{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/97387"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/97387","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A schema conversion approach for constructing heterogeneous information networks from documents","abstract":"Information networks with multi-typed nodes and edges with different semantics are called heterogenous information networks. Since heterogeneous information networks embed more complex information than homogeneous information networks due to their multi-typed nodes and edges, mining such networks has produced richer knowledge and insights. To extend the application of heterogeneous information network analysis to document analysis, it is necessary to build information networks from a collection of documents while preserving important information in the documents. This thesis describes a schema conversion approach to apply data mining techniques on the outcomes of natural language processing (NLP) tools to construct heterogeneous information networks. First, we utilize named entity recognition (NER) tools to explore networks over entities, topics, and words to demonstrate how a probabilistic model can convert the data schema of the NER tools. Second, we address a pat- tern mining method to construct a network with authors, documents, and writing styles by extracting discriminative writing styles from parse trees and converting them into nodes in a network. Third, we introduce a clustering method to merge redundant nodes in an information network with documents, claims, subjective, objective, and verbs. We use a semantic role labeling (SRL) tool to get initial network structures from news articles, and merge duplicated nodes using a similarity measure SynRank. Finally, we present a novel event mining framework for extracting high-quality structured event knowledge from large, redundant, and noisy news data. The proposed framework ProxiModel utilizes named entity recognition, time expression extraction, and phrase mining tools to get event information from documents.","abstract_html":"Information networks with multi-typed nodes and edges with different semantics are called heterogenous information networks. Since heterogeneous information networks embed more complex information than homogeneous information networks due to their multi-typed nodes and edges, mining such networks has produced richer knowledge and insights. To extend the application of heterogeneous information network analysis to document analysis, it is necessary to build information networks from a collection of documents while preserving important information in the documents. This thesis describes a schema conversion approach to apply data mining techniques on the outcomes of natural language processing (NLP) tools to construct heterogeneous information networks. First, we utilize named entity recognition (NER) tools to explore networks over entities, topics, and words to demonstrate how a probabilistic model can convert the data schema of the NER tools. Second, we address a pat- tern mining method to construct a network with authors, documents, and writing styles by extracting discriminative writing styles from parse trees and converting them into nodes in a network. Third, we introduce a clustering method to merge redundant nodes in an information network with documents, claims, subjective, objective, and verbs. We use a semantic role labeling (SRL) tool to get initial network structures from news articles, and merge duplicated nodes using a similarity measure SynRank. Finally, we present a novel event mining framework for extracting high-quality structured event knowledge from large, redundant, and noisy news data. The proposed framework ProxiModel utilizes named entity recognition, time expression extraction, and phrase mining tools to get event information from documents.","abstract_has_math":false,"creators":["Kim, Hyung Sul"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Han, Jiawei","Hockenmaier, Julia","Zhai, ChengXiang","Dmitriev, Pavel"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-10T19:15:19Z","date_published":"2017-08-10T19:15:19Z","updated_at":"2026-07-22T22:24:34Z","subjects":["Information network construction"],"languages":["en"],"rights":["Copyright 2017 Hyung Sul Kim"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/97387","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei","Hockenmaier, Julia","Zhai, ChengXiang","Dmitriev, Pavel"]},{"key":"dc:creator","label":"Author","values":["Kim, Hyung Sul"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-08-10T19:15:19Z","2017-04-19","2017-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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 network construction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Hyung Sul Kim"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/97387"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Information networks with multi-typed nodes and edges with different semantics are called heterogenous information networks. Since heterogeneous information networks embed more complex information than homogeneous information networks due to their multi-typed nodes and edges, mining such networks has produced richer knowledge and insights. To extend the application of heterogeneous information network analysis to document analysis, it is necessary to build information networks from a collection of documents while preserving important information in the documents. This thesis describes a schema conversion approach to apply data mining techniques on the outcomes of natural language processing (NLP) tools to construct heterogeneous information networks. First, we utilize named entity recognition (NER) tools to explore networks over entities, topics, and words to demonstrate how a probabilistic model can convert the data schema of the NER tools. Second, we address a pat- tern mining method to construct a network with authors, documents, and writing styles by extracting discriminative writing styles from parse trees and converting them into nodes in a network. Third, we introduce a clustering method to merge redundant nodes in an information network with documents, claims, subjective, objective, and verbs. We use a semantic role labeling (SRL) tool to get initial network structures from news articles, and merge duplicated nodes using a similarity measure SynRank. Finally, we present a novel event mining framework for extracting high-quality structured event knowledge from large, redundant, and noisy news data. The proposed framework ProxiModel utilizes named entity recognition, time expression extraction, and phrase mining tools to get event information from documents.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Hyung Sul Kim, accepted the attached license on 2017-04-17 at 18:24.","The student, Hyung Sul Kim, submitted this Dissertation for approval on 2017-04-17 at 18:30.","This Dissertation was approved for publication on 2017-04-19 at 18:19.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10829 on 2017-08-10 at 13:41:35","Made available in DSpace on 2017-08-10T19:15:19Z (GMT). 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To extend the application of heterogeneous information network analysis to document analysis, it is necessary to build information networks from a collection of documents while preserving important information in the documents. This thesis describes a schema conversion approach to apply data mining techniques on the outcomes of natural language processing (NLP) tools to construct heterogeneous information networks. First, we utilize named entity recognition (NER) tools to explore networks over entities, topics, and words to demonstrate how a probabilistic model can convert the data schema of the NER tools. Second, we address a pat- tern mining method to construct a network with authors, documents, and writing styles by extracting discriminative writing styles from parse trees and converting them into nodes in a network. Third, we introduce a clustering method to merge redundant nodes in an information network with documents, claims, subjective, objective, and verbs. We use a semantic role labeling (SRL) tool to get initial network structures from news articles, and merge duplicated nodes using a similarity measure SynRank. Finally, we present a novel event mining framework for extracting high-quality structured event knowledge from large, redundant, and noisy news data. The proposed framework ProxiModel utilizes named entity recognition, time expression extraction, and phrase mining tools to get event information from documents.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Hyung Sul Kim, accepted the attached license on 2017-04-17 at 18:24.","The student, Hyung Sul Kim, submitted this Dissertation for approval on 2017-04-17 at 18:30.","This Dissertation was approved for publication on 2017-04-19 at 18:19.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10829 on 2017-08-10 at 13:41:35","Made available in DSpace on 2017-08-10T19:15:19Z (GMT). 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