What do you need in a graph library?

A recently launched open source library in Python provides an abstraction layer for developing knowledge graphs. This survey attempts to find needs/features/use cases for us to prioritize in our development roadmap. Your info will be kept private, not shared with any third-party, and will not be used for any form of targeted marketing.

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What kinds of graph support do you need most? *

Building graphs in RDF, i.e., managing vocabularies, namespaces, literals, etc.

Interactive visualizations

Serialization methods (see detailed question below)

Network-based inference (see detailed question below)

Graph database integrations (see detailed question below)

Validation (see detailed question below)

Embedding (e.g., deep learning integration via PyTorch, etc.)

Graph algorithms (e.g., SSSP, BFS, centrality, etc.)

SPARQL queries

Parallel processing that scales across a cluster

Traversals of triples, triangles, other relatively simple structures

Multi-scale backbone filter

Homology, TDA, and other advanced analytics approaches

Other:

Which serialization methods do you need most?

Parquet

RDF files in N3 or TTL ("Turtle") format

JSON-LD files

Graph database connectors

XML files

Other:

Clear selection

Which forms of network inference do you need most?

Probabilistic Soft Logic (PSL)

Bayesian Networks (Statistical or Causal)

Markov Logic Networks (MLN)

Other:

Clear selection

Which forms of validation support do you need most?

Validating based on SHACL

Validating based on a W3C standards grammar

Validating based on axioms (e.g., SKOS)

Other:

Clear selection

Which graph database integrations do you need most?

DataStax

AWS Neptune

Neo4j

ArangoDB

Stardog

Tiger Graph

OrientDB

Ontotext

Oracle

Other:

Clear selection

What kinds of graph algorithms do you need to use the most?

Any other suggestions about what you'd like to see in a KG library?

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