Knowledge Representation Techniques
THE CONCEPTUAL DEPENDENCY(CD)
Cognitive scientists Roger Schank and Robert Abelson first proposed the conceptual dependency knowledge representation framework in the 1970s. The objective of conceptual dependency is to represent the meaning of natural language sentences in a computationally tractable and cognitively plausible manner.
The essence of conceptual dependency is representing a sentence as a network of concepts and their relationships. In the real world, concepts are abstract, high-level entities representing objects, events, and ideas. In contrast, relations characterize how these concepts are associated with one another. For instance, "John gave Mary the book" could be represented as a conceptual dependency network consisting of three concepts (John, Mary, book) and two relations (gave, recipient).
One of the primary benefits of conceptual dependency is its ability to convey the semantics of the natural language in a manner that is independent of the words employed. For instance, the sentences "John gave Mary the book" and "Mary received the book from John" may have distinct surface-level structures, but they can be represented by the same conceptual dependency network.
Conceptual dependency has been implemented in numerous applications, such as natural language understanding, query answering, and dialogue systems. Nonetheless, it has been criticised for its lack of formalisation and the difficulty of constructing large-scale knowledge bases using this method.
Role of CD in Artificial Intelligence
Conceptual Dependency (CD) is a useful framework for representing knowledge in artificial intelligence (AI) systems. Here are some reasons why CD is important in AI:
Natural language processing: CD is well-suited for representing the meaning of natural language sentences in a way that can be understood by AI systems. By representing the concepts and relations between them, CD can provide a semantic representation of language that can be used for tasks such as text classification, sentiment analysis, and question-answering.
Reasoning and decision-making: CD provides a way to represent knowledge at an abstract level, which can be used for reasoning and decision-making tasks. By representing the most fundamental concepts and relations, CD can simplify complex situations and enable AI systems to reason about them in a more efficient and effective way.
Dialogue systems: CD can be used to represent the context of a conversation in a structured way, which can help AI systems to understand and generate natural language responses. By modeling the concepts and relations involved in a conversation, CD can enable dialogue systems to have more natural and coherent conversations with humans.
Domain-specific applications: CD is flexible and can be adapted to a wide variety of domains and applications. By representing knowledge in a domain-specific way, CD can enable AI systems to perform specialized tasks such as medical diagnosis, legal reasoning, and financial analysis.
Overall, the CD is an important framework for representing knowledge in AI systems because it provides a way to represent concepts and relations in a structured, flexible, and intuitive way that can be used for a wide variety of applications.
1. It captures the implicit concept of a sentence and makes it explicit.
2. It helps in drawing inferences from sentences.
3. For any two or more sentences that are identical in meaning. It should be only one representation of meaning.
4. It provides a means of representation which are language independent.
5. It develops language conversion packages.
Primitive Acts in CD
In the context of Conceptual Dependency (CD), primitives are the basic building blocks of the knowledge representation framework. Primitives in CD refer to a small set of abstract concepts that are used to represent the most fundamental aspects of the world. These primitives are intended to be universal and applicable to any domain.
There are three main types of primitives in CD:
Actions: Actions represent basic events that occur in the world, such as "give," "take," or "move." Actions are represented in CD as a verb followed by a set of arguments that describe the participants involved in the event.
Objects: Objects are the basic physical entities in the world, such as "book," "ball," or "car." Objects are represented in CD as nouns.
Attributes: Attributes represent the properties or qualities of objects, such as "color," "size," or "shape." Attributes are represented in CD as adjectives.
Primitives are used to construct more complex concepts and relations in CD. For example, the sentence "John gave Mary the book" can be represented in CD using the primitives "give," "book," "John," and "Mary," along with the relations "Agent(give, John)," "Object(give, book)," and "Recipient(give, Mary)."
The use of primitives in CD allows for a more flexible and efficient knowledge representation system, as well as easier interpretation of natural language input. However, the choice of primitives is subjective and may vary depending on the specific domain or application.
EXAMPLES OF PRIMITIVE ACTS
-- Transfer of an abstract relationship. e.g. give.
-- Transfer of the physical location of an object. e.g. go.
-- Application of a physical force to an object. e.g. push.
-- Transfer of mental information. e.g. tell.
-- Construct new information from the old. e.g. decide.
-- Utter a sound. e.g. say.
-- Focus a sense on a stimulus. e.g. listen, watch.
-- Movement of a body part by owner. e.g. punch, kick.
-- Actor grasping an object. e.g. clutch.
-- Actor ingesting an object. e.g. eat.
-- Actor getting rid of an object from body
Examples of CD
Here are a few examples of how Conceptual Dependency (CD) can be used to represent natural language sentences:
"John threw the ball." This sentence can be represented in CD as follows:
- Action: throw
- Object: ball
- Agent(throw, John)
"The cat chased the mouse." This sentence can be represented in CD as follows:
- Action: chase
- Object 1: cat
- Object 2: mouse
- Agent(chase, cat)
- Patient(chase, mouse)
Conceptual Dependency (CD) has several advantages and disadvantages as a knowledge representation framework.
Advantages of CD:
Flexibility: CD is a flexible framework that can be applied to a wide variety of domains and applications. It is able to represent complex relationships between concepts and can be easily adapted to new domains or situations.
Natural language compatibility: CD is well-suited for representing natural language sentences because it can capture the meaning of sentences at an abstract level that is independent of the specific words used.
Computational efficiency: CD provides a computationally efficient way to represent knowledge because it focuses on the most fundamental concepts and relations, rather than representing every detail of a situation.
Intuitive interpretation: The CD framework is intuitive and can be easily understood by humans. This makes it useful for applications such as natural language processing and dialogue systems.
Disadvantages of CD:
Subjectivity: The selection of primitives and the identification of relations between them can be subjective, which can lead to different interpretations of the same sentence or situation.
Limited expressiveness: CD may not be able to capture all the nuances of complex situations, particularly those involving non-physical concepts such as emotions or abstract ideas.
Lack of formal semantics: CD is not a formally defined framework, which can make it difficult to reason about or validate the correctness of CD-based models.
Ambiguity: The use of abstract concepts in CD can sometimes result in ambiguity, where the meaning of a sentence or situation is not clear or can be interpreted in multiple ways.
Overall, CD provides a useful framework for representing knowledge in a flexible, intuitive, and computationally efficient way. However, its limitations should also be taken into consideration when applying it to specific domains or applications.
Consider a sentence
Ram gives Mary a Book
Here's a pictorial representation of the CD for the sentence "Ram gave Mary a book":
+-----+ gives +-----+
| Ram +---------------> book|
+-----+ Agent +-----+
| Mary| | to |
In this diagram, the node labeled "Ram" represents the object "Ram," while the node labeled "book" represents the object "book." The arc labeled "give" represents the action "give," and the labels "Agent," "Patient," and "Recipient" indicate the roles played by the objects in this action.
The node labeled "Mary" represents the recipient "Mary," and the labeled arc "to" indicates the relation between the "give" action and this recipient.
Overall, this pictorial representation of CD provides a visual way to understand the relationships between concepts and relations in natural language sentences.