Weak Slot and Filler Structure (by Mintoo Jakhmola LPU)
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Weak slot and filler structures allow for quick retrieval of attribute values, easy description of relation properties, and embrace aspects of object-oriented programming. A slot is an attribute-value pair, and a filler is a value that a slot can take. Semantic nets represent concepts as nodes and relationships as labeled arcs, and the meaning of a concept comes from its relations to other concepts. Frames represent real-world entities as collections of attributes and values through a hierarchical class structure.
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This document discusses weak slot-and-filler knowledge representation structures. It describes how slots represent attributes and fillers represent values. Semantic networks are provided as an example where nodes represent objects/values and links represent relationships. Property inheritance allows subclasses to inherit attributes from more general superclasses. Frames are also discussed as a type of weak structure where each frame contains slots and associated values describing an entity. The document notes challenges with tangled hierarchies and provides examples of how to resolve conflicts through inferential distance in the property inheritance algorithm.
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weak slot and filler
Slot and filler structures represent knowledge through attributes (slots) and their associated values (fillers). Weak slot and filler structures provide little domain knowledge. Frames are a type of weak structure where a frame contains slots describing an entity. Semantic networks also represent knowledge with nodes and labeled links, allowing inheritance of properties through generalization hierarchies. Both frames and semantic networks enable quick retrieval of attribute values and easy description of object relations, but semantic networks additionally allow representation of non-binary predicates and partitioned reasoning about quantified statements.
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weak slot and filler
Slot and filler structures represent knowledge through attributes (slots) and their associated values (fillers). Weak slot and filler structures provide little domain knowledge. Frames are a type of weak structure where a frame contains slots describing an entity. Semantic networks also represent knowledge with nodes and labeled links, allowing inheritance of properties through generalization hierarchies. Both frames and semantic networks enable quick retrieval of attribute values and easy description of object relations, but semantic networks additionally allow representation of non-binary predicates and partitioned reasoning about quantified statements.
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Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
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This document discusses weak slot and filler structures in artificial intelligence. It describes semantic net representation, which represents knowledge as a graphical network of nodes and arcs. It provides examples of representing statements about a cat named Jerry in a semantic net. The document also discusses frame representation, which organizes knowledge into structured records called frames that contain slots and slot values. An example frame is provided for a person named Ram. Advantages and disadvantages of both semantic nets and frames are outlined.
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FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
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The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.
Weak Slot and Filler Structures
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, Recursive Best First Search, Pruning the CLOSED and OPEN
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1) This document discusses semantic networks, which are a knowledge representation technique used in artificial intelligence. Semantic networks represent knowledge through nodes and links, where nodes represent concepts or objects, and links represent relationships between the nodes.
2) As an example, a simple semantic network is presented representing facts about a cat named Jerry - that Jerry is a cat, a mammal, owned by Jay, white in color, and likes cheese.
3) The document outlines different types of semantic networks including definitional, assertional, implicational, and learning networks. It also discusses advantages such as being a natural representation of knowledge, and disadvantages including lack of quantifiers and lack of intelligence.
Frame-Script and Predicate logic.pptx
The document discusses different knowledge representation techniques in natural language processing, including:
1. Frames, which represent knowledge as "packets" of information called frames that have slots with values.
2. Scripts, which describe stereotypical sequences of actions.
3. Semantic nets, which represent knowledge as a graph with nodes for objects and arcs for relationships.
4. Knowledge representation schemes like logical, procedural, network, and structured representations.
5. Parsing techniques including recursive transition networks and augmented transition networks.
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- 1. Weak Slot and Filler Structures
- 2. Why use this data structure? • It enables attribute values to be retrieved quickly • Properties of relations are easy to describe . • It allows ease of consideration as it embraces aspects of object oriented programming.
- 3. WHAT • A slot is an attribute value pair in its simplest form. • A filler is a value that a slot can take -- could be a numeric, string (or any data type) value or a pointer to another slot. • A weak slot and filler structure does not consider the content of the representation.
- 4. SEMANTIC NETS The major idea is that: • The meaning of a concept comes from its relationship to other concepts, and that, • The information is stored by interconnecting nodes with labeled arcs. • Semantic nets initially we used to represent labeled connections between nodes.
- 5. Representation in a Semantic Net These values can also be represented in logic as: isa(person, mammal), instance(Mike-Hall, person) team(Mike-Hall, Cardiff)
- 6. As a more complex example consider the sentence: John gave Mary the book. Here we have several aspects of an event.
- 7. Intersection search • The notion that spreading activation out of two nodes and finding their intersection finds relationships among objects. This is achieved by assigning a special tag to each visited node.
- 8. INHERITANCE • The isa and instance representation provide a mechanism to implement this. • Inheritance also provides a means of dealing with default reasoning. E.g. we could represent: • Emus are birds. • Typically birds fly and have wings. • Emus run.
- 9. A Semantic Network for a Default Reasoning
- 10. In making certain inferences we will also need to distinguish between the link that defines a new entity and holds its value and the other kind of link that relates two existing entities. Consider the example shown where the height of two people is depicted and we also wish to compare them. We need extra nodes for the concept as well as its value.
- 11. Comparison of two heights
- 12. FRAMES • A frame is a collection of attributes or slots and associated values that describe some real world entity. Frames on their own are not particularly helpful but frame systems are a powerful way of encoding information to support reasoning. Set theory provides a good basis for understanding frame systems. Each frame represents: • a class (set), or • an instance (an element of a class).
- 13. A simple frame system
- 14. Frame system • Person isa: Mammal Cardinality: ……. • Adult-Male isa: Person Cardinality: ……. • Rugby-Player isa: Adult-Male Cardinality: …….
- 15. • Here the frames Person, Adult-Male, Rugby- Player are all classes and the frames Cardiff- RFC are instances.