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.

1. Unit-4
Structural Knowledge Representation

2. Predicate logic or First-order predicate logic.
Constant 1, 2, A, John, Mumbai, cat,....
Variables x, y, z, a, b,....
Predicates Brother, Father, >,....
Function sqrt, LeftLegOf, ....
Connectives ∧, ∨, ¬, ⇒, ⇔
Equality ==
Quantifier ∀, ∃
Basic Elements of First-order logic:

11. • Marvin Minsky in the book on Computer Vision proposed frames as a means of
representing common-sense knowledge.
• In that Minsky proposed that knowledge is organized into small “packets” called
frames.
• The contents of the frame are certain slots which have values. All frames of a given
situation constitute the system.
• Whenever one encounters a situation, a series of related frames are activated and
reasoning is done.
• “A Frame can be defined as a data structure that has slots for various objects and a
collection of frames consists of expectations for a given situation.”
• A Frame structure provides facilities for describing objects, facts about situation,
procedures on what to do when a situation is encountered.
• Because of these facilities a frame provides, frames are used to represent the two
types of knowledge, viz., declarative/factual and procedural.
Frames

13. A Script is a knowledge representation structure that is extensively used for describing stereo-type
sequences of actions”.
The following fig. represents a miniature restaurant script with customer going to a restaurant, ordering
some eatables, eating them, paying the due amount and leaving the restaurant.
Script

14. Semantic Nets
Semantic networks are an alternative to predicate logic as a form of knowledge
representation. The idea is that we can store our knowledge in the form of a graph, with
nodes representing objects in the world, and arcs representing relationships between those
objects.

15. is intended to represent the data:
Tom is a cat.
Tom caught a bird.
Tom is owned by John.
Tom is ginger in colour.
Cats like cream.
The cat sat on the mat.
A cat is a mammal.
A bird is an animal.
All mammals are animals.
Mammals have fur.
• It is argued that this form of representation is closer to the way humans structure
knowledge by building mental links between things than the predicate logic we
considered earlier.
• Note in particular how all the information about a particular object is concentrated on
the node representing that object, rather than scattered around several clauses in
logic.
• Tom is a cat is represented by Cat(Tom)
• The cat sat on the mat is represented by ∃x∃y(Cat(x)∧Mat(y)∧SatOn(x,y))
• A cat is a mammal is represented by ∀x(Cat(X)→Mammal(x))

16. Knowledge Representation Schemes
1. Logical Representation Scheme:
• This class of representation uses expressions in formal logic to represent a knowledge
base.
• Inference rules and proof procedures apply this knowledge to problem solving.
• First order predicate calculus is the most widely used logical representation scheme, and
PROLOG is an ideal programming language for implementing logical representation
schemes.
2. Procedural Representation Scheme:
• Procedural scheme represents knowledge as a set of instructions for solving a problem.
• In a rule-based system, for example, an if then rule may be interpreted as a procedure
for searching a goal in a problem domain: to arrive at the conclusion, solve the premises
in order.
• Production systems are examples of a procedural representation scheme.

17. 3. Network Representation Scheme:
• Network representation captures knowledge as a graph in which the nodes
represent objects or concepts in the problem domain and the arcs represent
relations or associations between them.
• Examples of network representations include semantic network, conceptual
dependencies and conceptual graphs.
4. Structured Representation Scheme:
• Structured representation languages extend networks by allowing each node to be a
complex data structure consisting of named slots with attached values.
• These values may be simple numeric or complex data, such as pointers to other
frames, or even procedures.

22. Parse Tree:

25. NLP Applications
Speech Recognition
Speech Recognition is a technology that enables the computer to convert voice input data to
machine readable format. There are a lot of fields where speech recognition is used like, virtual
assistants, adding speech-to-text, translating speech, sending emails etc.
Voice Assistants and Chatbots
All of us are well versed with the idea of Voice assistants like Alexa, Siri and Google Assistant,
and chatbots that are integrated in many websites to help and guide new users. Voice assistant is
a software that uses NLP and speech recognition to understand voice commands of a user and
perform accordingly.
Auto Correct and Auto prediction
There are many softwares available nowadays that check grammar and spelling of the text we
type and save us from embarrassing spelling and grammatical mistakes in our emails, texts or
other documents. NLP plays an important role in those softwares and functions.

