}