Ontotext’s GraphDB 8.9 Boosts Semantic Similarity Search

Sofia Tuesday, April 9, 2019

GraphDB 8.9 – the latest release of Ontotext’s signature semantic graph database GraphDB – consolidates the experience of data scientists developing knowledge graph embeddings and semantic similarity searches.

After the initial release of features allowing to identify similar nodes in the graph by analyzing the graph structure and meaning, we got a number of feature requests for further improvements. The semantic similarity search is based on the Random Indexing algorithm. Random Indexing is a highly scalable algorithm based on Random Projection, a method for reducing the vector dimensionality by starting with random vectors and later refining the distance between their points in multiple iterations.

First of all, the latest GraphDB release enables users to create hybrid similarity searches using pre-built text-based similarity vectors for the predication-based similarity index. The index combines the power of graph topology with the text similarity. The users can control the index accuracy by specifying the number of iterations required to refine the embeddings.

Another improvement is that now GraphDB 8.9 allows users to boost the term weights when searching in text-based similarity indexes. It also simplifies the processes of abortion of running queries or updates from the SPARQL editor in the Workbench.

As usual, GraphDB 8.9 is updated to the RDF4J 2.4.6 public release to keep users up to date with the latest RDF4J developments and to enable them to get the latest bug fixes and features in the RDF4J project.

Get your GraphDB now and develop your proprietary knowledge graph for advanced search and analytics.


For more information, contact Doug Kimball, Chief Marketing Officer at Ontotext