{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:DOBELNOGQSID2FE6EUJ4KKRG64","short_pith_number":"pith:DOBELNOG","schema_version":"1.0","canonical_sha256":"1b8245b5c684903d149e2513c52a26f73bf337e19d90b104504154017c7e44f8","source":{"kind":"arxiv","id":"1606.00979","version":1},"attestation_state":"computed","paper":{"title":"Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.NE"],"primary_cat":"cs.IR","authors_text":"Guoliang Ji, Hua Wu, Jun Zhao, Kang Liu, Shizhu He, Yuanzhe Zhang, Zhanyi Liu","submitted_at":"2016-06-03T06:40:14Z","abstract_excerpt":"With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Knowledge base-based question answering (KB-QA) is one of the most promising approaches to access the substantial knowledge. Meantime, as the neural network-based (NN-based) methods develop, NN-based KB-QA has already achieved impressive results. However, previous work did not put emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers. This simple representation strategy is unable to express the proper "},"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":"1606.00979","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-06-03T06:40:14Z","cross_cats_sorted":["cs.AI","cs.CL","cs.NE"],"title_canon_sha256":"a3ba888a99b56a498b69ea1ffc63e74988e78a5052bedbde9cee1121ae3da623","abstract_canon_sha256":"cb2159855c7268c6cd4471785947398cb12ee62953d2392fef4249cd95a342ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:12:59.971231Z","signature_b64":"m6XgGRp/GI2vN8jWyD+RIyJr4Q2xdwZQo0GI2CwpFc4D74ViVQewTmijuzlA9TUiU0kFEz8dZv7oByrHaAmBDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b8245b5c684903d149e2513c52a26f73bf337e19d90b104504154017c7e44f8","last_reissued_at":"2026-05-18T01:12:59.970879Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:12:59.970879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.NE"],"primary_cat":"cs.IR","authors_text":"Guoliang Ji, Hua Wu, Jun Zhao, Kang Liu, Shizhu He, Yuanzhe Zhang, Zhanyi Liu","submitted_at":"2016-06-03T06:40:14Z","abstract_excerpt":"With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Knowledge base-based question answering (KB-QA) is one of the most promising approaches to access the substantial knowledge. Meantime, as the neural network-based (NN-based) methods develop, NN-based KB-QA has already achieved impressive results. However, previous work did not put emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers. This simple representation strategy is unable to express the proper "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.00979","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":"1606.00979","created_at":"2026-05-18T01:12:59.970937+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.00979v1","created_at":"2026-05-18T01:12:59.970937+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.00979","created_at":"2026-05-18T01:12:59.970937+00:00"},{"alias_kind":"pith_short_12","alias_value":"DOBELNOGQSID","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_16","alias_value":"DOBELNOGQSID2FE6","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_8","alias_value":"DOBELNOG","created_at":"2026-05-18T12:30:12.583610+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/DOBELNOGQSID2FE6EUJ4KKRG64","json":"https://pith.science/pith/DOBELNOGQSID2FE6EUJ4KKRG64.json","graph_json":"https://pith.science/api/pith-number/DOBELNOGQSID2FE6EUJ4KKRG64/graph.json","events_json":"https://pith.science/api/pith-number/DOBELNOGQSID2FE6EUJ4KKRG64/events.json","paper":"https://pith.science/paper/DOBELNOG"},"agent_actions":{"view_html":"https://pith.science/pith/DOBELNOGQSID2FE6EUJ4KKRG64","download_json":"https://pith.science/pith/DOBELNOGQSID2FE6EUJ4KKRG64.json","view_paper":"https://pith.science/paper/DOBELNOG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.00979&json=true","fetch_graph":"https://pith.science/api/pith-number/DOBELNOGQSID2FE6EUJ4KKRG64/graph.json","fetch_events":"https://pith.science/api/pith-number/DOBELNOGQSID2FE6EUJ4KKRG64/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DOBELNOGQSID2FE6EUJ4KKRG64/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DOBELNOGQSID2FE6EUJ4KKRG64/action/storage_attestation","attest_author":"https://pith.science/pith/DOBELNOGQSID2FE6EUJ4KKRG64/action/author_attestation","sign_citation":"https://pith.science/pith/DOBELNOGQSID2FE6EUJ4KKRG64/action/citation_signature","submit_replication":"https://pith.science/pith/DOBELNOGQSID2FE6EUJ4KKRG64/action/replication_record"}},"created_at":"2026-05-18T01:12:59.970937+00:00","updated_at":"2026-05-18T01:12:59.970937+00:00"}