{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117646"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117646","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"ABM: attention-based message passing network for knowledge graph completion","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","abstract_has_math":false,"creators":["Xu, Weikai"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Tong, Hanghang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Knowledge Graph Completion","Attention","Message Passing"],"languages":["en","eng"],"rights":["Copyright 2022 Weikai Xu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117646","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Tong, Hanghang"]},{"key":"dc:creator","label":"Author","values":["Xu, Weikai"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-11-21"]},{"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":["Knowledge Graph Completion","Attention","Message Passing"]}]},{"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 2022 Weikai Xu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117646"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","The student, Weikai Xu, accepted the attached license on 2022-11-18 at 01:36.","The student, Weikai Xu, submitted this Thesis for approval on 2022-11-18 at 01:40.","This Thesis was approved for publication on 2022-11-21 at 09:34.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18566 on 2023-04-12 at 08:10:49","Knowledge graphs are ubiquitous and play an important role in many real-world applications, including recommender systems, question-answering, fact-checking, and so on. However, most of the knowledge graphs are incomplete which can hamper their practical usage. Fortunately, knowledge graph completion (KGC) can mitigate this problem by inferring missing edges in the knowledge graph according to the existing information. In this thesis, we propose a novel KGC method named Attention-Based Message passing (ABM) which focuses on predicting the relation between any two entities in a knowledge graph. The proposed ABM consists of three integral parts, including (1) context embedding, (2) structure embedding, and (3) path embedding. In the context embedding, the proposed ABM generalizes the existing message passing neural network to update the node embedding and the edge embedding to assimilate the knowledge of nodes’ neighbors, which captures the relative role information of the edge that we want to predict. In the structure embedding, the proposed method overcomes the shortcomings of the existing Graph Neural Network (GNN) method (i.e., most methods ignore the structural similarity between nodes.) by assigning different attention weights to different nodes while conducting the aggregation. Path embedding generates paths between any two entities and treats these paths as sequences. Then, the sequence can be used as the input of the Transformer to update the embedding of the knowledge graph to gather the global role of the missing edges. By utilizing these three mutually complementary strategies, the proposed ABM is able to capture both local and global information which in turn leads to a superb performance. Experiment results show that ABM outperforms baseline methods on a wide range of datasets."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["ABM: attention-based message passing network for knowledge graph completion"]}]}],"canonical_facts":{"dc:contributor":["Tong, Hanghang"],"dc:creator":["Xu, Weikai"],"dc:date":["2022-12","2022-11-21"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","The student, Weikai Xu, accepted the attached license on 2022-11-18 at 01:36.","The student, Weikai Xu, submitted this Thesis for approval on 2022-11-18 at 01:40.","This Thesis was approved for publication on 2022-11-21 at 09:34.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18566 on 2023-04-12 at 08:10:49","Knowledge graphs are ubiquitous and play an important role in many real-world applications, including recommender systems, question-answering, fact-checking, and so on. However, most of the knowledge graphs are incomplete which can hamper their practical usage. Fortunately, knowledge graph completion (KGC) can mitigate this problem by inferring missing edges in the knowledge graph according to the existing information. In this thesis, we propose a novel KGC method named Attention-Based Message passing (ABM) which focuses on predicting the relation between any two entities in a knowledge graph. The proposed ABM consists of three integral parts, including (1) context embedding, (2) structure embedding, and (3) path embedding. In the context embedding, the proposed ABM generalizes the existing message passing neural network to update the node embedding and the edge embedding to assimilate the knowledge of nodes’ neighbors, which captures the relative role information of the edge that we want to predict. In the structure embedding, the proposed method overcomes the shortcomings of the existing Graph Neural Network (GNN) method (i.e., most methods ignore the structural similarity between nodes.) by assigning different attention weights to different nodes while conducting the aggregation. Path embedding generates paths between any two entities and treats these paths as sequences. Then, the sequence can be used as the input of the Transformer to update the embedding of the knowledge graph to gather the global role of the missing edges. By utilizing these three mutually complementary strategies, the proposed ABM is able to capture both local and global information which in turn leads to a superb performance. Experiment results show that ABM outperforms baseline methods on a wide range of datasets."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117646"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Weikai Xu"],"dc:subject":["Knowledge Graph Completion","Attention","Message Passing"],"dc:title":["ABM: attention-based message passing network for knowledge graph completion"],"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"}