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.

Now, use NL2Query™ to search that database with natural language.

Instead of a SPARQL query, a user can use natural language, such as English, to search the database. Historically, Lymba's NLP pipeline has been used to extract knowledge out of large volumes of text and store it in a graph database.

We use that same technology for the query: a person’s question is sent through the Lymba pipeline just like any other text and the system establishes a semantic representation of the data. Then the system converts the plain English entry into SPARQL, queries the database, and displays the retrieved result.

The system can query any graph, even if Lymba did not originally extract the data. Knowledge Base Creation 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

Organizations can now provide employees a new knowledge base to make quicker, more thoughtful decisions about their business by giving them an easy way to access their company’s data resources.

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