{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T22:53:43Z","timestamp":1757631223539,"version":"3.44.0"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>\n            This demo paper presents KGNet, a graph machine learning-enabled RDF engine. KGNet integrates graph machine learning (GML) models with existing RDF engines as query operators to support node classification and link prediction tasks. For easy integration, KGNet extends the SPARQL language with user-defined predicates to support the GML operators. We refer to this extension as SPARQL\n            <jats:sup>\n              <jats:italic toggle=\"yes\">ML<\/jats:italic>\n            <\/jats:sup>\n            query. Our SPARQL\n            <jats:sup>\n              <jats:italic toggle=\"yes\">ML<\/jats:italic>\n            <\/jats:sup>\n            query optimizer is in charge of optimizing the selection of the near-optimal GML models. The development of KGNet poses research opportunities in various areas spanning KG management. In the paper, we demonstrate the ease of integration between the RDF engines and GML models through the SPARQL\n            <jats:sup>\n              <jats:italic toggle=\"yes\">ML<\/jats:italic>\n            <\/jats:sup>\n            inference query language. We present several real use cases of different GML tasks on real KGs. Using KGNet, users do not need to learn a new scripting language or have a deep understanding of GML methods. The audience will experience KGNet with different KGs and GML models, as shown in our demo video and Colab notebook.\n          <\/jats:p>","DOI":"10.14778\/3611540.3611599","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:32:37Z","timestamp":1694777557000},"page":"3974-3977","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Demonstration of SPARQL\n            <sup>\n              <i>ML<\/i>\n            <\/sup>\n            : An Interfacing Language for Supporting Graph Machine Learning for RDF Graphs"],"prefix":"10.14778","volume":"16","author":[{"given":"Hussein","family":"Abdallah","sequence":"first","affiliation":[{"name":"Concordia University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Waleed","family":"Afandi","sequence":"additional","affiliation":[{"name":"Concordia University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Essam","family":"Mansour","sequence":"additional","affiliation":[{"name":"Concordia University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_1_1_1","DOI":"10.1109\/ICDE55515.2023.00225"},{"doi-asserted-by":"publisher","unstructured":"Yingtong Dou Zhiwei Liu and et.al. 2020. Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters. In (CIKM). 315--324. 10.1145\/3340531.3411903","key":"e_1_2_1_2_1","DOI":"10.1145\/3340531.3411903"},{"key":"e_1_2_1_3_1","volume-title":"The MADlib Analytics Library or MAD Skills, the SQL. ArXiv abs\/1208.4165","author":"Hellerstein Joseph M.","year":"2012","unstructured":"Joseph M. Hellerstein, Christopher R\u00e9, and et.al. 2012. The MADlib Analytics Library or MAD Skills, the SQL. ArXiv abs\/1208.4165 (2012)."},{"doi-asserted-by":"publisher","unstructured":"Aidan Hogan Eva Blomqvist and et.al. 2021. Knowledge Graphs. 54 4 Article 71 (July 2021) 37 pages. 10.1145\/3447772","key":"e_1_2_1_4_1","DOI":"10.1145\/3447772"},{"volume-title":"Web Engineering","author":"Li Ziyu","unstructured":"Ziyu Li and et.al. 2023. Optimizing ML Inference Queries Under Constraints. In Web Engineering. Springer Nature Switzerland, Cham, 51--66.","key":"e_1_2_1_5_1"},{"doi-asserted-by":"publisher","unstructured":"Qiuru Lin Sai Wu and et.al. 2022. A Comparative Study of in-Database Inference Approaches. In (ICDE). 1794--1807. 10.1109\/ICDE53745.00180","key":"e_1_2_1_6_1","DOI":"10.1109\/ICDE53745.00180"},{"doi-asserted-by":"publisher","key":"e_1_2_1_7_1","DOI":"10.24963\/380"},{"doi-asserted-by":"publisher","key":"e_1_2_1_8_1","DOI":"10.1145\/2949741.2949748"},{"doi-asserted-by":"publisher","key":"e_1_2_1_9_1","DOI":"10.1145\/3535101"},{"key":"e_1_2_1_10_1","first-page":"1096","article-title":"Database Meets Artificial Intelligence: A Survey","volume":"34","author":"Zhou Xuanhe","year":"2022","unstructured":"Xuanhe Zhou, Chengliang Chai, Guoliang Li, and Ji Sun. 2022. Database Meets Artificial Intelligence: A Survey. IEEE TKDE 34, 3 (2022), 1096--1116.","journal-title":"IEEE TKDE"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3611540.3611599","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T22:36:23Z","timestamp":1757543783000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3611540.3611599"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":10,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["10.14778\/3611540.3611599"],"URL":"https:\/\/doi.org\/10.14778\/3611540.3611599","relation":{},"ISSN":["2150-8097"],"issn-type":[{"type":"print","value":"2150-8097"}],"subject":[],"published":{"date-parts":[[2023,8]]},"assertion":[{"value":"2023-08-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}