RDFFrames: knowledge graph access for machine learning tools
<p>Knowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query interface. Surprisingly, machine learning tools for knowl...
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2021
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| _version_ | 1864513567972130816 |
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| author | Aisha Mohamed (5152970) |
| author2 | Ghadeer Abuoda (14150706) Abdurrahman Ghanem (12119838) Zoi Kaoudi (14150709) Ashraf Aboulnaga (14150712) |
| author2_role | author author author author |
| author_facet | Aisha Mohamed (5152970) Ghadeer Abuoda (14150706) Abdurrahman Ghanem (12119838) Zoi Kaoudi (14150709) Ashraf Aboulnaga (14150712) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aisha Mohamed (5152970) Ghadeer Abuoda (14150706) Abdurrahman Ghanem (12119838) Zoi Kaoudi (14150709) Ashraf Aboulnaga (14150712) |
| dc.date.none.fl_str_mv | 2021-08-26T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00778-021-00690-5 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/RDFFrames_knowledge_graph_access_for_machine_learning_tools/21597114 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Data management and data science Machine learning Knowledge graphs RDF SPARQL PyData Data preparation Machine learning |
| dc.title.none.fl_str_mv | RDFFrames: knowledge graph access for machine learning tools |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Knowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query interface. Surprisingly, machine learning tools for knowledge graphs do not use SPARQL, despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of data model and programming style. Machine learning tools work on data in tabular format and process it using an imperative programming style, while SPARQL is declarative and has as its basic operation matching graph patterns to RDF triples. We posit that a good interface to knowledge graphs from a machine learning software stack should use an imperative, navigational programming paradigm based on graph traversal rather than the SPARQL query paradigm based on graph patterns. In this paper, we present RDFFrames, a framework that provides such an interface. RDFFrames provides an imperative Python API that gets internally translated to SPARQL, and it is integrated with the PyData machine learning software stack. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPQARL query, executes it on the database system, and returns the results in a standard tabular format. Thus, RDFFrames is a useful tool for data preparation that combines the usability of PyData with the flexibility and performance of RDF database systems.</p><h2>Other Information</h2> <p> Published in: The VLDB Journal<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s00778-021-00690-5" target="_blank">http://dx.doi.org/10.1007/s00778-021-00690-5</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_ee06183d581068c4595eba110f3e3ac9 |
| identifier_str_mv | 10.1007/s00778-021-00690-5 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/21597114 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | RDFFrames: knowledge graph access for machine learning toolsAisha Mohamed (5152970)Ghadeer Abuoda (14150706)Abdurrahman Ghanem (12119838)Zoi Kaoudi (14150709)Ashraf Aboulnaga (14150712)Information and computing sciencesData management and data scienceMachine learningKnowledge graphsRDFSPARQLPyDataData preparationMachine learning<p>Knowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query interface. Surprisingly, machine learning tools for knowledge graphs do not use SPARQL, despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of data model and programming style. Machine learning tools work on data in tabular format and process it using an imperative programming style, while SPARQL is declarative and has as its basic operation matching graph patterns to RDF triples. We posit that a good interface to knowledge graphs from a machine learning software stack should use an imperative, navigational programming paradigm based on graph traversal rather than the SPARQL query paradigm based on graph patterns. In this paper, we present RDFFrames, a framework that provides such an interface. RDFFrames provides an imperative Python API that gets internally translated to SPARQL, and it is integrated with the PyData machine learning software stack. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPQARL query, executes it on the database system, and returns the results in a standard tabular format. Thus, RDFFrames is a useful tool for data preparation that combines the usability of PyData with the flexibility and performance of RDF database systems.</p><h2>Other Information</h2> <p> Published in: The VLDB Journal<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s00778-021-00690-5" target="_blank">http://dx.doi.org/10.1007/s00778-021-00690-5</a></p>2021-08-26T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00778-021-00690-5https://figshare.com/articles/journal_contribution/RDFFrames_knowledge_graph_access_for_machine_learning_tools/21597114CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/215971142021-08-26T06:00:00Z |
| spellingShingle | RDFFrames: knowledge graph access for machine learning tools Aisha Mohamed (5152970) Information and computing sciences Data management and data science Machine learning Knowledge graphs RDF SPARQL PyData Data preparation Machine learning |
| status_str | publishedVersion |
| title | RDFFrames: knowledge graph access for machine learning tools |
| title_full | RDFFrames: knowledge graph access for machine learning tools |
| title_fullStr | RDFFrames: knowledge graph access for machine learning tools |
| title_full_unstemmed | RDFFrames: knowledge graph access for machine learning tools |
| title_short | RDFFrames: knowledge graph access for machine learning tools |
| title_sort | RDFFrames: knowledge graph access for machine learning tools |
| topic | Information and computing sciences Data management and data science Machine learning Knowledge graphs RDF SPARQL PyData Data preparation Machine learning |