{"doi":"10.1145/3106426.3106447","title":"Efficient parallel translating embedding for knowledge graphs","abstract":null,"journal":"Proceedings of the International Conference on Web Intelligence","year":2017,"id":588586,"datarank":1.0973846373002436,"base_score":2.9444389791664403,"endowment":2.9444389791664403,"self_citation_contribution":0.44166584687496613,"citation_network_contribution":0.6557187904252775,"self_endowment_contribution":0.44166584687496613,"citer_contribution":0.6557187904252775,"corpus_percentile":null,"corpus_rank":null,"citation_count":18,"citer_count":15,"citers_with_citation_signal":11,"citers_with_endowment":11,"datacite_reuse_total":1,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1155796,"name":"Manling Li","orcid":"0000-0002-5543-3343","position":1,"is_corresponding":false},{"id":1505809,"name":"Yantao Jia","orcid":null,"position":2,"is_corresponding":false},{"id":953300,"name":"Yuanzhuo Wang","orcid":"0000-0002-1940-9741","position":3,"is_corresponding":false},{"id":1505810,"name":"Xueqi Cheng","orcid":null,"position":4,"is_corresponding":false},{"id":1505808,"name":"Denghui Zhang","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Efficient parallel translating embedding for knowledge graphs","abstract":"Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the-art results among knowledge graph embedding methods. However, a major limitation of these methods is the time consuming training process, which may take several days or even weeks for large knowledge graphs, and result in great difficulty in practical applications. In this paper, we propose an efficient parallel framework for translating embedding methods, called ParTrans-X, which enables the methods to be paralleled without locks by utilizing the distinguished structures of knowledge graphs. Experiments on two datasets with three typical translating embedding methods, i.e., TransE [3], TransH [19], and a more efficient variant TransE- AdaGrad [11] validate that ParTrans-X can speed up the training process by more than an order of magnitude.","is_dataset_classified":null,"base_score":2.9444389791664403,"endowment":2.9444389791664403,"datacite_reuse_total":1,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"20725694","pmcid":null,"openalex_id":"https://openalex.org/W2604454537","authors":[],"funders":[{"funder_name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences","grant_id":"2014431, 2016102","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"61572469, 61402442, 91646120,61572473, 61402022","title":null},{"funder_name":"National Grand Fundamental Research 973 Program of China","grant_id":"2013CB329602, 2014CB340401","title":null},{"funder_name":"National Key R&D Program of China","grant_id":"2016QY02D0405, 2016YFB1000902","title":null},{"funder_name":"Key Research Program of the CAS","grant_id":"KGZD-EW-T03-2","title":null}],"total_grants":5,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2017,"count":1},{"year":2019,"count":1},{"year":2020,"count":3},{"year":2021,"count":5},{"year":2022,"count":4},{"year":2023,"count":3},{"year":2024,"count":1}],"oa_status":"green","license":"https://www.acm.org/publications/policies/copyright_policy#Background","oa_locations":[{"url":"https://arxiv.org/pdf/1703.10316","host_type":"repository"},{"url":"https://arxiv.org/pdf/1703.10316","host_type":"repository"},{"url":"https://dl.acm.org/doi/10.1145/3106426.3106447","host_type":"publisher"},{"url":"https://dl.acm.org/doi/pdf/10.1145/3106426.3106447","host_type":"publisher"},{"url":"http://arxiv.org/abs/1703.10316","host_type":"repository"},{"url":"https://doi.org/10.1145/3106426.3106447","host_type":"conference"}],"fields_of_study":["Advanced Graph Neural Networks","Privacy-Preserving Technologies in Data","Topic Modeling"],"mesh_terms":[],"keywords":["Embedding","Knowledge graph","Computer science","Theoretical computer science","Graph","Graph embedding","Process (computing)","Artificial intelligence"],"sdg_mappings":[],"linked_datasets":[{"doi":"10.48550/arxiv.1703.10316","title":"Efficient Parallel Translating Embedding For Knowledge Graphs","publisher":"arXiv","resource_type":"Text"}],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-22T00:43:10.974108Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}