{"doi":"10.6084/m9.figshare.7356416.v2","title":"Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings","abstract":"Recent developments in machine learning have led to a rise of largenumber of methods for extracting features from structured data. The featuresare represented as vectors and may encode for some semantic aspects of data.They can be used in a machine learning models for different tasks or to com-pute similarities between the entities of the data. SPARQL is a query languagefor structured data originally developed for querying Resource Description Frame-work (RDF) data. It has been in use for over a decade as a standardized NoSQLquery language. Many different tools have been developed to enable data shar-ing with SPARQL. For example, SPARQL endpoints make your data interopera-ble and available to the world. SPARQL queries can be executed across multi-ple endpoints. We have developed a Vec2SPARQL, which is a general frame-work for integrating structured data and their vector space representations.Vec2SPARQL allows jointly querying vector functions such as computing sim-ilarities (cosine, correlations) or classifications with machine learning modelswithin a single SPARQL query. We demonstrate applications of our approachfor biomedical and clinical use cases. Our source code is freely available athttps://github.com/bio-ontology-research-group/vec2sparql and we make aVec2SPARQL endpoint available at http://sparql.bio2vec.net/","journal":"OPAL (Open@LaTrobe) (La Trobe University)","year":2018,"id":7759,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0413,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2018-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":70375,"name":"Şenay Kafkas","orcid":"0000-0001-7509-5786","position":1,"is_corresponding":false},{"id":70376,"name":"Andreas Karwath","orcid":"0000-0002-6942-3760","position":2,"is_corresponding":false},{"id":6530,"name":"Alexander Malic","orcid":"0000-0002-8394-3439","position":3,"is_corresponding":false},{"id":12968,"name":"Georgios V. Gkoutos","orcid":"0000-0002-2061-091X","position":4,"is_corresponding":false},{"id":74,"name":"Michel J. Dumontier","orcid":"0000-0003-4727-9435","position":5,"is_corresponding":false},{"id":12963,"name":"Robert Hoehndorf","orcid":"0000-0001-8149-5890","position":6,"is_corresponding":false},{"id":70374,"name":"Maxat Kulmanov","orcid":"0000-0003-1710-1820","position":0,"is_corresponding":false}],"reference_count":32,"raw_metadata":null,"created_at":"2026-03-01T18:20:47.508186Z","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":[]}