Ontology in AI: Meaning, Example, Types, Use-application. In Artificial Intelligence, ontology relates to a shared vocabulary for researchers. It includes definitions of the basic concept and the relation between them which are machine-interpretable.
Table of Contents
Introduction
Ontology in AI allows the commands of the system to use the contents and relationships between them to make the speculation that imitates human behavior.
If talking about ontology alone then it is a branch of philosophy that deals with the study of existence and being. Moreover, practical business refers to construction that binds different sources of information and consists of interconnecting data from multiple domains. It can be used as a means of resolving organizational differences to enhance integration between databases.
Meaning
Let’s have a look at the meaning of ontology in Artificial Intelligence. Rapid advancement has been seen in Artificial Intelligence and its branches such as deep learning, and machine learning in the previous years.
The work of Artificial Intelligence is that it extracts relevant information and generates insights from data to find suitable solutions which have been observed throughout the discovery of Artificial Intelligence.
But the running of these programs is not easy as it requires algorithms, data, and code, and its translation into something meaningful requires data science.
With the help of data sciences, organizations can communicate with customers, stakeholders, track, and analyze the trend. Presence of Ontology in this case with relevant terms and connections from a specific domain the process of identifying the concept, improving the concept, and unifying of data for critical information becomes streamlined.
A common misconception is that machine learning gets better with more data however many researchers have denied this claim. With stepping forward the businesses realize that too much can be overwhelming to analyze, destroying the complexity of the value and higher investments in terms of money and time both.
According to the studies, 85% of projects of Artificial Intelligence fail because of this reason. It is because of a lack of understanding of how a large amount of data can be utilized. If we focus them in this direction, the ontologies mark the remarkable differential.
Ontological modeling can help an AI system to broaden its scope. It can include any type of data and whether the data is in a structured, unstructured, or unstructured format. It smooths the data integration. It can give a vast amount of data as input. The benefit of ontology is that it can diverse organizations in different industries setting out unique goals.
According to Enterprises, AI should be able to allow faster digital transformation means the ability to rapidly scale is essential. Companies can enable this by developing a repeatable framework instead of using a chain of data in one location that could grow through existing associative relationships.
For organizations to power, their AI projects Ontology can help provide a reusable, and adaptive structure.
Example
Ontology-based Artificial Intelligence can result in extremely targeted results and it does not require training sets to become functional as well. Regarding AI, different ontologies exist that can be considered as examples.
For instance, a natural processing system can make use of ontologies to decide that the word cat and dog are semantically similar. Two of the prominent ontologies are present in Ontolingua and Cyc. An abstract layer of Wordnet (lexical database) can be considered as a simple ontology.
Ontology-based AI emulates human performance and allows the system to make inferences based on content and relationships.
Types
Knowledge models are of different types that run along a continuum beginning from the simplest level in which a controlled vocabulary is introduced for the encouragement of the use of the same for a particular meaning. There are different types of ontologies that are used in Artificial Intelligence. Some of them are:
The Semantic Web
The semantic web is a way through which machine-interpretable knowledge is allowed to be distributed on the Web. These pages are also meant to be read by humans instead of just providing HTML pages. These websites also provide information that can be used by computers.
RDF
RDF allows the sentences to be reified in their languages which means it can be presenting arbitrary logical formulas which are generally undecidable. To be undecidable is not necessarily a bad thing, it is just that computation time that the system may take cannot be fixed. For instance, simple logic programs with function symbols, and all the programming languages are virtually undecidable.
XML
XML or Extensive Markup language is used for providing a machine-readable syntax design. Moreover, humans can read it too. It is a text-based language in which items are placed hierarchically. The syntax for XML is quite complicated however at the simple level, the tag is either in the form <tag…/>.
URI
A URI or Uniform Resource Identifier is used for the unique identification of a resource. A resource can be anything carrying a unique identity. A URI is a string referring to a resource that may include a person, a web page, or a corporation however commonly URIs use the syntax of a web address.
Use-Application: Ontology in AI
In Artificial Intelligence Ontology has widespread uses as it helps to improve the quality of data for training datasets. It provides more coherent and easy navigation when users wish to move from one concept to another in ontology structure.
On the other hand, interestingly ontology can be used for the creation of a knowledge graph for the set of individual facts. A piece of knowledge can be described as a set of entities where nodes and edges between the nodes explain the type and relationship between them.
A recent uptake has been seen in expressing ontologies with the use of ontology in languages such as Web Ontology Language (OWL). A domain-specific ontology is a combination with AI-driven tools for data analytics which can serve relevant data and uncover new data trends and patterns. It means that ontology can fit every organization’s goal which can either be logical, semantic, mathematical based approaches.
What is an ontology in the context of Artificial Intelligence?
