{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:R6FAA2GDQLXLI5UMLQZQLXW4ZP","short_pith_number":"pith:R6FAA2GD","schema_version":"1.0","canonical_sha256":"8f8a0068c382eeb4768c5c3305dedccbc46374c03b3a244689bc5551b1223f5a","source":{"kind":"arxiv","id":"1807.01292","version":1},"attestation_state":"computed","paper":{"title":"Intent Generation for Goal-Oriented Dialogue Systems based on Schema.org Annotations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dieter Fensel, Umutcan \\c{S}im\\c{s}ek","submitted_at":"2018-07-03T17:15:49Z","abstract_excerpt":"Goal-oriented dialogue systems typically communicate with a backend (e.g. database, Web API) to complete certain tasks to reach a goal. The intents that a dialogue system can recognize are mostly included to the system by the developer statically. For an open dialogue system that can work on more than a small set of well curated data and APIs, this manual intent creation will not scalable. In this paper, we introduce a straightforward methodology for intent creation based on semantic annotation of data and services on the web. With this method, the Natural Language Understanding (NLU) module o"},"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":"1807.01292","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-07-03T17:15:49Z","cross_cats_sorted":[],"title_canon_sha256":"c1d11b6ebe1aed977fca213b0a4f73dc6deba269cb8506d2e5bbcb137a24e529","abstract_canon_sha256":"0f1615448c6375d68afc0c7f582b45a9a43e5f73eb9284ce87ede21fb5369bff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:44.693102Z","signature_b64":"5KcISB0b9zxFh1m2JS6f2OgS3Ogu9fcu2cpdmvtrj0YtxmtRBEmASvcXF+yR/iedh0WCGgYmoL8x1+A8AFiPCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f8a0068c382eeb4768c5c3305dedccbc46374c03b3a244689bc5551b1223f5a","last_reissued_at":"2026-05-18T00:11:44.692392Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:44.692392Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Intent Generation for Goal-Oriented Dialogue Systems based on Schema.org Annotations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dieter Fensel, Umutcan \\c{S}im\\c{s}ek","submitted_at":"2018-07-03T17:15:49Z","abstract_excerpt":"Goal-oriented dialogue systems typically communicate with a backend (e.g. database, Web API) to complete certain tasks to reach a goal. The intents that a dialogue system can recognize are mostly included to the system by the developer statically. For an open dialogue system that can work on more than a small set of well curated data and APIs, this manual intent creation will not scalable. In this paper, we introduce a straightforward methodology for intent creation based on semantic annotation of data and services on the web. With this method, the Natural Language Understanding (NLU) module o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.01292","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1807.01292","created_at":"2026-05-18T00:11:44.692516+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.01292v1","created_at":"2026-05-18T00:11:44.692516+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.01292","created_at":"2026-05-18T00:11:44.692516+00:00"},{"alias_kind":"pith_short_12","alias_value":"R6FAA2GDQLXL","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"R6FAA2GDQLXLI5UM","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"R6FAA2GD","created_at":"2026-05-18T12:32:50.500415+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.10241","citing_title":"Building Korean linguistic resource for NLU data generation of banking app CS dialog system","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R6FAA2GDQLXLI5UMLQZQLXW4ZP","json":"https://pith.science/pith/R6FAA2GDQLXLI5UMLQZQLXW4ZP.json","graph_json":"https://pith.science/api/pith-number/R6FAA2GDQLXLI5UMLQZQLXW4ZP/graph.json","events_json":"https://pith.science/api/pith-number/R6FAA2GDQLXLI5UMLQZQLXW4ZP/events.json","paper":"https://pith.science/paper/R6FAA2GD"},"agent_actions":{"view_html":"https://pith.science/pith/R6FAA2GDQLXLI5UMLQZQLXW4ZP","download_json":"https://pith.science/pith/R6FAA2GDQLXLI5UMLQZQLXW4ZP.json","view_paper":"https://pith.science/paper/R6FAA2GD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.01292&json=true","fetch_graph":"https://pith.science/api/pith-number/R6FAA2GDQLXLI5UMLQZQLXW4ZP/graph.json","fetch_events":"https://pith.science/api/pith-number/R6FAA2GDQLXLI5UMLQZQLXW4ZP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R6FAA2GDQLXLI5UMLQZQLXW4ZP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R6FAA2GDQLXLI5UMLQZQLXW4ZP/action/storage_attestation","attest_author":"https://pith.science/pith/R6FAA2GDQLXLI5UMLQZQLXW4ZP/action/author_attestation","sign_citation":"https://pith.science/pith/R6FAA2GDQLXLI5UMLQZQLXW4ZP/action/citation_signature","submit_replication":"https://pith.science/pith/R6FAA2GDQLXLI5UMLQZQLXW4ZP/action/replication_record"}},"created_at":"2026-05-18T00:11:44.692516+00:00","updated_at":"2026-05-18T00:11:44.692516+00:00"}