26. Email Filtering
Most of the professional work is done through emails and it would be quite a hassle if all the
emails we received were not segregated into different sections. Gmail classifies all the emails
into primary, social and promotional sections. Even all the spam emails are sent to a different
section so that they do not flood our inbox.
Sentiment Analysis
Human speech could be quite hard to interpret as it involves expressions and sentiments
beyond literal meanings. Expressions like sarcasm, threat, exclamation etc. are often very
hard to be recognised by the computer.
Advertisement to Targeted Audience
If you ever search any product or object in any shopping site, you would often see ads of
those products and other related products on other sites. This type of targeted online
advertising is done with the help of NLP and it is known as Targeted Advertising.

27. Translation
Social Media has brought the entire world together but with unity comes challenges
like language barrier. With different translating softwares that work individually or are
integrated within other applications, this hurdle has been easily defeated.
Social Media Analytics
Social Media is an integral part of everyone’s life nowadays and many people use it to
post their thoughts about different businesses and products. The companies can
understand their market position and get their customer reviews by analyzing the
data.
Recruitment
NLP has made the job easier by filtering through all the resumes and shortlisting the
candidates by different techniques like information extraction and name entity
recognition. It goes through different attributes like Location, skills, education etc. and
selects candidates who meet the requirements of the company closely.

28. Text Summarisation
There is a huge amount of data available on the internet and it is very hard to go
through all the data to extract a single piece of information. With the help of NLP, text
summarization has been made available to the users. This helps in the simplification of
huge amounts of data in articles, news, research papers etc.

29. The top 7 techniques Natural Language Processing (NLP) uses to extract data from text are:
1. Sentiment Analysis
This is the dissection of data (text, voice, etc) in order to determine whether it’s positive,
neutral, or negative.
2. Named Entity Recognition
NER (because we in the tech world are huge fans of our acronyms) is a Natural Language
Processing technique that tags ‘named identities’ within text and extracts them for further
analysis.
3. Text Summary
This is a fun one. Text summarization is the breakdown of jargon, whether scientific, medical,
technical or other, into its most basic terms using natural language processing in order to
make it more understandable.
4. Topic Modeling
Topic Modeling is an unsupervised Natural Language Processing technique that utilizes
artificial intelligence programs to tag and group text clusters that share common topics.

30. 5. Text Classification
Again, text classification is the organizing of large amounts of unstructured text (meaning
the raw text data you are receiving from your customers). Topic modeling, sentiment
analysis, and keyword extraction (which we’ll go through next) are subsets of text
classification.
6. Keyword Extraction
The final key to the text analysis puzzle, keyword extraction, is a broader form of the
techniques we have already covered. By definition, keyword extraction is the automated
process of extracting the most relevant information from text using AI and machine learning
algorithms.
7. Lemmatization and Stemming
Lemmatization considers the context and converts the word to its meaningful base form,
which is called Lemma. For instance, stemming the word 'Caring' would return 'Car‘.
Stemming is a process that stems or removes last few characters from a word, often leading
to incorrect meanings and spelling.

31. Recursive Transition Networks (RTN)
• RTNs are considered as development for finite state automata with some essential
conditions to take the recursive complexion for some definitions in consideration.
• A recursive transition network consists of nodes (states) and labeled arcs (transitions).
• It permits arc labels to refer to other networks and they in turn may refer back to the
referring network rather than just permitting word categories.
• It is a modified version of transition network. It allows arc labels that refer to other
networks rather than word category.

32. POP: indicates that input string has been accepted by the network. In RTN, one state is
specified as a start state. A string is accepted by an RTN if a POP arc is reached and all the
input has been consumed. Let us consider a sentence “The stone was dark black”.
Here The: ART
Stone: ADJ NOUN
Was: VERB
Dark: ADJ
Black: ADJ NOUN
The RTN structure is given in figure

33. Augmented Transition Network (ATN)
• An ATN is a modified transition network. It is an extension of RTN. The ATN uses a top
down parsing procedure to gather various types of information to be later used for
understanding system.
• It produces the data structure suitable for further processing and capable of storing semantic
details.
• An augmented transition network (ATN) is a recursive transition network that can perform
tests and take actions during arc transitions.
• An ATN uses a set of registers to store information.
• A set of actions is defined for each arc and the actions can look at and modify the registers.
An arc may have a test associated with it.
• The arc is traversed (and its action) is taken only if the test succeeds.