{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ELBRJOM3VNQEH37LQC2CVCNRVD","short_pith_number":"pith:ELBRJOM3","schema_version":"1.0","canonical_sha256":"22c314b99bab6043efeb80b42a89b1a8ca59ea257bffc444d7643b4d41f9b611","source":{"kind":"arxiv","id":"1710.04822","version":2},"attestation_state":"computed","paper":{"title":"Fast Top-k Area Topics Extraction with Knowledge Base","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.AI","authors_text":"Fang Zhang, Jie Tang, Jingfei Han, Marie-Francine Moens, Shiyin Wang, Xiaochen Wang","submitted_at":"2017-10-13T06:34:44Z","abstract_excerpt":"What are the most popular research topics in Artificial Intelligence (AI)? We formulate the problem as extracting top-$k$ topics that can best represent a given area with the help of knowledge base. We theoretically prove that the problem is NP-hard and propose an optimization model, FastKATE, to address this problem by combining both explicit and latent representations for each topic. We leverage a large-scale knowledge base (Wikipedia) to generate topic embeddings using neural networks and use this kind of representations to help capture the representativeness of topics for given areas. We d"},"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":"1710.04822","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-10-13T06:34:44Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"8e7dd9d7d1c79b652c3517aa800d416e8d1e49f9c7eea5a64378b3eba1c17d56","abstract_canon_sha256":"1dc6a5b5dc3a3c9a69f697f1f55c336307b5bc796ea128a5eadd5e0823859c58"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:01.057407Z","signature_b64":"8zQ/uXFJxo0AHa/JvpFk+3HtQrQsqie+/G5OuMpRlCbIEHw0Ls0K3Yg/5rSq8BSxMFNSRxzWFNC2gOq5nwszAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22c314b99bab6043efeb80b42a89b1a8ca59ea257bffc444d7643b4d41f9b611","last_reissued_at":"2026-05-18T00:29:01.056872Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:01.056872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast Top-k Area Topics Extraction with Knowledge Base","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.AI","authors_text":"Fang Zhang, Jie Tang, Jingfei Han, Marie-Francine Moens, Shiyin Wang, Xiaochen Wang","submitted_at":"2017-10-13T06:34:44Z","abstract_excerpt":"What are the most popular research topics in Artificial Intelligence (AI)? We formulate the problem as extracting top-$k$ topics that can best represent a given area with the help of knowledge base. We theoretically prove that the problem is NP-hard and propose an optimization model, FastKATE, to address this problem by combining both explicit and latent representations for each topic. We leverage a large-scale knowledge base (Wikipedia) to generate topic embeddings using neural networks and use this kind of representations to help capture the representativeness of topics for given areas. We d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04822","kind":"arxiv","version":2},"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":"1710.04822","created_at":"2026-05-18T00:29:01.056956+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.04822v2","created_at":"2026-05-18T00:29:01.056956+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04822","created_at":"2026-05-18T00:29:01.056956+00:00"},{"alias_kind":"pith_short_12","alias_value":"ELBRJOM3VNQE","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_16","alias_value":"ELBRJOM3VNQEH37L","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_8","alias_value":"ELBRJOM3","created_at":"2026-05-18T12:31:12.930513+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/ELBRJOM3VNQEH37LQC2CVCNRVD","json":"https://pith.science/pith/ELBRJOM3VNQEH37LQC2CVCNRVD.json","graph_json":"https://pith.science/api/pith-number/ELBRJOM3VNQEH37LQC2CVCNRVD/graph.json","events_json":"https://pith.science/api/pith-number/ELBRJOM3VNQEH37LQC2CVCNRVD/events.json","paper":"https://pith.science/paper/ELBRJOM3"},"agent_actions":{"view_html":"https://pith.science/pith/ELBRJOM3VNQEH37LQC2CVCNRVD","download_json":"https://pith.science/pith/ELBRJOM3VNQEH37LQC2CVCNRVD.json","view_paper":"https://pith.science/paper/ELBRJOM3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.04822&json=true","fetch_graph":"https://pith.science/api/pith-number/ELBRJOM3VNQEH37LQC2CVCNRVD/graph.json","fetch_events":"https://pith.science/api/pith-number/ELBRJOM3VNQEH37LQC2CVCNRVD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ELBRJOM3VNQEH37LQC2CVCNRVD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ELBRJOM3VNQEH37LQC2CVCNRVD/action/storage_attestation","attest_author":"https://pith.science/pith/ELBRJOM3VNQEH37LQC2CVCNRVD/action/author_attestation","sign_citation":"https://pith.science/pith/ELBRJOM3VNQEH37LQC2CVCNRVD/action/citation_signature","submit_replication":"https://pith.science/pith/ELBRJOM3VNQEH37LQC2CVCNRVD/action/replication_record"}},"created_at":"2026-05-18T00:29:01.056956+00:00","updated_at":"2026-05-18T00:29:01.056956+00:00"}