Ontotext is releasing GraphDB 8.7, the latest version of its semantic graph database adding support for concept-matching in knowledge graphs thanks to a new plugin returning similar terms, documents, and entities based on statistic semantics methods such as Random Projection. This release also includes performance improvements that enable efficient query federation across repositories hosted in a single database instance and faster updates in big knowledge graphs. Active repositories can now be used as a source for backup, without downtime for reading operations even in non-cluster environments.
GraphDB release 8.7 features the Semantic Vectors package, integrated as a plugin, to enrich the RDF graph with semantic similarity indices, based on a highly scalable reduced dimensionality vector space model. Similar to the full-text search connectors of GraphDB, developers and administrators can define various indices, which cover specific types of documents and entities, specific attributes and property paths. Unlike full-text search, the company says, the database allows a user to search for similarity across all combinations of terms and documents: get terms similar to a given one, find similar entities or documents, search for a document by term or get terms most characteristic for a document.
According to Ontotext, it is continuously improving GraphDB, expanding its functionalities to serve various use cases. With the new plugin, users can get more results based on matching of semantically close concepts—results that cannot be obtained via structured and full-text search queries, which require exact matching of words or identifiers. For example, when a new question is submitted to a help desk, users will be able to query and find similar questions in the database and how they have been answered in the past. In news, a collection and processing scenario, similarity search can be used to interlink and group news about the same story from different sources.
The new version of GraphDB also offers much faster protocol for internal federation across repositories in one and the same database instance. It offers several times faster query evaluation compared to a scenario where data across repositories is combined using non-optimized SPARQL federation. The actual speedup depends on the federation pattern but the company says that evaluations show 2X to 8X better performance across different queries in a scenario for news analytics using semantic metadata and big knowledge graphs, derived from Ontotext’s FactForge demonstration service. This optimization makes feasible a wide range of scenarios related to better data governance. For instance, it enables the segmentation of the RDF graph into multiple repositories with different security settings and update procedures. It also enables multi-tenant knowledge-graph-as-a-service scenarios, where multiple proprietary repositories can be queried together with a big non-proprietary domain knowledge graph.
Another performance improvement supports dynamic management of big knowledge graphs. GraphDB 8.7 does faster commits, due to optimization, which minimizes the transaction overhead. The result is twice faster update rate for scenarios like the one in LDBC Semantic Publishing Benchmark, where small transactions make frequent updates in a knowledge graph with 1 billion statements.
GraphDB 8.7 also features improvements which make the operations of critical database instances easier and more reliable. The GraphDB can now create a backup of an active repository by changing its status from read/write to read-only. This enables efficient backups without read downtime.
GraphDB 8.7 is also upgraded to the latest versions of Elasticsearch v.6.3.x, Solr v.7.4.x and Lucene v.7.4.x and refactoring to use REST client instead of Transport client.
More information is now available from Ontotext about GraphDB 8.7.
Cludo Custom Site Search