Humans are the supreme beings among all living things. Because we have so many feelings, judgments, failures, and successes, it is impossible to feel every emotion. However, knowledge representation in artificial intelligence solutions have now surpassed all human efforts. As there are numerous complex tasks in AI that evaluates, whether in machine learning or deep learning. One such methodology is knowledge representation, which is independent of the logical situation and allows a strategy to decide on knowledge investment. Let’s closely look at some critical aspects of knowledge representation in AI.
An overview of knowledge representation in artificial intelligence solutions
Knowledge representation in artificial intelligence solutions is a process that AI goes through to learn about human behavior. As a result, AI will behave more competently in the use cases assigned. AI experts will eventually implement tasks to represent knowledge to humans in a specific way. However, our emotions, beliefs, research, intentions, knowledge, failures, and judgments are all high-level for any machine. And now, this knowledge representation assists the computer to understand all aspects of the human world, allowing AI to become truly intelligent. This knowledge stores information and reasoning deduce this stored information. And intelligence entails making decisions and acting based on knowledge.
Knowledge representation techniques in artificial intelligence solutions
Let’s look at the ways to represent knowledge in AI.
It is the most fundamental and well-known to all knowledge representation in artificial intelligence solutions in machines, with a well-defined syntax. This syntax must be free of ambiguity and must deal with prepositions. So, logical representation is a set of communication rules that computers use to represent facts. It is classified into two kinds:
- Propositional logic: It is known as propositional calculus or statement logic. It employs the Boolean, or True or False, method.
- First Order Predicate Calculus Logic (FOPL): It is a more advanced version of propositional logic that represents objects using quantifiers and predicates.
Many programming languages use semantics to convey information to represent logically. The disadvantage of this method is its strict representation and inefficiency.
It is a quick and simple graphical representation of how objects interlink to the data network. It is sometimes a kind of correlation between humans and computers. And semantic networks in AI services are made up of nodes, blocks, edges, and how they respond to objects. It’s also referred to as alternative FOPL representation. Furthermore, the representations are more organic than logical. It is comprehensive, but it occasionally suffers from costly calculations and does not support equivalent quantifiers.
One of the most common ways for AI services to represent knowledge is through production rules. It is translated as a simple if-else system and combines Propositional and FOPL logic in some ways. You require a basic understanding of knowledge representation. A rule applier, working memory, and a recognized act cycle are needed. In this case, you must use the production rule to check every condition for every input before committing an action. As a result, entering and checking the situations, and solving the problem, will continue as a recognition and act cycle. However, there are some drawbacks, such as the inability to store previous results and inefficiency when executing complex projects. A natural language system can also save money by avoiding the costs of disadvantages.
Frame representation is a basic level of knowledge representation in artificial intelligence solutions for business i.e, how values enter the rows and columns. As a result, it is known as a Frame. However, you must have a thorough understanding of attributes and values. This AI-specific data structure employs slots and fillers of any data type and shape. It is similar to DBMS where all the values are entered into the columns. The spaces in this section have names (attributes), and knowledge about them is in the fillers. And you will benefit from representing data in a row and column structure. However, this representation can subdivide knowledge into the table and then further substructures. Furthermore, because it is a typical data structure, it is simple to understand and manipulate. Moreover, it also includes common concepts like adding, removing, and deleting slots, which are simple to implement.
Type of knowledge representation
Here are some different kinds of knowledge representation.
As a result, this declarative knowledge type helps to store factual information in memory and appears static. The domain of such knowledge determines the relationship between events or things.
It differs from general declarative knowledge and is also known as imperative knowledge. And provides information on task completion and is used by modern robots to conquer or navigate within a room.
In business, meta-knowledge is to describe the knowledge of pre-defined knowledge. Planning, tagging, and learning are a few examples. This model evolves and uses a new specification.
It is also known as external knowledge representation in artificial intelligence solutions and works on the thumb rule. Additionally, it is a very productive for reasoning because it solves problems based on expert-compiled records of previous problems or issues. And provides insights based on previous problems it has solved.
It helps to establish relationships between concepts or objects and their descriptions, serving as a form of knowledge for solving real-world problems.
Essentials of knowledge representation cycle in AI
Here are some of the vital uses of knowledge representation.
It assists the AI system in gathering information about its surroundings via various sensors. Additionally, it familiarises the AI system with its surroundings and helps to interact. These senses can be structured data or other forms of sensor-based input such as video, audio, text, or any sensor-based values.
As the name implies, it is obtained through an AI system to run deep learning algorithms. These algorithms are present in the learning block, and AI transfers information from the perception block to the learning block for learning.
We use knowledge and reason and make decisions based on it. As a result, these two blocks will act like humans, going through all the knowledge data and finding the relevant ones to provide to the learning model whenever required.
Planning and Execution Block
Though these two blocks are independent but they work together. These blocks use the information from the knowledge and reasoning blocks to carry out specific actions. As a result, knowledge representation is extremely beneficial for AI systems to function intelligently.
Approaches to knowledge representation
Here are some common approaches to knowledge representation.
Simple relational knowledge
It is the basic tabular form of storing values relationally. The set of objects gets stored in columns. You know this is the same rule database follows and how relations between different entities form.
All classes are in a hierarchy in this approach. This approach also includes inheritable knowledge that shows the relationship between instance and class. It’s known as an instance relation. Objects and values are in Boxed nodes in this approach.
The inferential knowledge approach expresses knowledge as formal logic. And it depends on logic and ensures correctness.
Conclusion: AI solutions for business have so much to offer in every vertical and sector. Nowadays, AI is empowering every aspect it is entering. For more consideration, consult AI development services for your projects.