Lymba NL2Query™
Natural Language Search for Graph Databases
In large organizations, graph databases are beginning to be used instead of relational databases for speedier performance and to find and set relationships among the data. This allows a company to rapidly expand how much it knows about its data and its business.
Traditionally, you need to know SPARQL (or a graph querying language) in order to access the data and that skill is generally only held by a handful of people. This means that even though an expansive, flexible knowledge base can now be created, it will be hampered by how quickly it can roll out.
Power up Lymba. Instead of a SPARQL query, a user can use Lymba to search that database for answers with natural language. Historically, Lymba’s NLP pipeline has been used to extract knowledge out of large volumes of text and store it in a graph database.
The system can query any graph, even if Lymba did not extract the data. NL2Query is accomplished by leveraging the Lymba K-Extractor NLP pipeline, which includes 86 entity types and 26 semantic relationships. We then layer on an ontology to understand the context of the search. We can start with a free consultation to understand your opportunities and provide a demo.
