From Knowledge to Reasoning: Knowledge Representation in AI
SparkCognition's knowledge representation IP transforms siloed data into a digital knowledge base to help you make better and faster decisions.
Learn how SparkCognition uses knowledge representation to optimize maritime shipping schedules.
How does SparkCognition leverage knowledge representation?
SparkCognition Knowledge Studio transforms an organization’s tribal knowledge and subject matter expertise into a knowledge base that delivers the right insights at the right time.
Our patented AI-powered computational knowledge graph technology enables the creation of a new class of knowledge-based applications that improve critical business functions and operational decision-making. Knowledge Studio expedites the encoding of knowledge from diverse sources while enhancing it with computations and AI techniques. At the core of the Knowledge Studio is the Computational Knowledge Graph (CKG), a unique technology that separates conceptual modeling and operations on the data from the data content itself. This separation provides fluidity of modeling within a graph and makes it possible to repurpose any data into a relevant structure.
Applications created with Knowledge Studio become invaluable decision-intelligence drivers, improving efficiency, profitability, and sustainability while establishing a competitive advantage that grows over time.
What is knowledge representation in AI?
Knowledge representation and reasoning (KRR) is a subfield of artificial intelligence that differs from the probabilistic forms of machine learning that have become predominant in AI applications in recent years. An essential component of many AI applications, knowledge representation techniques aim to enable machines to apply logic similar to how humans approach and solve complex problems.
KRR approaches are a part of symbolic AI, affectionately known as “good old-fashioned artificial intelligence” by practitioners owing to its place as the dominant paradigm in early AI research history. It consists of concepts and techniques used to represent information about the world in a form that is usable by machines to solve complex problems and perform human-like semantic reasoning on a variety of tasks. Knowledge graphs are data structures that are well suited for storing information about various entities and their relationships. Knowledge graphs have broad applicability across a large range of AI use cases because of their ability to facilitate inference and understanding. When used in combination with sub-symbolic AI techniques like deep learning, emerging research is showing the possibility for new levels of performance in reasoning and understanding beyond those offered by today’s solutions.
Three benefits of knowledge representation (that scale)
“SparkCognition enables us to optimize all local decisions across our maritime shipping and logistics workflows.”
– Ibrahim Q. Al Buainain, President and CEO of Aramco Trading Company
First-order logic (FOL) allows for the representation of complex relationships and logical statements. FOL provides a formal way to represent objects, properties, and relationships between them to unlock reasoning actions using logical inference.
Semantic networks are a graphical method of knowledge representation that represent concepts as nodes and relationships as edges between them. Semantic networks can be applied to knowledge-based systems and information retrieval use cases and can help address probabilities or uncertainties.
Useful for learning complex patterns in data, neural networks can be used to represent knowledge in the form of learned weights and connections between neurons. They can also be used in combination with other methods, such as semantic networks or rule-based systems.
Learn more about AI and knowledge representation
Webinar: Unlocking the AI Black Box: Transforming Oil & Gas Operations With Neural Networks
The Long Journey to Realizing the True Potential of Machine Learning
Optimize Maritime Fleet Scheduling with the Power of AI
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