{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120112"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120112","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Descriptive knowledge graph for explaining entity relationships","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_has_math":false,"creators":["Zhu, Kerui"],"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 Chen-Chuan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:56Z","subjects":["Descriptive Knowledge Graph","Relation Description"],"languages":["en","eng"],"rights":["Copyright 2023 Kerui Zhu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120112","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chang, Kevin Chen-Chuan"]},{"key":"dc:creator","label":"Author","values":["Zhu, Kerui"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-04-27"]},{"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":["Descriptive Knowledge Graph","Relation Description"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Kerui Zhu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120112"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Kerui Zhu, accepted the attached license on 2023-04-25 at 23:41.","The student, Kerui Zhu, submitted this Thesis for approval on 2023-04-25 at 23:56.","This Thesis was approved for publication on 2023-04-27 at 13:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19186 on 2023-09-01 at 16:55:35","We propose DEER (Descriptive Knowledge Graph for Explaining Entity Relationships) – an open and informative form of modeling entity relationships. In DEER, relationships between entities are represented by free-text relation descriptions. For instance, the relationship between entities of machine learning and algorithm can be represented as “Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.” To construct DEER, we propose a self-supervised learning method to extract relation descriptions with the analysis of dependency patterns and generate relation descriptions with a transformer-based relation description synthesizing model, where no human labeling is required. Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships. The results suggest that we can build an open and informative knowledge graph without human annotation. We also present a novel system that automates the extraction or generation of informative and descriptive sentences from biomedical corpus and builds a descriptive knowledge graph to facilitate efficient search for relational knowledge. In contrast to previous search engines or exploration systems that retrieve unconnected passages, our system organizes descriptive sentences into a graph, enabling researchers to explore relationships between entities. Our system also includes a relation synthesis model that generates concise descriptive sentences from retrieved sentences, reducing the need for human reading effort. With our system, researchers can quickly obtain a high-level overview of directly related entities to a query entity (e.g., diseases treated by a chemical) or indirect connections between two entities (e.g., candidate drugs for treating a disease). This information can guide literature surveys and facilitate the discovery of potential research topics. Our system also speeds up the literature curation and drug repurposing process. We demonstrate the effectiveness of our system on the CORD-19 dataset, but it can be deployed on any biomedical corpus without manual adaptation."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Descriptive knowledge graph for explaining entity relationships"]}]}],"canonical_facts":{"dc:contributor":["Chang, Kevin Chen-Chuan"],"dc:creator":["Zhu, Kerui"],"dc:date":["2023-05","2023-04-27"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Kerui Zhu, accepted the attached license on 2023-04-25 at 23:41.","The student, Kerui Zhu, submitted this Thesis for approval on 2023-04-25 at 23:56.","This Thesis was approved for publication on 2023-04-27 at 13:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19186 on 2023-09-01 at 16:55:35","We propose DEER (Descriptive Knowledge Graph for Explaining Entity Relationships) – an open and informative form of modeling entity relationships. In DEER, relationships between entities are represented by free-text relation descriptions. For instance, the relationship between entities of machine learning and algorithm can be represented as “Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.” To construct DEER, we propose a self-supervised learning method to extract relation descriptions with the analysis of dependency patterns and generate relation descriptions with a transformer-based relation description synthesizing model, where no human labeling is required. Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships. The results suggest that we can build an open and informative knowledge graph without human annotation. We also present a novel system that automates the extraction or generation of informative and descriptive sentences from biomedical corpus and builds a descriptive knowledge graph to facilitate efficient search for relational knowledge. In contrast to previous search engines or exploration systems that retrieve unconnected passages, our system organizes descriptive sentences into a graph, enabling researchers to explore relationships between entities. Our system also includes a relation synthesis model that generates concise descriptive sentences from retrieved sentences, reducing the need for human reading effort. With our system, researchers can quickly obtain a high-level overview of directly related entities to a query entity (e.g., diseases treated by a chemical) or indirect connections between two entities (e.g., candidate drugs for treating a disease). This information can guide literature surveys and facilitate the discovery of potential research topics. Our system also speeds up the literature curation and drug repurposing process. We demonstrate the effectiveness of our system on the CORD-19 dataset, but it can be deployed on any biomedical corpus without manual adaptation."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120112"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Kerui Zhu"],"dc:subject":["Descriptive Knowledge Graph","Relation Description"],"dc:title":["Descriptive knowledge graph for explaining entity relationships"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}