The Datum Universe Model

What?

The Datum Universe model is a knowledge representation scheme. It specifies how to represent data and how to make inferences.

Why?

Identify the fundamental elements of knowledge and the fundamental elements of intelligence. The goal is to provide a unified minimalistic framework that has the building blocks for representing any kind of knowledge constructs

How?

The Datum Universe represents knowledge using two fundamental concepts:

1. The Datum; which is an abstract element defined entirely by its relations to other datums
2. The "is" relation; which is a directed link between two datums and is the only type of relation allowed among datums.

Intelligence emerges from two fundamental properties of the Datum Universe:

1. The transitivity property of the "is" relationship (if a is b and b is c, then a is c) allows for inheritance and generalization. See side notes for examples.
2. Induction; which is the capability of the datum universe to create new datums autonomously. The induced datums reduce the number of connections and provide a built-in classification process. See side notes for an example.

You can understand the datum universe model as a Graph, as a Poset, or by comparison to relational Tables and EAV / RDF models. You can also read the white papers listed.

Advantages

Existing data models rely on higher, more complex, and diverse building blocks to represent real life knowledge. For example, in relational databases, we have the concepts: table, row, column, field, primary key, and foreign key. In graph-based knowledge representations, we have nodes and an unlimited number of relationship types. These concepts are "hard-coded" because we cannot modify or advance them, nor reason about their properties within the same framework.

The Datum Universe approach is to build these complex concepts as "soft-coded" constructs out of the minimum "hard-coded" elements. This approach makes it possible to:

1. Encode intelligence into the framework. Providing inheritance, generalization /prediction, and a classification process. These are the building blocks for data mining, machine learning and natural language understanding applications.
2. Study knowledge bases using formal tools like Partial Orders and Graph Theory. The Datum Universe is essentially a Directed Acyclic Graph (DAG) in a Transitive Reduction state. This gives us a deeper insight into the knowledge base content.
3. Implement the model in various ways to achieve different performance-memory behaviour from extremely fast O(1) to extremely memory compact.
4. Provide a simple operator-based query language.
5. Implement the model totally in hardware. For example, similar to IBM's SyNAPSE chip

Applications

We can highlight 3 major areas for applications of the Datum Universe:

1. Traditional In-memory database systems. The model provides flexibility in the actual representation of data in memory. Different representations may target different memory/performance profiles. The fundamental nature of the model makes it easy to represent temporal data as well as executable code as datums. This also leads to the simplicity and power of the models's query operators. See the Datumtron Graph Database API white paper.
2. Data mining system. Use the induction process to have data mining / machine learning built into the database. As data changes, new patterns can be detected making predictions based on updated patterns. Contrast this with running external data mining algorithms on snapshots of the database. For example see the Predict tool.
3. Intelligent knowledge Agent. Using Natural Language parsers, we can acquire knowledge from existing text sources and build a datum universe. Since the hard-coded knowledge is minimal, there is no limit on the depth of understanding that can be achieved by an intelligent agent.