In this blog series, we discuss why Knowledge Graphs are excellent at guiding and focusing ML and at serving as a unifying fabric for different AI algorithms.
How Do Knowledge Graphs Enable AI?
Knowledge Graphs represent knowledge, thus, are part of the KR branch of AI. They enable computers to reason based on the full available contextual and conceptual information. Knowledge Graphs are also excellent at guiding and focusing ML and at serving as a unifying fabric for the different AI algorithms.
A knowledge graph is self-descriptive, or, simply put, it captures the data as well as its meaning or semantics. As a result, computers can reason — derive additional implicit information from the explicitly stated information.
Different AI algorithms can use the knowledge graph — both, as input and as output. Results of processing come together, enriching each other. For example, given a training set with the information about people and provided with directions to learn about specific properties such as eye color or parents, ML algorithms can learn that each person has no more than two parents, and that children of blue eyed parents always have blue eyes. These rules can then be captured explicitly in the knowledge graph as a suggested rule and checked by the human curators.
Once approved for inclusion, rules could be used to identify issues with missing or incorrect data. For example, to ag as an issue a blue eyed person whose parents have brown eyes. Established rules can also be used as “known axioms” in further application of ML to ensure that insu cient or incorrect training data does not cause erroneous conclusions.
Knowledge Graphs Form A Trusted Foundation for AI and ML by Providing Meaning to Information
A Knowledge Graph represents a knowledge domain:
- It represents knowledge as a graph – A network of nodes and links, not tables of rows and columns
- It represents facts (data) and models (metadata) in the same way. Models include rich rules and enable inferencing
- It is based on open standards, from top to bottom and can readily connect to knowledge in private and public clouds
Enterprise AI and ML are well supported by Knowledge Graphs which can provide:
- An automated means for maintaining and improving data quality at any step in the data lifecycle
- Well-understood, curated training data sets
- Integration of structured and unstructured data sources as input
What Does Google Say About Knowledge Graphs?
Google has been one of the key pioneers in the use of sophisticated ML. Starting in 2012, Google began to use a knowledge graph to deliver more intelligent search results.
“Scientists of AI at Google’s Google Brain and DeepMind units acknowledge ML is falling short of human cognition and propose that using models of networks might be a way to find relations between things that allow computers to generalize more broadly about the world.” Peter W. Battaglia of Google’s Deep Mind unit, along with colleagues from Google Brain, MIT, and the University of Edinburgh argue for “blending powerful deep learning approaches with structured representations,” and their solution is something called a “graph network.”
These are models of collections of objects, or entities, whose relationships are explicitly mapped out as “edges” connecting the objects. The idea is that graph networks are bigger than any one ML approach. Graphs bring an ability to generalize about structure that the individual neural nets don’t have.
Once approved for inclusion, rules could be used to identify issues with missing or incorrect data. For example, to flag as an issue a blue eyed person whose parents have brown eyes. Established rules can also be used as “known axioms” in further application of ML to ensure that insufficient or incorrect training data does not cause erroneous conclusions.
What Other Leading Companies Invest In Knowledge Graphs?
Apple, Amazon, Airbnb, Bloomberg, Facebook, LinkedIn, Thomson Reuters — and these are just a few of the companies that invest in Knowledge Graphs.
In July 2017, W3C released a new standard called SHACL for representing graph shapes, models and rules graphs. It quickly became a hot topic with growing a number of implementations.
We believe this to be somewhat of a “view into a rear window” since so many organizations already use Knowledge Graphs in production.
“With a large-scale knowledge graph, developers can build high-dimensional representations of entities and relations. The resulting embeddings will greatly bene t many machine-learning, NLP, and AI tasks as sources of features and constraints, and can form the basis for more sophisticated inferences and ways to curate training data.”
The graph database market is quickly going mainstream with Amazon’s Neptune, an on-demand AWS graph database. A number of other vendors have started to o er graph databases.
How Do Knowledge Graphs Overcome The Typical Challenges of Data Governance?
Data Governance Issues
- Galaxies of data
- Diversity of perspectives – Business, Technical, Regulatory
- Diversity of representation
- Create a knowledge graph representing data sources
- Link to other relevant enterprise information e.g., systems, policies, infrastructure, activities
- Enrich, discover, connect
- Use to guide business decisions
- Provide common search for all types of stakeholders
- Connect business terms to data elements
- Support regulatory compliance by tracing data lineage
- Represent regulations as Knowledge Graphs
- Infer rules from data in Knowledge Graphs
- Connect public and private Knowledge Graphs
- Create enriched knowledge resources
- Provide Knowledge Graph APIs for applications
Join us for Part 3 of our blog series to learn how TopBraid EDG uses AI to support enhanced information governance.