An ontology in AI refers to a formal, explicit specification of a shared conceptualization. It’s a structured representation of knowledge about a particular domain, defining the concepts, relationships, and properties within that domain. Think of it as a blueprint of knowledge, outlining the categories of things that exist, how they relate to each other, and the rules that govern those relationships. Unlike a simple database, an ontology goes beyond storing data; it captures the meaning of the data, enabling AI systems to reason and make inferences.
This shared understanding is crucial for AI to process information intelligently, allowing it to understand the world in a more human-like way. Ontologies provide a common vocabulary and framework for different AI systems to communicate and share knowledge effectively, which is essential for collaborative problem-solving. They provide a standardized way to represent knowledge, making it easier to integrate different systems and datasets.
How does an ontology differ from a taxonomy?
While both ontologies and taxonomies deal with organizing knowledge, they differ in scope and complexity. A taxonomy is a hierarchical classification of things, often based on observed characteristics. Think of the Linnaean system for classifying living organisms (Kingdom, Phylum, Class, etc.). It focuses primarily on categorizing entities into a tree-like structure. An ontology, on the other hand, is much richer and more expressive.
It not only classifies entities but also defines the relationships between them, their properties, and the rules that govern them. For example, an ontology about animals might not only classify them by species but also define relationships like “eats,” “lives in,” and “is a predator of,” along with properties like “has fur” or “lays eggs.” Essentially, a taxonomy is a subset of an ontology, focusing primarily on hierarchical classification, while an ontology provides a more comprehensive and nuanced representation of knowledge.
What is the purpose of using ontologies in AI?
Ontologies serve several crucial purposes in AI. Firstly, they enable knowledge sharing and reuse. By providing a standardized representation of a domain, ontologies allow different AI systems to understand and exchange information without ambiguity. Secondly, they facilitate reasoning and inference. The explicit representation of relationships and rules within an ontology allows AI systems to draw logical conclusions and make informed decisions.
For instance, if an ontology states that “all dogs are mammals” and “all mammals have fur,” an AI system can infer that “all dogs have fur.” Thirdly, ontologies improve the accuracy and efficiency of AI applications. By providing a structured knowledge base, they help AI systems to better understand the context of information and avoid misinterpretations. Finally, they enhance the explainability of AI systems. By making the underlying knowledge explicit, ontologies make it easier to understand how an AI system arrived at a particular conclusion.
Can you give a simple example of an ontology?
Imagine an ontology for the domain of “fruits.” It would define the concept of “fruit” and its properties, such as “edible,” “contains seeds,” and “grows on a plant.” It would also define different types of fruits, like “apple,” “banana,” and “orange,” and their specific properties, such as “red,” “yellow,” and “orange,” respectively. The ontology would also specify relationships between these concepts, such as “an apple is a fruit,” “a banana is a fruit,” and “an apple grows on an apple tree.”
This simple ontology allows an AI system to understand the relationships between different types of fruits and their properties. For example, if the system is told that “a Granny Smith is an apple,” it can infer that “a Granny Smith is a fruit,” “a Granny Smith is edible,” and “a Granny Smith contains seeds.”
What are the different types of ontologies?
Ontologies can be categorized in several ways. One common classification distinguishes between top-level ontologies, domain ontologies, task ontologies, and application ontologies. Top-level ontologies, also known as foundational ontologies, describe very general concepts like time, space, and existence. Domain ontologies capture knowledge specific to a particular domain, such as medicine or finance. Task ontologies describe knowledge related to specific tasks, such as planning or diagnosis.
Application ontologies are developed for specific applications, combining elements from different types of ontologies. Another categorization focuses on the level of formality, ranging from lightweight ontologies, which are simple and informal, to heavyweight ontologies, which are complex and highly formal.
How are ontologies created?
Creating an ontology is a complex process that often involves collaboration between domain experts and knowledge engineers. It typically starts with identifying the scope and purpose of the ontology. Then, the key concepts and relationships within the domain are identified and defined. Formal languages, such as OWL (Web Ontology Language), are often used to represent the ontology.
The ontology is then evaluated and refined through various techniques, such as competency questions and validation against real-world data. Ontology creation can be a manual process, but there are also tools and methodologies that can automate some aspects of the process, such as extracting concepts and relationships from text or databases.
What is OWL (Web Ontology Language)?
OWL is a family of markup languages used to create ontologies. It’s based on RDF (Resource Description Framework) and provides a richer set of constructs for representing knowledge than RDF alone. OWL allows for defining classes, properties, and relationships between them. It also supports reasoning and inference through its formal semantics. OWL is widely used in the Semantic Web and is a key technology for enabling machine-readable knowledge representation. It provides different levels of expressiveness, allowing developers to choose the appropriate level for their needs.
What are some applications of ontologies in AI?
