{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90846"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90846","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 relation ranking","abstract":"A traditional page link-based search system is not adequate for users intending to query data efficiently. For instance, emergent phenomena reveal that some entity-based search engines, such as EntityRank, directly return answers (target entities) to users instead of web pages. Most of the time, however, compared to searching for interested entities, users more often focus on relationships among entities. To our knowledge, there is only one web search system that automatically extracts relations from massive unstructured corpora. This system is referred to as OpenIE, which indeed brings us one step closer to an entity relation-based system. Nevertheless, its system extracts only direct relations between a pair of entities and ranks simply by occurrence frequency. The monotone pattern extraction, adopted in their relation phrase extraction model, provides high quality entity relations but also fail to return many potential true relations in the corpus, which has been explained in Section 4.2 and affects recall significantly shown in 5.3. In addition, it is difficult for users to retrieve their interested and true relations from massive relation candidate set without a qualified ranking model. Consequently, there still exists a gap between the system and users for retrieving entity relations efficiently by a simple query. To assist users to find their interested relations efficiently, this thesis specifically focuses on the core challenges of the ranking model. Naturally, the quality of each relation candidate is largely relevant to its context. Thus, to evaluate various conditions, a novel idea of context patterns driven ranking has been introduced. After evaluating our online prototype on millions of PubMed medical abstracts, we show that our system performs better than the OpenIE system on both precision and recall. Note that this rich and novel system is the product of a collaborative team effort comprised of the following members: Zequn Zhang, Jiarui Xu, and Varun Berry, and supervised by Professor Kevin Chang.","abstract_html":"A traditional page link-based search system is not adequate for users intending to query data efficiently. For instance, emergent phenomena reveal that some entity-based search engines, such as EntityRank, directly return answers (target entities) to users instead of web pages. Most of the time, however, compared to searching for interested entities, users more often focus on relationships among entities. To our knowledge, there is only one web search system that automatically extracts relations from massive unstructured corpora. This system is referred to as OpenIE, which indeed brings us one step closer to an entity relation-based system. Nevertheless, its system extracts only direct relations between a pair of entities and ranks simply by occurrence frequency. The monotone pattern extraction, adopted in their relation phrase extraction model, provides high quality entity relations but also fail to return many potential true relations in the corpus, which has been explained in Section 4.2 and affects recall significantly shown in 5.3. In addition, it is difficult for users to retrieve their interested and true relations from massive relation candidate set without a qualified ranking model. Consequently, there still exists a gap between the system and users for retrieving entity relations efficiently by a simple query. To assist users to find their interested relations efficiently, this thesis specifically focuses on the core challenges of the ranking model. Naturally, the quality of each relation candidate is largely relevant to its context. Thus, to evaluate various conditions, a novel idea of context patterns driven ranking has been introduced. After evaluating our online prototype on millions of PubMed medical abstracts, we show that our system performs better than the OpenIE system on both precision and recall. Note that this rich and novel system is the product of a collaborative team effort comprised of the following members: Zequn Zhang, Jiarui Xu, and Varun Berry, and supervised by Professor Kevin Chang.","abstract_has_math":false,"creators":["Zhang, Tianxiao"],"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":2016,"date_issued":"2016-07-07T20:35:25Z","date_published":"2016-07-07T20:35:25Z","updated_at":"2026-07-22T22:26:34Z","subjects":["Entity Relation Search","Context Pattern","PubMed Data"],"languages":["en"],"rights":["Copyright 2016 Tianxiao Zhang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90846","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, Tianxiao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-07-07T20:35:25Z","2018-07-08T09:15:36Z","2016-04-28","2016-05"]},{"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":["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":["Entity Relation Search","Context Pattern","PubMed Data"]}]},{"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 Tianxiao Zhang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90846"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A traditional page link-based search system is not adequate for users intending to query data efficiently. 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In addition, it is difficult for users to retrieve their interested and true relations from massive relation candidate set without a qualified ranking model. Consequently, there still exists a gap between the system and users for retrieving entity relations efficiently by a simple query. To assist users to find their interested relations efficiently, this thesis specifically focuses on the core challenges of the ranking model. Naturally, the quality of each relation candidate is largely relevant to its context. Thus, to evaluate various conditions, a novel idea of context patterns driven ranking has been introduced. After evaluating our online prototype on millions of PubMed medical abstracts, we show that our system performs better than the OpenIE system on both precision and recall. Note that this rich and novel system is the product of a collaborative team effort comprised of the following members: Zequn Zhang, Jiarui Xu, and Varun Berry, and supervised by Professor Kevin Chang.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Tianxiao Zhang, accepted the attached license on 2016-04-27 at 15:44.","The student, Tianxiao Zhang, submitted this Thesis for approval on 2016-04-27 at 16:15.","This Thesis was approved for publication on 2016-04-28 at 13:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9557 on 2016-07-07 at 13:51:07","Made available in DSpace on 2016-07-07T20:35:25Z (GMT). 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In addition, it is difficult for users to retrieve their interested and true relations from massive relation candidate set without a qualified ranking model. Consequently, there still exists a gap between the system and users for retrieving entity relations efficiently by a simple query. To assist users to find their interested relations efficiently, this thesis specifically focuses on the core challenges of the ranking model. Naturally, the quality of each relation candidate is largely relevant to its context. Thus, to evaluate various conditions, a novel idea of context patterns driven ranking has been introduced. After evaluating our online prototype on millions of PubMed medical abstracts, we show that our system performs better than the OpenIE system on both precision and recall. Note that this rich and novel system is the product of a collaborative team effort comprised of the following members: Zequn Zhang, Jiarui Xu, and Varun Berry, and supervised by Professor Kevin Chang.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Tianxiao Zhang, accepted the attached license on 2016-04-27 at 15:44.","The student, Tianxiao Zhang, submitted this Thesis for approval on 2016-04-27 at 16:15.","This Thesis was approved for publication on 2016-04-28 at 13:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9557 on 2016-07-07 at 13:51:07","Made available in DSpace on 2016-07-07T20:35:25Z (GMT). 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