Relationship between the Semantic Web and NLP

Relationship between the
Semantic Web and NLP

Rajendra Akerkar
Technomathematics Research Foundation,
Kolhapur, India

March 17, 2009 Akerkar: Sogndal Lecture 1

Structure of this talk
 Relationship between NLP and SW
 Inspiration: QA system and H
I i ti t d Haystack
t k
 RDF Schema & NL Annotations
 Information Access Schemata
 Information Planning Schemata
 Integration
 Conclusion


The sense of the relationship

 Could the Semantic Web enhance the
technical level of NLP technologies?

 Could NLP technologies help in delivering
and using a better Semantic Web?
g


Purpose of the Semantic Web

 to help users
 locate,
 organize,
organize and
 process information.

 belief:
 It should be grounded in the information access
method humans are comfortable with
— natural language.


Why natural language?

 It is intuitive
intuitive,
 easy to use and rapidly deployable, and
 no specialized training
training.


Vision
 The Semantic Web equally accessible by
computers using specialized languages and
interchange formats, and humans using
natural l
t l language.
 Ask a computer: “when was the king of Norway
born?
born?”
 “What’s the cheapest flight to the Mumbai this
month?
month?”
 Retrieve “exact information”.


What synergistic opportunities exist between natural
language technology and the S
l h l d h Semantic W b?
i Web?
 State of the art
State-of-the-art NL systems are capable of
providing users intuitive access to a wealth of
textual data using ordinary language.
 However, such systems are often hampered
by
 the knowledge engineering bottleneck (wrappers,
integrate new data source),
 knowledge integration (from multi. Sources), and
g g )
 time consuming.
 Here Semantic Web comes in …


Semantic Web research

 Constructing, integrating, packaging,
Constructing integrating packaging and
exporting segments of knowledge to be
usable by the entire world.
y
 NL technology can tap into this knowledge
framework
 In return provides natural language information
access for the Semantic Web.


SW: What is missing?

 Where in the loop is the human?
 How will we communicate with our software agents?
 How will we access information on the Semantic Web?

Obviously, we cannot expect ordinary Semantic Web users to
manually manipulate ontologies, query with formal logic
expressions, etc.
i t
We would like to communicate with software agents in natural
language…

 What is the role of natural language in the Semantic
Web?


Mechanism for integrating NL into the
RDF
 Augmenting RDF property definitions
 Creating Information Access Schemata
 To bridge gap between NL & RDF
 Extension to mirror human question
answering behaviour in the form of NL query
plans.


Inspiration

 Question Answering
Question-Answering (QA) System

 Haystack System
 End user semantic web platform
 aggregates all user s information into a unified
user’s
repository.


QA system

 The use of metadata is a common technique
for rendering information fragments more
tenable to processing by computer systems.

 Our approach
 natural language itself as metadata
 numerous advantages and opportunities.
 preserves h
human readability and
d bilit d
 encourages non-expert users to engage in metadata
creation.


QA system

 Natural language annotations
 machine-parsable sentences and phrases that
describe the content of various i f
d ib th t t f i information
ti
segments.
 annotations serve as metadata
 describe the kinds of questions a particular piece of
knowledge is capable of answering.
 Contains natural language annotation
technology


QA system

 “For pioneering contributions to the theory and
For
practice of optimizing compiler techniques that laid
the foundation for modern optimizing compilers and
automatic parallel execution.” F
t ti ll l ti ” Frances E All was
Allen
selected for Turing award 2006.

 Annotation:
 Frances E Allen is selected for Turing award in 2006.
 2006 Turing award


QA system

 The annotations allow system to answer:

 What award did Allan receive in 2006?
 Who was selected for the Turing award in 2006?
 To whom was the Turing award given in 2006?


QA system

 Feature of natural language annotations
 any information segment can be annotated:
 not only text, but also images, multimedia …
y , g ,
 To provide uniform access to semi-structured
resources on the Web
 a virtual database system
 integrates Web sources under a single query interface.


Haystack

 Aggregates a user’s information into a unified
user s
repository.
 e mail,
e-mail, documents, calendar, and web pages.
 It is presented using RDF
 makes it easy for agents to access filter and
access, filter,
process this information in an automated fashion.


Haystack

 “Present Tim the letter from the secretary I
Present
met with last Tuesday from TMRF.”
 Current IT allows to store all info to answer the
query
 Scattered amongst multiple systems
 Agent need to communicate with
 Email client
 Calendar
 File system
 Directory server


Haystack

 Reduce the protocol barriers to information—
information
standardizing on RDF as a common model
for information—
 agents are free to mine the semantics of a user’s
various data sources
 End-user
End user application for managing
information
 serves as a powerful platform for experimenting
with various information retrieval and user
interface research problems


QA System & Haystack

 By incorporating natural language search
capabilities into Haystack
 Demonstrate
 the usefulness of natural language search
 show its applicability to the Semantic Web


To endow Haystack with the ability to
answer
 What is the state bird of India?
 Tell me what the vision statement of TMRF is.
 Do you know Sogndal’s population?
Sogndal s

 Easy on Web
 But, for this data to be usable by any
Semantic Web system it must be
system,
restructured in terms of the RDF model.


Adenine

 To facilitate frequent manipulation of RDF
data, Haystack’s programming language.
 Features of Lisp, Python, and Notation3.
 Basic data unit is the RDF triple.


