{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108194"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108194","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Semantic pattern discovery in open information extraction","abstract":"Open information extraction (OpenIE) is a novel paradigm that produces structured information from unstructured text with minimum or no supervision. The task involves extracting relevant relation tuples or expressions from a text corpus. Existing methods in the domain tend to produce a large percentage of ill-structured, incomplete or redundant extractions which cannot be directly used in downstream applications, and often fail on sentences with long and complex structures. In this paper, we propose a novel semantic pattern-discovery for OpenIE (SemPatIE) framework which extracts relations in the form of typed textual pattern structures, called meta patterns and groups semantically similar pattern structures. To perform these tasks, the framework uses three techniques: (1) it simplifies complex sentence structures by performing a context-aware sentence segmentation method which splits the dependency graph of sentences at noun or verb level and enables pattern extraction between distantly placed entities; (2) it extracts meta patterns and handles its pattern sparsity problem by introducing a novel idea of iterative frequent pattern mining and nested push-ups; (3) it generates semantic pattern clusters by embedding a multi text-based network between entities, entity types, extracted meta patterns and context words. Experiments show SemPatIE outperforms state-of-the-art OpenIE baselines in handling structurally complex sentences and has a significantly higher recall than existing pattern-based methods. Case studies exhibit the framework's high generalization ability and scalabilty, and effective clustering performance which has direct applications in downstream tasks like knowledge graph construction, evidence mining and truth finding.","abstract_html":"Open information extraction (OpenIE) is a novel paradigm that produces structured information from unstructured text with minimum or no supervision. The task involves extracting relevant relation tuples or expressions from a text corpus. Existing methods in the domain tend to produce a large percentage of ill-structured, incomplete or redundant extractions which cannot be directly used in downstream applications, and often fail on sentences with long and complex structures. In this paper, we propose a novel semantic pattern-discovery for OpenIE (SemPatIE) framework which extracts relations in the form of typed textual pattern structures, called meta patterns and groups semantically similar pattern structures. To perform these tasks, the framework uses three techniques: (1) it simplifies complex sentence structures by performing a context-aware sentence segmentation method which splits the dependency graph of sentences at noun or verb level and enables pattern extraction between distantly placed entities; (2) it extracts meta patterns and handles its pattern sparsity problem by introducing a novel idea of iterative frequent pattern mining and nested push-ups; (3) it generates semantic pattern clusters by embedding a multi text-based network between entities, entity types, extracted meta patterns and context words. Experiments show SemPatIE outperforms state-of-the-art OpenIE baselines in handling structurally complex sentences and has a significantly higher recall than existing pattern-based methods. Case studies exhibit the framework's high generalization ability and scalabilty, and effective clustering performance which has direct applications in downstream tasks like knowledge graph construction, evidence mining and truth finding.","abstract_has_math":false,"creators":["Chauhan, Aabhas"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Han, Jiawei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T23:58:49Z","date_published":"2020-08-26T23:58:49Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Information Extraction","Pattern Mining"],"languages":["en"],"rights":["Copyright 2020 Aabhas Chauhan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108194","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei"]},{"key":"dc:creator","label":"Author","values":["Chauhan, Aabhas"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T23:58:49Z","2022-08-26T23:58:55Z","2020-05-13","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Information Extraction","Pattern Mining"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Aabhas Chauhan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108194"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Open information extraction (OpenIE) is a novel paradigm that produces structured information from unstructured text with minimum or no supervision. The task involves extracting relevant relation tuples or expressions from a text corpus. Existing methods in the domain tend to produce a large percentage of ill-structured, incomplete or redundant extractions which cannot be directly used in downstream applications, and often fail on sentences with long and complex structures. In this paper, we propose a novel semantic pattern-discovery for OpenIE (SemPatIE) framework which extracts relations in the form of typed textual pattern structures, called meta patterns and groups semantically similar pattern structures. To perform these tasks, the framework uses three techniques: (1) it simplifies complex sentence structures by performing a context-aware sentence segmentation method which splits the dependency graph of sentences at noun or verb level and enables pattern extraction between distantly placed entities; (2) it extracts meta patterns and handles its pattern sparsity problem by introducing a novel idea of iterative frequent pattern mining and nested push-ups; (3) it generates semantic pattern clusters by embedding a multi text-based network between entities, entity types, extracted meta patterns and context words. Experiments show SemPatIE outperforms state-of-the-art OpenIE baselines in handling structurally complex sentences and has a significantly higher recall than existing pattern-based methods. Case studies exhibit the framework's high generalization ability and scalabilty, and effective clustering performance which has direct applications in downstream tasks like knowledge graph construction, evidence mining and truth finding.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Aabhas Chauhan, accepted the attached license on 2020-05-12 at 17:23.","The student, Aabhas Chauhan, submitted this Thesis for approval on 2020-05-12 at 17:30.","This Thesis was approved for publication on 2020-05-13 at 14:47.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15369 on 2020-08-25 at 17:31:19","Made available in DSpace on 2020-08-26T23:58:49Z (GMT). No. of bitstreams: 2 CHAUHAN-THESIS-2020.pdf: 740952 bytes, checksum: 80bf6487f3c6a4753336adbad2bd7427 (MD5) LICENSE.txt: 4211 bytes, checksum: 263b7214aabab193365e29534b125f54 (MD5) Previous issue date: 2020-05-13","Embargo set by: Seth Robbins for item 115807 Lift date: 2022-08-26T23:58:55Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Semantic pattern discovery in open information extraction"]}]}],"canonical_facts":{"dc:contributor":["Han, Jiawei"],"dc:creator":["Chauhan, Aabhas"],"dc:date":["2020-08-26T23:58:49Z","2022-08-26T23:58:55Z","2020-05-13","2020-05"],"dc:description":["Open information extraction (OpenIE) is a novel paradigm that produces structured information from unstructured text with minimum or no supervision. The task involves extracting relevant relation tuples or expressions from a text corpus. Existing methods in the domain tend to produce a large percentage of ill-structured, incomplete or redundant extractions which cannot be directly used in downstream applications, and often fail on sentences with long and complex structures. In this paper, we propose a novel semantic pattern-discovery for OpenIE (SemPatIE) framework which extracts relations in the form of typed textual pattern structures, called meta patterns and groups semantically similar pattern structures. To perform these tasks, the framework uses three techniques: (1) it simplifies complex sentence structures by performing a context-aware sentence segmentation method which splits the dependency graph of sentences at noun or verb level and enables pattern extraction between distantly placed entities; (2) it extracts meta patterns and handles its pattern sparsity problem by introducing a novel idea of iterative frequent pattern mining and nested push-ups; (3) it generates semantic pattern clusters by embedding a multi text-based network between entities, entity types, extracted meta patterns and context words. Experiments show SemPatIE outperforms state-of-the-art OpenIE baselines in handling structurally complex sentences and has a significantly higher recall than existing pattern-based methods. Case studies exhibit the framework's high generalization ability and scalabilty, and effective clustering performance which has direct applications in downstream tasks like knowledge graph construction, evidence mining and truth finding.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Aabhas Chauhan, accepted the attached license on 2020-05-12 at 17:23.","The student, Aabhas Chauhan, submitted this Thesis for approval on 2020-05-12 at 17:30.","This Thesis was approved for publication on 2020-05-13 at 14:47.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15369 on 2020-08-25 at 17:31:19","Made available in DSpace on 2020-08-26T23:58:49Z (GMT). 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