RDFFrames: knowledge graph access for machine learning tools

Abstract

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.

Preliminary Guidelines For Combining Data Integration and Visual Data Analysis

“…Work in natural language processing (NLP) has studied how entity resolution can be learned and improved over time through user interactions [20]. A number of tools [21], [22] help people model queries into knowledge graphs [23], [24]; however, there are scale limitations for large, complex, and heterogeneous structures [25]. Other automated approaches feature interactive programming interfaces [26], equi-join-able tables [27], and deep learning approaches to entity resolution [28].…”

Towards a GML-Enabled Knowledge Graph Platform

“…However, we classify GMLaaS as part of the DB4AI approach since we have optimized the training pipeline using our meta-sampling approach, which queries a KG to extract a task-specific subgraph. Works RDFFrames [41], DistRDF2ML [42], and Apple Saga [18] aim to bridge the gap between ML and RDF systems by enabling the user to extract data from heterogeneous graph engines in a standard tabular format to apply traditional ML tasks such as classification, regression, and clustering or use KGE methods to generate node/edge embeddings for similarity search applications.…”

Scoping review of knowledge graph applications in biomedical and healthcare sciences

“…A challenge arises from enrichment of KGs with data for a specific disease; this approach may improve predictions when analysing a diseaserelevant question by adding relevant contextual data, but may introduce bias into the graph. (20) (21) KGs exhibit substantial heterogeneity in terms of size, as reflected by wide variation in node and edge counts, as well as counts of node and edge classes. This may in part reflect the diverse scope and use cases for knowledge graphs or represent limitations in available storage and compute for analysis but may also represent a lack of known best practice regarding optimal KG size and connectivity.…”