{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T19:04:20Z","timestamp":1789153460540,"version":"build-2803163510"},"reference-count":67,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Semantic Web: \u2013 Interoperability, Usability, Applicability"],"published-print":{"date-parts":[[2024,10,9]]},"abstract":"<jats:p>In a GraphQL Web API, a so-called GraphQL schema defines the types of data objects that can be queried, and so-called resolver functions are responsible for fetching the relevant data from underlying data sources. Thus, we can expect to use GraphQL not only for data access but also for data integration, if the GraphQL schema reflects the semantics of data from multiple data sources, and the resolver functions can obtain data from these data sources and structure the data according to the schema. However, there does not exist a semantics-aware approach to employ GraphQL for data integration. Furthermore, there are no formal methods for defining a GraphQL API based on an ontology. In this work, we introduce a framework for using GraphQL in which a global domain ontology informs the generation of a GraphQL server that answers requests by querying heterogeneous data sources. The core of this framework consists of an algorithm to generate a GraphQL schema based on an ontology and a generic resolver function based on semantic mappings. We provide a prototype, OBG-gen, of this framework, and we evaluate our approach over a real-world data integration scenario in the materials design domain and two synthetic benchmark scenarios (Link\u00f6ping GraphQL Benchmark and GTFS-Madrid-Bench). The experimental results of our evaluation indicate that: (i) our approach is feasible to generate GraphQL servers for data access and integration over heterogeneous data sources, thus avoiding a manual construction of GraphQL servers, and (ii) our data access and integration approach is general and applicable to different domains where data is shared or queried via different ways.<\/jats:p>","DOI":"10.3233\/sw-233550","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T12:13:14Z","timestamp":1704802394000},"page":"1639-1675","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Ontology-based GraphQL server generation for data access and data integration"],"prefix":"10.1177","volume":"15","author":[{"given":"Huanyu","family":"Li","sequence":"first","affiliation":[{"name":"Link\u00f6ping University","place":["Sweden"]},{"name":"Link\u00f6ping University","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olaf","family":"Hartig","sequence":"additional","affiliation":[{"name":"Link\u00f6ping University","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rickard","family":"Armiento","sequence":"additional","affiliation":[{"name":"Link\u00f6ping University","place":["Sweden"]},{"name":"Link\u00f6ping University","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick","family":"Lambrix","sequence":"additional","affiliation":[{"name":"Link\u00f6ping University","place":["Sweden"]},{"name":"Link\u00f6ping University","place":["Sweden"]},{"name":"University of G\u00e4vle","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1063\/1.4946894"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","unstructured":"A.\u00a0Alhazmi T.\u00a0Blount and G.\u00a0Konstantinidis ForBackBench: A benchmark for chasing vs. query-rewriting in: Proceedings of the VLDB Endowment\u00a0\u2013 48th International Conference on Very Large Data Bases Vol.\u00a015 Sydney Australia 2022 2022 pp.\u00a01519\u20131532. doi:10.14778\/3529337.3529338.","DOI":"10.14778\/3529337.3529338"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","unstructured":"C.W.\u00a0Andersen R.\u00a0Armiento E.\u00a0Blokhin G.J.\u00a0Conduit S.\u00a0Dwaraknath M.L.\u00a0Evans \u00c1.\u00a0Fekete A.\u00a0Gopakumar S.\u00a0Gra\u017eulis A.\u00a0Merkys F.\u00a0Mohamed C.\u00a0Oses G.\u00a0Pizzi G.-M.\u00a0Rignanese M.\u00a0Scheidgen L.\u00a0Talirz C.\u00a0Toher D.\u00a0Winston R.\u00a0Aversa K.\u00a0Choudhary P.\u00a0Colinet S.\u00a0Curtarolo D.\u00a0Di Stefano C.\u00a0Draxl S.\u00a0Er M.\u00a0Esters M.\u00a0Fornari M.\u00a0Giantomassi M.\u00a0Govoni G.\u00a0Hautier V.\u00a0Hegde M.K.\u00a0Horton P.\u00a0Huck G.\u00a0Huhs J.\u00a0Hummelsh\u00f8j A.\u00a0Kariryaa B.\u00a0Kozinsky S.\u00a0Kumbhar M.\u00a0Liu N.\u00a0Marzari A.J.\u00a0Morris A.A.\u00a0Mostofi K.A.\u00a0Persson G.\u00a0Petretto T.\u00a0Purcell F.\u00a0Ricci F.\u00a0Rose M.\u00a0Scheffler D.\u00a0Speckhard M.\u00a0Uhrin A.\u00a0Vaitkus P.\u00a0Villars D.\u00a0Waroquiers C.\u00a0Wolverton M.\u00a0Wu and X.\u00a0Yang OPTIMADE: An API for exchanging materials data Scientific Data8(217) (2021). doi:10.1038\/s41597-021-00974-z.","DOI":"10.1038\/s41597-021-00974-z"},{"key":"e_1_3_3_5_2","unstructured":"Apollo Inc. 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