{"id":{"repo_id":"umkc","oai_identifier":"oai:mospace.umsystem.edu:10355/89577"},"canonical_url":"https://search.dev.ndltd.org/etd/umkc/oai:mospace.umsystem.edu:10355/89577","repository":{"repo_id":"umkc","name":"University of Missouri - Kansas City","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"An Approach For Scalable First-Order Rule Learning On Twitter Data","abstract":"Scalable Rule Learning (SRLearn) is a scalable divide-and-conquer approach with graph-based modeling of social media data, to scale up first-order rule learning through Markov Logic Networks on a commodity cluster on large scale Twitter data. SRLearn takes advantage of distributed systems to partition large-scale data into smaller but meaningful partitions based on user interaction and incorporates a gradient boosting approach with a tool called BoostSRL for first-order rule mining. We show how this scalable solution on first order predicates is more accurate and efficient than existing systems, such as ProbKB (a scalable system to construct probabilistic knowledge base) and XGBoost (extreme gradient boosting) on relational data.","abstract_html":"Scalable Rule Learning (SRLearn) is a scalable divide-and-conquer approach with graph-based modeling of social media data, to scale up first-order rule learning through Markov Logic Networks on a commodity cluster on large scale Twitter data. SRLearn takes advantage of distributed systems to partition large-scale data into smaller but meaningful partitions based on user interaction and incorporates a gradient boosting approach with a tool called BoostSRL for first-order rule mining. We show how this scalable solution on first order predicates is more accurate and efficient than existing systems, such as ProbKB (a scalable system to construct probabilistic knowledge base) and XGBoost (extreme gradient boosting) on relational data.","abstract_has_math":false,"creators":["Senapati, Monica"],"institution":"University of Missouri--Kansas City","degree_name":null,"degree_level":"Doctoral","degree_discipline":"Computer Science (UMKC)","degree_department":null,"school":null,"contributors":[],"advisors":["Rao, Praveen R.","Choi, Baek-Young"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021","date_published":"2021","updated_at":"2026-07-24T05:19:15Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10355/89577","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Rao, Praveen R.","Choi, Baek-Young"]},{"key":"dc:creator","label":"Author","values":["Senapati, Monica"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-03-21T13:07:56Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-03-21T13:07:56Z"]},{"key":"dc:date.issued","label":"Date","values":["2021"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science (UMKC)","Telecommunications and Computer Networking (UMKC)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Kansas City"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10355/89577"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Title from PDF of title page viewed March 28, 2022","Dissertation advisors: Praveen Rao and Baek-Young Cho","Vita","Includes bibliographical references (pages 95-103)","Thesis (Ph.D.)--School of Computing and Engineering. 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We show how this scalable solution on first order predicates is more accurate and efficient than existing systems, such as ProbKB (a scalable system to construct probabilistic knowledge base) and XGBoost (extreme gradient boosting) on relational data."]},{"key":"dc:title","label":"Title","values":["An Approach For Scalable First-Order Rule Learning On Twitter Data"]}]}],"canonical_facts":{"dc:contributor.advisor":["Rao, Praveen R.","Choi, Baek-Young"],"dc:creator":["Senapati, Monica"],"dc:date.accessioned":["2022-03-21T13:07:56Z"],"dc:date.available":["2022-03-21T13:07:56Z"],"dc:date.issued":["2021"],"dc:description":["Title from PDF of title page viewed March 28, 2022","Dissertation advisors: Praveen Rao and Baek-Young Cho","Vita","Includes bibliographical references (pages 95-103)","Thesis (Ph.D.)--School of Computing and Engineering. 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We show how this scalable solution on first order predicates is more accurate and efficient than existing systems, such as ProbKB (a scalable system to construct probabilistic knowledge base) and XGBoost (extreme gradient boosting) on relational data."],"dc:identifier.uri":["https://hdl.handle.net/10355/89577"],"dc:title":["An Approach For Scalable First-Order Rule Learning On Twitter Data"],"thesis:degree_discipline":["Computer Science (UMKC)","Telecommunications and Computer Networking (UMKC)"],"thesis:degree_level":["Doctoral"],"thesis:institution_name":["University of Missouri--Kansas City"]},"updated_at":"2026-07-24T05:19:15Z"}