As mentioned before, we as humans categorize things naturally. This is how we understand the world around us and keep track of things.
We have categories for people (friends, strangers, family), places (homes, offices, streets), objects (toys, furniture, food), and events (birthdays, holidays, vacations).
We have very broad categories for people (men or women) and very specific ones (skin color, hair texture). For places, we have geographical locations like cities or states and specific locations like rooms or desks.
We also have a very strong sense of what is normal for each of these categories. For example: A man is not usually a woman, a stranger is not usually a friend, a home is not usually an office.
These categorical perceptions are what psychologists call implicit bias. It is the unconscious tendency to assign attributes to people based on one’s own personal experience.
Ontology is the study of the nature of being, being, and beings. In computer science, ontology is the theory of concepts and conceptual structure.
In other words, it’s the classification of things and actions into categories. For example, in computer science, a computer, a dog, and running are all considered entities.
Ontologies are used in several different fields including knowledge engineering, semantic computing, and cognitive sciences. They are used to maintain information about various types of objects, events, people, and places.
By maintaining information about these things in an organized manner using ontologies, artificial intelligence systems can more easily understand and process this information. This helps with their efficiency and effectiveness.
Taxonomy is the science that defines categories and subcategories to organize information. It is also the name for the hierarchy of categories that are used to organize information.
For example, in science, taxonomy is used to organize living things (taxons) into broader and broader categories (taxonomic hierarchies) until you reach the top category, which is every living thing (monotypic).
In everyday life, taxonomy is used to organize things such as animals, foods, places, and occupations. For example, animals can be organized by type (fish, amphibians, reptiles, birds), size (tiny-smallest to biggest), shape (round or angular), and color (variations of black and white).
This organization helps people remember things and use them in situations where they need what they organized. For example, if someone needs a fish for an aquarium but only have amphibians, then they know they do not have a fish but a similar creature.
The knowledge base in the brain is made up of semantic and episodic memory. Semantic memory allows us to learn facts and concepts, while episodic memory allows us to learn about events in our lives.
Semantic memories are organized by concepts, from things like food or politics to the names of people we know and things we own.
Episodic memory allows us to remember things that happen to us, like what we did this past weekend. Both of these forms of memory are maintained by the hippocampal region of the brain.
The other parts of the brain play a role in maintaining other types of information, like personal information (name, birthday, etc.) or facts (like the capital of your state). All of this information is stored in different regions of the brain.
It is important to note that amnesia can affect one or more types of memories. For example, someone could have normal episodic memories but lack implicit memory.
A database is a collection of information that is organized and accessed via specific criteria. Databases are most commonly organized by object, event, person, or place.
For example, a restaurant database would have all the information about restaurants: what they are called, location, prices, types of food, reviews, etc. This would be accessed by name, location, or cuisine type to find the correct information.
These are very useful in society as they organize a lot of data and make it easily accessible. For example, you can look up any restaurant’s details and prices quickly due to the way they are organized in the database.
Databases are used in almost every field in society due to the widespread use of information. Anyone from businessmen to activists use information stored in databases for important decisions and facts.
A relational database is what most search engines and AI use to recognize things, events, people, and places. A relational database maintains information about various types of objects, events, people, and places by linking them together.
These links can be the type of object (apple), event (eating an apple), person (Taylor Swift), or place (the apple orchard).
By linking all of these things together, a search engine or AI can quickly recognize what you are looking for because it can pull from multiple sources at once.
For example, if you were searching for information about Taylor Swift’s birthday party, your search engine would pull up information about the event since it was linked to her name.
A significant part of the brain is dedicated to processing visual information. As such, the way that the brain organizes and processes visual information is a large part of what maintains object-oriented information.
Object recognition is maintained by what’s known as an “object-oriented database” in the brain. This recognizes objects as being individual things with their own distinct features.
For example, a tree is recognized as having distinct features such as its leaves, trunk, and shape, its bark, and its ability to be climbed. These are all considered separate objects within the tree object-oriented database.
These databases are also referred to as semantic memory which maintains information about various types of objects, events, people, and places. All of these things are recognized as individual things with their own traits that make them what they are.
A graph database represents data as a set of nodes connected by sets of directed or undirected arcs. Nodes typically represent entities like people or places, but can also represent events, actions, or anything else that can be named.
These nodes are linked by relationships, which are defined by specific attributes. These attributes define the type of relationship (friendship, employment, etc.) and how the relationship is defined (who knows whom).
By representing data in this way, a graph database allows for complex interactions and relationships between entities to be analyzed more effectively. This is due to the way nodes and relationships are structured – they can be analyzed algorithmically.
Because nodes can represent any kind of entity or thing, there are no limits to what a graph database can store. This means that it is not only useful for social media applications, but for a wide range of uses within the workplace.
In a key-value store, information is organized by keys (which can be considered entity types) and values (which can be any sort of information associated with that key).
As an example, let’s say we had a keyword “apple.” In a dictionary, this word is associated with the definition “a fruit produced by an apple tree.”
In a knowledge base, the word “apple” would represent the key and the definition would be the value. This separation of the key from its corresponding value is what gives the system its ability to organize information in different ways.
For instance, in addition to associating the word “apple” with the definition of a fruit produced by an apple tree, you could add other keys related to different types of apples. These could be differentiated by color, size, flavor, production method (organic or non-organic), etc. The various colors could be grouped together under another key called “color type.