Real-world events demonstrate our inability to understand rapidly and accurately what we already know. In other words, we cannot answer questions completely, despite the fact that we may hold the requisite data. For example, if someone attempted to enter the United States (US) at an airport, and US officials initiated a query to the “system” and found nothing, that person may enter the US erroneously. This might occur because US officials asking a question such as “What do we know about this person?” cannot assuredly answer it – and not in a timely fashion.
In our scenario, US officials pose questions to a fractal set of applications and databases, not a system. When a question is asked, “line-of-business” applications query a set of pre-wired “schema-bound” data sources. The entire set of available databases and other content repositories are not queried. Therefore, although the answer may reside “in the enterprise,” the officials will never know. That is to say, we don’t know what we know. To be fair, even a coherent, well-functioning system may not know all there is to know about a given question; however, such a system would rapidly and completely provide “all that we do know”. In other words, we could indeed say, “We know what we know.”
Cambridge Semantics leverages Semantic Web technologies, which comprise schema-less, self-describing, and link-able data models, methodologies and standards, to liberate content from brittle, application-specific silos; thus creating an agile information fabric. Our industry standards-based approach interoperates structured data and unstructured content from myriad sources to create unified information access. Our approach provides a model wherein schematic changes do not “break” consuming applications; they simply add to the information fabric. Because data is self-describing, software is decoupled from data. Expensive data Extraction, Transformation, and Loading (ETL) processes diminish; software is agnostic to the data schema.
Whereas today’s Web Oriented Architecture identifies, addresses, and links pages, Cambridge Semantics employs semantic technologies to uniquely identify, address, and link data. Humans and software processes can unambiguously correlate and interpret information across multiple sources. When a client poses a query, the selected data do not require “syntactic matches” for the software to discover and return the answer. Our solutions platform transcends syntax and structure and operates at the concept level. The result: global context enabling information on demand, which supports decision-making.
We understand creating information on demand and shared understanding requires systems that support “speed of change.” Understanding context and its value is crucial to realizing the ability to “know what we know.” Cambridge Semantics employs semantic technologies to create adaptive and resilient information infrastructure, which enables organizations to gain insights from their holdings and realize information on demand.
Want to learn more about how semantic graph technology? Watch Barry Zane, our Vice President of Engineering and an industry expert in database technologies, in this on-demand webinar discussing "Semantic Graph Databases: The Evolution of Relational Databases".