{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/rdf2vec-rdf-graph-embeddings-and-their","title":"RDF2Vec: RDF Graph Embeddings and Their Applications","arxiv_id":null,"date":"2017-11-10","proceeding":"Semantic Web Journal 2017 11","authors":["Petar Ristoski","Jessica Rosati","Tommaso Di Noia","Renato De Leone","Heiko Paulheim"],"abstract":"Linked Open Data has been recognized as a valuable source for background information in many data mining and information retrieval tasks. However, most of the existing tools require features in propositional form, i.e., a vector of nominal or numerical features associated with an instance, while Linked Open Data sources are graphs by nature. In this paper, we present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs.We generate sequences by leveraging local information from graph sub-structures, harvested by Weisfeiler-Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs.We evaluate our approach on three different tasks: (i) standard machine learning tasks, (ii) entity and document modeling, and (iii) content-based recommender systems. The evaluation shows that the proposed entity embeddings outperform existing techniques, and that pre-computed feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks.","url_abs":"http://www.semantic-web-journal.net/content/rdf2vec-rdf-graph-embeddings-and-their-applications-1","url_pdf":"http://www.semantic-web-journal.net/system/files/swj1738.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"rdf2vec-rdf-graph-embeddings-and-their","repo_url":"https://github.com/IBCNServices/pyRDF2Vec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"entity-embeddings","task_name":"Entity Embeddings"},{"task_slug":"general-knowledge","task_name":"General Knowledge"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"knowledge-graph-embedding","task_name":"Knowledge Graph Embedding"},{"task_slug":"knowledge-graph-embeddings","task_name":"Knowledge Graph Embeddings"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"node-classification","task_name":"Node Classification"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[{"method_slug":"rdf2vec","method_name":"RDF2Vec"}],"datasets_introduced":[],"methods_introduced":[{"slug":"rdf2vec","name":"RDF2Vec","full_name":"RDF2Vec"}],"results":[{"leaderboard":"/sota/node-classification-on-aifb","task":"Node Classification","dataset":"AIFB","model":"RDF2Vec+SVM","rank_in_archive_order":6,"of":7,"metrics":{"Accuracy":"88.88"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-am","task":"Node Classification","dataset":"AM","model":"RDF2Vec+SVM","rank_in_archive_order":5,"of":8,"metrics":{"Accuracy":"88.33"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-bgs","task":"Node Classification","dataset":"BGS","model":"RDF2Vec+SVM","rank_in_archive_order":3,"of":7,"metrics":{"Accuracy":"87.24"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-mutag","task":"Node Classification","dataset":"MUTAG","model":"RDF2Vec+SVM","rank_in_archive_order":6,"of":6,"metrics":{"Accuracy":"67.20"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}