{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:JYI2OWRWOCURBMM337Y7A42BXR","short_pith_number":"pith:JYI2OWRW","schema_version":"1.0","canonical_sha256":"4e11a75a3670a910b19bdff1f07341bc66a877217d23e05dbcde6f5c4d717308","source":{"kind":"arxiv","id":"2607.14494","version":1},"attestation_state":"computed","paper":{"title":"SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.IR","cs.LG"],"primary_cat":"cs.AI","authors_text":"Koji Tsuda, Yiming Zhang","submitted_at":"2026-07-16T02:17:21Z","abstract_excerpt":"Complex knowledge base question answering (KBQA) is commonly approached through either information retrieval over a question-specific subgraph or semantic parsing into an executable logical form. We study the latter paradigm. Recent large language model agents make semantic parsing interactive: they alternate between reasoning, querying the knowledge base, and extending a partial SPARQL query. This interleaving reduces reliance on one-shot generation, but makes the quality of \\emph{KB grounding} depend on what the interaction tools expose. Existing agents retrieve or prune candidate properties"},"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":"2607.14494","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-16T02:17:21Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"bd42672228120ab3ae320b81ac84c06f285a0132a65dab077d8014b2934ba1f1","abstract_canon_sha256":"b06cb0efb31a656a1c5ca946222abaf297f3bd7955a223efb00c7ae9a24fce65"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-17T00:21:15.376141Z","signature_b64":"oC0bDxTB9HoysTK+Rl76jBT4CUNdsTgEHMriY7qiK45NIFfPJnWJ+RnRgOydMNVUnk6cT+0cpz0xrNs0LQuDCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e11a75a3670a910b19bdff1f07341bc66a877217d23e05dbcde6f5c4d717308","last_reissued_at":"2026-07-17T00:21:15.375285Z","signature_status":"signed_v1","first_computed_at":"2026-07-17T00:21:15.375285Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.IR","cs.LG"],"primary_cat":"cs.AI","authors_text":"Koji Tsuda, Yiming Zhang","submitted_at":"2026-07-16T02:17:21Z","abstract_excerpt":"Complex knowledge base question answering (KBQA) is commonly approached through either information retrieval over a question-specific subgraph or semantic parsing into an executable logical form. We study the latter paradigm. Recent large language model agents make semantic parsing interactive: they alternate between reasoning, querying the knowledge base, and extending a partial SPARQL query. This interleaving reduces reliance on one-shot generation, but makes the quality of \\emph{KB grounding} depend on what the interaction tools expose. Existing agents retrieve or prune candidate properties"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.14494","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/2607.14494/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":"2607.14494","created_at":"2026-07-17T00:21:15.375727+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.14494v1","created_at":"2026-07-17T00:21:15.375727+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.14494","created_at":"2026-07-17T00:21:15.375727+00:00"},{"alias_kind":"pith_short_12","alias_value":"JYI2OWRWOCUR","created_at":"2026-07-17T00:21:15.375727+00:00"},{"alias_kind":"pith_short_16","alias_value":"JYI2OWRWOCURBMM3","created_at":"2026-07-17T00:21:15.375727+00:00"},{"alias_kind":"pith_short_8","alias_value":"JYI2OWRW","created_at":"2026-07-17T00:21:15.375727+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/JYI2OWRWOCURBMM337Y7A42BXR","json":"https://pith.science/pith/JYI2OWRWOCURBMM337Y7A42BXR.json","graph_json":"https://pith.science/api/pith-number/JYI2OWRWOCURBMM337Y7A42BXR/graph.json","events_json":"https://pith.science/api/pith-number/JYI2OWRWOCURBMM337Y7A42BXR/events.json","paper":"https://pith.science/paper/JYI2OWRW"},"agent_actions":{"view_html":"https://pith.science/pith/JYI2OWRWOCURBMM337Y7A42BXR","download_json":"https://pith.science/pith/JYI2OWRWOCURBMM337Y7A42BXR.json","view_paper":"https://pith.science/paper/JYI2OWRW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.14494&json=true","fetch_graph":"https://pith.science/api/pith-number/JYI2OWRWOCURBMM337Y7A42BXR/graph.json","fetch_events":"https://pith.science/api/pith-number/JYI2OWRWOCURBMM337Y7A42BXR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JYI2OWRWOCURBMM337Y7A42BXR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JYI2OWRWOCURBMM337Y7A42BXR/action/storage_attestation","attest_author":"https://pith.science/pith/JYI2OWRWOCURBMM337Y7A42BXR/action/author_attestation","sign_citation":"https://pith.science/pith/JYI2OWRWOCURBMM337Y7A42BXR/action/citation_signature","submit_replication":"https://pith.science/pith/JYI2OWRWOCURBMM337Y7A42BXR/action/replication_record"}},"created_at":"2026-07-17T00:21:15.375727+00:00","updated_at":"2026-07-17T00:21:15.375727+00:00"}