Ontologies have numerous applications in AI, including knowledge management, natural language processing, information retrieval, and machine learning. In knowledge management, ontologies are used to organize and manage large amounts of information, making it easier to find and retrieve relevant data. In natural language processing, ontologies help AI systems understand the meaning of text and resolve ambiguities. In information retrieval, ontologies are used to improve the accuracy of search results by matching queries to relevant concepts.
In machine learning, ontologies can be used to provide background knowledge to learning algorithms, improving their performance.
How do ontologies contribute to machine learning?
Ontologies can significantly enhance machine learning in several ways. They can provide background knowledge to learning algorithms, allowing them to learn more effectively from smaller amounts of data. For example, an ontology about animals can help a machine learning algorithm classify images of animals even if it has seen only a few examples of each species. Ontologies can also be used to improve the accuracy of machine learning models by providing constraints and relationships between concepts. Furthermore, they can help make machine learning models more interpretable by providing a clear representation of the underlying knowledge.
What are the challenges of developing ontologies?
Developing ontologies can be challenging due to several factors. One challenge is the complexity of the domains being modeled. Many real-world domains are complex and involve a large number of concepts and relationships, making it difficult to capture all the relevant information in an ontology. Another challenge is the subjectivity involved in defining concepts and relationships. Different experts may have different views on how a domain should be represented, leading to disagreements and inconsistencies in the ontology. Furthermore, maintaining and updating ontologies can be a significant effort, as the domain knowledge may evolve over time.
How are ontologies evaluated?
Ontologies can be evaluated using various techniques. One common approach is to use competency questions, which are questions that the ontology should be able to answer. The ontology is evaluated based on its ability to answer these questions correctly. Another approach is to compare the ontology to existing knowledge bases or datasets. The ontology is evaluated based on its consistency with these resources. Ontologies can also be evaluated by domain experts, who can assess the accuracy and completeness of the ontology.
What is the difference between a formal ontology and an informal ontology?
Formal ontologies are characterized by their rigorous and explicit representation of knowledge, often using formal languages like OWL. They are designed for automated reasoning and inference by computer systems. Informal ontologies, on the other hand, are less rigorous and may be represented using natural language or semi-formal notations. They are often used for communication and knowledge sharing among humans. Formal ontologies are more suitable for applications that require automated reasoning, while informal ontologies are more suitable for applications that require human understanding.
What are some tools for developing ontologies?
Several tools are available for developing ontologies, including Protégé, TopBraid Composer, and WebODE. Protégé is a popular open-source tool that provides a graphical interface for creating and editing ontologies. TopBraid Composer is a commercial tool that offers a more comprehensive set of features for ontology development. WebODE is a web-based tool that allows for collaborative ontology development. These tools provide various functionalities, such as creating classes and properties, defining relationships, and performing reasoning.
How do ontologies relate to the Semantic Web?
Ontologies are a core component of the Semantic Web, which aims to make the web more machine-readable and understandable. Ontologies provide the formal structure and vocabulary needed to represent knowledge on the web, enabling machines to understand the meaning of web content. By using ontologies, web resources can be annotated with semantic information, making it easier for machines to find, retrieve, and process information. The Semantic Web relies heavily on ontologies to enable knowledge sharing and interoperability between different systems.
What is the future of ontologies in AI?
The future of ontologies in AI is bright, with ongoing research and development focusing on several key areas. One area of focus is improving the scalability and efficiency of ontology development and reasoning. As the amount of data and knowledge continues to grow, it’s crucial to develop methods for creating and managing large-scale ontologies. Another area of research is making ontologies more dynamic and adaptable to changing environments.
Real-world knowledge is constantly evolving, so ontologies need to be able to adapt to these changes. Furthermore, there’s a growing interest in integrating ontologies with other AI technologies, such as machine learning and deep learning. Combining the strengths of ontologies with these techniques can lead to more powerful and intelligent AI systems.
Finally, there’s a push towards developing more user-friendly tools and methodologies for ontology development, making it easier for domain experts and non-technical users to create and use ontologies. The increasing importance of knowledge representation and reasoning in AI suggests that ontologies will play an even greater role in the future of intelligent systems.
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External resource
- W3C. (2004). OWL Web Ontology Language Overview. W3C Recommendation.
https://www.w3.org/TR/owl-features/ - Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. Stanford University Knowledge Systems Laboratory Technical Report KSL-01-05.
http://protege.stanford.edu/publications/ontology_development/ontology101.pdf - Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The Semantic Web. Scientific American, 284 (5), 34-43.
https://www.scientificamerican.com/article/the-semantic-web/ - Stanford Encyclopedia of Philosophy. (2023). Ontology in AI and computer science.
https://plato.stanford.edu/entries/ontology-ai/
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