RDFFrames: Knowledge Graph Access for Machine Learning Tools

02/10/2020
by   Aisha Mohamed, et al.
0

Knowledge graphs represented as RDF datasets are becoming increasingly popular, and they are an integral part of many machine learning applications. A rich ecosystem of data management systems and tools that support RDF has evolved over the years to facilitate high performance storage and retrieval of RDF data, most notably RDF database management systems that support the SPARQL query language. Surprisingly, machine learning tools for knowledge graphs typically 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 the expected data model and the 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 the basic query 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 introduce RDFFrames, a framework that provides such an interface. RDFFrames enables the user to make a sequence of calls in a programming language such as Python to define the data to be extracted from a knowledge graph stored in an RDF database system. It then translates these calls into compact SPQARL queries, executes these queries on the database system, and returns the results in a standard tabular format. Thus, RDFframes combines the usability of a machine learning software stack with the performance of an RDF database system.

READ FULL TEXT
research
01/25/2018

Killing Two Birds with One Stone -- Querying Property Graphs using SPARQL via GREMLINATOR

Knowledge graphs have become popular over the past decade and frequently...
research
04/18/2023

BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs

Knowledge graphs are an increasingly common data structure for represent...
research
07/05/2021

PandaDB: Understanding Unstructured Data in Graph Database

At present, graph model is widely used in many applications, such as kno...
research
05/22/2018

MonetDBLite: An Embedded Analytical Database

While traditional RDBMSes offer a lot of advantages, they require signif...
research
04/25/2019

Declarative Recursive Computation on an RDBMS, or, Why You Should Use a Database For Distributed Machine Learning

A number of popular systems, most notably Google's TensorFlow, have been...
research
06/17/2021

PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python

Machine learning is a general-purpose technology holding promises for ma...
research
04/23/2023

CBIM: A Graph-based Approach to Enhance Interoperability Using Semantic Enrichment

Interoperability remains a challenge in the construction industry. In th...

Please sign up or login with your details

Forgot password? Click here to reset