Knowledge Assets

Our goal is to help organizations to create a living knowledge graph that can continuously integrate and unify the entire spectrum of data assets an organization possesses. This provides context for unleashing an organization’s collective intelligence by augmenting the ability of people to collaborate and share knowledge, derive insights and answer questions using the resulting dynamic knowledge repository.

What are Knowledge Assets?

Within and across any organization, knowledge is captured in many formats and many locations. At most enterprises, there are between ten and twenty critical knowledge stores, including internal resources like financial and accounting systems, customer service support tickets, sales reports and customer databases, inventory and product databases, email systems, messaging streams, and external information, including things like analyst reports, news articles, and geospatial data. Unfortunately, although these knowledge stores exist, and can be enumerated, the information within these knowledge stores is mostly isolated in data silos and cannot be easily leveraged for decision-making.

What is a Knowledge Graph? What is a Graph Database?

A knowledge graph is a new way of visualizing information and data. Unlike a chart which focuses on numerical values and chronology, a knowledge graph depicts the complex connections or relationships that link various pieces of information, creating a powerful presentation of patterns and structures of the information. The chief characteristics of a knowledge graph are nodes and connections (sometimes called edges, sometimes called graphs) between nodes. By examining the distribution of the nodes and their respective connections, it is easy to identify unusual relationships and clusters of associated information.

Because of their unique architecture, Knowledge graphs can quickly examine the second-order and third-order relationships that exist between various data, delivering deep insights about business questions or decisions. Frequently, knowledge graphs are the only tool that uncover these kinds of insights. Knowledge graphs are especially useful when applied to very large datasets that include information from many different sources. A single knowledge graph can quickly synthesize and analyze many gigabytes of information, performing a task in seconds that would have formerly taken a large team of dedicated analysts many months to complete. Knowledge graphs are widely used in the financial sector for fraud detection and risk assessment. Menome Technologies is applying the power of knowledge graphs to engineering, environment, energy and M&A sectors.

Menome has deep technology relationships with leading graph database vendors, including Neo4j, Amazon Neptune and Azure Cosmos DB. Menome Insight can be deployed using any of these engines.