A knowledge graph is a knowledge base that uses a graph-structured data model or topology to integrate data. Knowledge graphs are often used to store interlinked descriptions of objects, events or concepts with free-form semantics. Knowledge graphs use ontologies to put data in context via linking and semantic metadata and this way provide a framework for data integration, unification, analytics and sharing.
They are also prominently associated with and used by Google, Bing, and Yahoo, and with question-answering services such as Google Assistant, Siri and Alexa. All these examples were developed with proprietary tools.
For organizations that look to benefit from knowledge graphs, the available solutions in the market require significant changes in their IT departments. This is due to the fact that most data in the world is stored in formats that are not compatible with the format in which data is stored in knowledge graphs, so data needs to be extracted from its current DBMS, transformed to a new format and loaded into a separate, suitable DBMS. Another reason being is that to use knowledge graphs, data engineers and consumers need to acquire news skills to model in OWL and query in SPARQL.
Different from most other solutions, the timbr SQL Knowledge Graph platform creates a virtual layer that works in standard SQL to seamlessly connect to existing databases and is implemented without requiring new skills.
Contact us to learn how timbr can help your organization join the knowledge revolution.