Weaviate Search Graph Vs. GA of IBM Graph

Question

IBM Graph service is only compared to how it can add and store properties in the form of key/value pairs associated with the data, for both vertices and nodes connected by edges, rather than the more traditional form of storing the data in tabular form using rows and columns. However, how is the GA of IBM Graph compared to Knowledge Graph with Weaviate Search Graph (GraphQL - RESTful - Semantic Search - Semantic Classification - Knowledge Representation)?

Answer 1

The answer is based on the assumption that you mean JanusGraph because of this article.

• JanusGraph is a Graph DBMS optimized for distributed clusters.
• Weaviate is a GraphQL based semantic search engine.

The main difference is that JanusGraph focusses on large graphs whereas Weaviate focusses on search where results are represented in graph format.

• You might pick JanusGraph if you want to store and analyze a large graph.
• You might pick Weaviate if you build a search engine and/or store data in graph format.

Something Weaviate can do what other search engines (regardless if they are graph-based or not) is index your data semantically, e.g., you can find a data object about the publication Vogue by searching for fashion.

Weaviate query example:

Disclaimer:
I'm part of the team working on Weaviate.