{ "version": "https://jsonfeed.org/version/1.1", "title": "chem-bla-ics", "description": "Chemblaics (pronounced chem-bla-ics) is the science that uses open science and computers to solve problems in chemistry, biochemistry and related fields.", "home_page_url": "https://chem-bla-ics.linkedchemistry.info/", "feed_url": "https://chem-bla-ics.linkedchemistry.info/2024/11/17/sparql-examples.json", "icon": "https://chem-bla-ics.linkedchemistry.info/assets/images/chem-bla-ics_logo.png", "language": "en", "authors": [ { "name": "Egon Willighagen", "url": "https://orcid.org/0000-0001-7542-0286", "_orcid": "0000-0001-7542-0286" } ], "items": [ { "id": "https://doi.org/10.59350/djm89-5nb39", "url": "https://chem-bla-ics.linkedchemistry.info/2024/11/17/sparql-examples.html", "title": "SPARQL examples: SIB model, software, and patches", "content_html": "

Jerven Bolleman et al. recently published a great preprint\nabout how to use RDF to give SPARQL queries context by linking it (semantically) with metadata. The context includes\nkeywords, the SPARQL endpoint the query can be run against, and a human-oriented description of the query. A few groups\nhave at recent hackathons been working on usingn the combination of a SPARQL query and a human-oriented description\nto train large language models, including the group behind this paper. Given that SPARQL is a very small language, I can see\nthis may work well, and that it may support our VHP4Safety and\nScholia projects.

\n\n

But in addition to the data model for SPARQL as research output (see doi:10.32388/ZNWI7T.2),\nthe paper also introduces the sparql-example-utils software that I was\nfirst introduced with at the recent October Scholia hackathon.

\n\n

But I have/had some features I like to see added. The first is provenance. Who is the author/contributor of the SPARQL\nquery? Is there a open license for it, or perhaps public domain? How do I give attribution if I reuse the SPARQL query?\nThese things matter in a modern recognition and rewards world where is room for\neveryone’s talent. A set of good SPARQL queries may be more valuable than a ten-page Jupyter notebook (and the other way\naround). So, I started\nwriting\npatches. And I created\na custom jar so that I can see these\npatches in action in our growing list of SPARQL queries\n(here a WikiPathways query):

\n\n

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I started collecting SPARQL queries for ChEMBL,\nWikiPathways, and\nVHP4Safety. These queries are often part\nof other interfaces but we can easily extract the original SPARQL from the Turtle files behind these pages.

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References

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Bolleman, J., Emonet, V., Altenhoff, A., Bairoch, A., Blatter, M.-C., Bridge, A., Duvaud, S., Gasteiger, E., Kuznetsov, D., Moretti, S., Michel, P.-A., Morgat, A., Pagni, M., Redaschi, N., Zahn-Zabal, M., de Farias, T. M., & Sima, A. C. (2024). A large collection of bioinformatics question-query pairs over federated knowledge graphs: methodology and applications (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2410.06010 CrossRef Scholia
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Houweling, J., & Willighagen, E. (2023). Research Output Management. In Definitions. Qeios. https://doi.org/10.32388/znwi7t CrossRef Scholia
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", "summary": "Jerven Bolleman et al. recently published a great preprint about how to use RDF to give SPARQL queries context by linking it (semantically) with metadata. The context includes keywords, the SPARQL endpoint the query can be run against, and a human-oriented description of the query. A few groups have at recent hackathons been working on usingn the combination of a SPARQL query and a human-oriented description to train large language models, including the group behind this paper. Given that SPARQL is a very small language, I can see this may work well, and that it may support our VHP4Safety and Scholia projects.", "image": "https://chem-bla-ics.linkedchemistry.info/assets/images/sparql-examples-tgx.png", "date_published": "2024-11-17T00:00:00+00:00", "date_modified": "2025-03-11T00:00:00+00:00", "tags": ["sparql","wikipathways","vhp4safety","chembl","scholia"], "_references": [ { "url": "https://doi.org/10.32388/ZNWI7T", "doi": "10.32388/ZNWI7T" } , { "url": "https://doi.org/10.48550/arXiv.2410.06010", "doi": "10.48550/arXiv.2410.06010" } ], "_funding": [{"award": { "title" : "The Virtual Human Platform for Safety Assessment", "acronym" : "VHP4Safety", "uri" : "drc.filenumber:nwa129219272" }, "funder": { "name": "Dutch Research Council", "ror": "04jsz6e67" } }], "authors": [ { "name": "Egon Willighagen", "url": "https://orcid.org/0000-0001-7542-0286" } ] } ] }