{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/95610"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/95610","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Entity-relation search: context pattern driven extraction and indexing","abstract":"Our research focuses on searching relations between entities with context constraints. In particular, we are interested in efficiently searching for the relations among medical entities (e.g. diseases, chemicals, species, genes, or mutations) in a professional medical corpus. Existing relation extraction systems, like OpenIE, are able to extract some relations between entities. However, its results are inseparable in terms of extraction contexts, which prevents it from being able to search for the relations of given contexts. To address this issue, we propose to build an entity-relation search system with an awareness of extraction contexts. In order to achieve this goal, we propose to extract and index contexts for each extracted relation. We evaluate our search model over millions of professional medical abstracts and show that our context indexing is effective to support the task of searching relations into contexts. Note that this rich and novel system is the product of a collaborative team effort: Tianxiao Zhang, Jiarui Xu and Varun Berry, and supervised by Professor Kevin Chang. While we separately document our individual contributions, we intentionally share some parts of our thesis to improve the readability of our overall system design. This thesis mainly focuses on the design of our context extraction and indexing method.","abstract_html":"Our research focuses on searching relations between entities with context constraints. In particular, we are interested in efficiently searching for the relations among medical entities (e.g. diseases, chemicals, species, genes, or mutations) in a professional medical corpus. Existing relation extraction systems, like OpenIE, are able to extract some relations between entities. However, its results are inseparable in terms of extraction contexts, which prevents it from being able to search for the relations of given contexts. To address this issue, we propose to build an entity-relation search system with an awareness of extraction contexts. In order to achieve this goal, we propose to extract and index contexts for each extracted relation. We evaluate our search model over millions of professional medical abstracts and show that our context indexing is effective to support the task of searching relations into contexts. Note that this rich and novel system is the product of a collaborative team effort: Tianxiao Zhang, Jiarui Xu and Varun Berry, and supervised by Professor Kevin Chang. While we separately document our individual contributions, we intentionally share some parts of our thesis to improve the readability of our overall system design. This thesis mainly focuses on the design of our context extraction and indexing method.","abstract_has_math":false,"creators":["Zhang, Zequn"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Chang, Kevin C."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-03-01T17:02:02Z","date_published":"2017-03-01T17:02:02Z","updated_at":"2026-07-22T22:26:37Z","subjects":["Relation search","Relation indexing"],"languages":["en"],"rights":["Copyright 2016 Zequn Zhang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/95610","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chang, Kevin C."]},{"key":"dc:creator","label":"Author","values":["Zhang, Zequn"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-03-01T17:02:02Z","2019-03-02T10:15:24Z","2016-12-05","2016-12"]},{"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":["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":["Relation search","Relation indexing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Zequn Zhang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/95610"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Our research focuses on searching relations between entities with context constraints. In particular, we are interested in efficiently searching for the relations among medical entities (e.g. diseases, chemicals, species, genes, or mutations) in a professional medical corpus. Existing relation extraction systems, like OpenIE, are able to extract some relations between entities. However, its results are inseparable in terms of extraction contexts, which prevents it from being able to search for the relations of given contexts. To address this issue, we propose to build an entity-relation search system with an awareness of extraction contexts. In order to achieve this goal, we propose to extract and index contexts for each extracted relation. We evaluate our search model over millions of professional medical abstracts and show that our context indexing is effective to support the task of searching relations into contexts. Note that this rich and novel system is the product of a collaborative team effort: Tianxiao Zhang, Jiarui Xu and Varun Berry, and supervised by Professor Kevin Chang. While we separately document our individual contributions, we intentionally share some parts of our thesis to improve the readability of our overall system design. This thesis mainly focuses on the design of our context extraction and indexing method.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-12-01","The student, Zequn Zhang, accepted the attached license on 2016-12-02 at 14:07.","The student, Zequn Zhang, submitted this Thesis for approval on 2016-12-02 at 14:13.","This Thesis was approved for publication on 2016-12-05 at 13:33.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10421 on 2017-02-28 at 14:43:12","Made available in DSpace on 2017-03-01T17:02:02Z (GMT). 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In particular, we are interested in efficiently searching for the relations among medical entities (e.g. diseases, chemicals, species, genes, or mutations) in a professional medical corpus. Existing relation extraction systems, like OpenIE, are able to extract some relations between entities. However, its results are inseparable in terms of extraction contexts, which prevents it from being able to search for the relations of given contexts. To address this issue, we propose to build an entity-relation search system with an awareness of extraction contexts. In order to achieve this goal, we propose to extract and index contexts for each extracted relation. We evaluate our search model over millions of professional medical abstracts and show that our context indexing is effective to support the task of searching relations into contexts. Note that this rich and novel system is the product of a collaborative team effort: Tianxiao Zhang, Jiarui Xu and Varun Berry, and supervised by Professor Kevin Chang. While we separately document our individual contributions, we intentionally share some parts of our thesis to improve the readability of our overall system design. This thesis mainly focuses on the design of our context extraction and indexing method.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-12-01","The student, Zequn Zhang, accepted the attached license on 2016-12-02 at 14:07.","The student, Zequn Zhang, submitted this Thesis for approval on 2016-12-02 at 14:13.","This Thesis was approved for publication on 2016-12-05 at 13:33.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10421 on 2017-02-28 at 14:43:12","Made available in DSpace on 2017-03-01T17:02:02Z (GMT). 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