"A taxonomy is a classification scheme that organizes categories in a broader-narrower hierarchy. Items that share similar qualities are grouped into the same category, and the taxonomy provides a global organization by relating categories to one another." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)
"AI is intended to create systems for making probabilistic decisions, similar to the way humans make decisions. […] Today’s AI is not very able to generalize. Instead, it is effective for specific, well-defined tasks. It struggles with ambiguity and mostly lacks transfer learning that humans take for granted. For AI to make humanlike decisions that are more situationally appropriate, it needs to incorporate context." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)
"Data architects often turn to graphs because they are flexible enough to accommodate multiple heterogeneous representations of the same entities as described by each of the source systems. With a graph, it is possible to associate underlying records incrementally as data is discovered. There is no need for big, up-front design, which serves only to hamper business agility. This is important because data fabric integration is not a one-off effort and a graph model remains flexible over the lifetime of the data domains." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)
"Data fabrics are general-purpose, organization-wide data access interfaces that offer a connected view of the integrated domains by combining data stored in a local graph with data retrieved on demand from third-party systems. Their job is to provide a sophisticated index and integration points so that they can curate data across silos, offering consistent capabilities regardless of the underlying store (which might or might not be graph based) […]." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)
"Despite their predictive power, most analytics and data science practices ignore relationships because it has been historically challenging to process them at scale." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)
"Graph data models are uniquely able to represent complex, indirect relationships in a way that is both human readable, and machine friendly. Data structures like graphs might seem computerish and off-putting, but in reality they are created from very simple primitives and patterns. The combination of a humane data model and ease of algorithmic processing to discover otherwise hidden patterns and characteristics is what has made graphs so popular."
"In an era of machine learning, where data is likely to be used to train AI, getting quality and governance under control is a business imperative. Failing to govern data surfaces problems late, often at the point closest to users (for example, by giving harmful guidance), and hinders explainability (garbage data in, machine-learned garbage out)." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)
"Knowledge graphs are a specific type of graph with an emphasis on contextual understanding. Knowledge graphs are interlinked sets of facts that describe real-world entities, events, or things and their interrelations in a human- and machine-understandable format." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)
"[…] knowledge graphs are useful because they provide contextualized understanding of data. They achieve this by adding a layer of metadata that imposes rules for structure and interpretation." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)
"Knowledge graphs use an organizing principle so that a user (or a computer system) can reason about the underlying data. The organizing principle gives us an additional layer of organizing data (metadata) that adds connected context to support reasoning and knowledge discovery. […] Importantly, some processing can be done without knowledge of the domain, just by leveraging the features of the property graph model (the organizing principle)." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)
"Many AI systems employ heuristic decision making, which uses a strategy to find the most likely correct decision to avoid the high cost (time) of processing lots of information. We can think of those heuristics as shortcuts or rules of thumb that we would use to make fast decisions." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)
"Understanding the entire data ecosystem, from the production of a data point to its consumption in a dashboard or a visualization, provides the ability to invoke action, which is more valuable than the mere sum of its parts." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)
"We think of context as the network surrounding a data point of interest that is relevant to a specific AI system. […] AI benefits greatly from context to enable probabilistic decision making for real-time answers, handle adjacent scenarios for broader applicability, and be maximally relevant to a given situation. But all systems, including AI, are only as good as their inputs." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)
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