Adenine :State class and the :bird property
@prefix dc: <http://purl.org/dc/elements/1.1/>
@prefix : <www.tourindia.com/data#>

add { :State Triples
T i l are enclosed i curly
l d in l
rdf:type rdfs:Class ;
rdfs:label "State" braces { } and expressed in
} subject-predicate-object order.
add { :bird
rdf:type rdf:Property ; semicolon denotes the predicate-
rdfs:label “State bird" ;
rdfs:domain :State object pair is to assume the last
} used subject
subject.
# ... more property declarations

add { :india
rdf:type :State ;
dc:title “India" ; RDF literals are written as strings in double quotes
:bird “Peacock" ;
:flower “Lotus" ;
:population "1,147,995,904"
# ... more information about India and its states
}


Adenine unique feature

 Every Adenine instruction is encoded as a
node in the RDF graph, and a sequence of
instructions is expressed by adenine:next
p y
arcs between these instruction nodes.

 As a result, data and procedures can be
embedded within the same RDF graph and
can be distributed together.


The connection between the RDF schema and the
NL annotations in natural language schema
i i t ll h
@prefix nl: <http://www.tmrfindia.org/sw/projects/enlight#>
add { :stateAttribute
rdf:type nl:NaturalLanguageSchema ;
# This annotation handles cases like "[state bird] of [India]"
# and "[population] of [India]".
nl:annotation @( :attribute "of" :state ) ; The definition of :attribute
# Code to run to resolve state attribute restricts the resource representing
nl:code :stateAttributeCode the attribute to be queried to have
} type rdf:Property.
df P t
add { :attribute
rdf:type nl:Parameter ;
nl:domain rdf:Property ; The rdfs:label property to resolve the actual literal,
nl:descriptionProperty rdfs:label e.g., “State bird” or “population”.
}
add { :state
:state restricts the resource to have type
rdf:type nl:Parameter ;
nl:domain :State ; :State and to have the resolver dc:title
nl:descriptionProperty dc:title
}
# The identifier [state] will be bound to the value of the named
# parameter :state. The identifier [attribute] will be bound to the
# value of the named parameter :attribute.
method :stateAttributeCode :state = state :attribute = attribute
# Ask the system what the [attribute] property of [state] is
return (ask %{ attribute state ?x })


Question Answering
 What is the state bird of India?

 System parses the question and determines that
:stateAttribute is the relevant natural language schema to
invoke.
i k
 System extracts the natural language bindings of :attribute
and :state, which are “state bird” and “India”, respectively. This
is further resolved into the RDF resources :bird and :india
:india.
 As a response to the question, the method
:stateAttributeCode is invoked with named parameter
:attribute bound to :bird and named parameter :state
p
bound to :india.
 The invoked method performs a query into Haystack’s RDF store,
which returns “Peacock”, the state bird of India.


User query is parsed by QA System

 So,
So a single natural language annotation is
capable of answering a question.

 QA system is capable of normalizing different
methods for requesting the same information
information.
 imperative (“Tell me...”),
 interrogative (“What is ”)
( What is... ).


Natural language schema
add { :stateAttribute
rdf:type nl:NaturalLanguageSchema The method invoked by the
; NLS queries the RDF store for
q
nl:annotation @( :state " has the the resource of type :State
largest " :comparisonAttribute that contains the maximal
) ;
integer value for the property
nl:code
:maxComparisonAttributeCode given by
}
:comparisonAttribute.

p
method :maxComparisonAttributeCode
:comparisonAttribute = attribute
return (ask %{ Allow our system to answer the
rdf:type ?x :State , following questions:
adenine:argMax ?x ?y 1 xsd:int • Which state has the lowest
population?
%{
• Do you know what state has the
:attribute ?x ?y largest area?
}
} @(?x))


QA System

 Built a prototype implementing the natural
language schemata.
 Limited in the types of questions that it can
answer and the domain.

 However, proof of concept that demonstrates
a method of marrying natural language with
the Semantic Web.


Further integrating natural language
technology with the Semantic Web
 RDF triples ≈ System’s ternary expression
representation of NL.

 Clipping natural language annotations directly
into
i t rdf:Property d fi iti
definitions.

 Consider a piece of an ontology modeling an
address book entry in Haystack:


A natural language-aware software agent could answer questions…

add { :Person
rdf:type rdfs:Class The :homeAddress is a property
} specifying a user’s home address.
add { :homeAddress
rdf:type rdf:Property ;
rdfs:domain :Person ;
rdfs:range xsd:string ;
Annotation
nl:annotation @( nl:subject " lives at " expresses this
nl:object ) ; connection
nl:annotation @( nl:subject "’s home address is " concretely in
nl:object ) ; natural language,
nl:annotation @( nl:subject "’s bungalow" ) ; via the
nl:generation @( nl:subject "’s home address is nl:annotation
nl:object ) property.
}

The phrase “nl:subject lives at nl:object” is linked to every RDF
statement involving the :homeAddress property, where nl:subject is
shorthand for indicating the subject (domain) of the relation, and nl:object
is h th d for the bj t (range) of the relation.
i shorthand f th object ( ) f th l ti


‘Make sense’ with minimal cost!

 The nl:generation property specifies a
natural language version of the knowledge.
 allows software agents to present meaningful,
natural responses to users.

 Question: Where does Ram live?
 Reply: Ram’s home is Tellefsens gate 5.


Information Access Schemata

 Despite the simplicity of adding NL
annotations to RDF properties
 Significant restriction : only one RDF statement
can be queried at once.
 Solution: Create a schemata that captures similar
patterns of information access.


An information access schema is a
quadruple
 Annotations: NL sentences ( (either declarative or
interrogative) or phrases that describe the types of
user questions the schema can answer
answer.
 Pattern: a declarative pattern of RDF triples that
references a pre-existing ontology.
p g gy
 Action: a set of operators to further process variables
bound during the pattern matching process.
 Mapping: mechanism for handling disjunction
between lexical and ontological terms.


Example: “family” of questions

 What is the country in Asia with the largest area?
 Tell me what Asian country has the highest
population density
density.
 What country in Europe has the lowest infant
mortality rate?
y
 What is the most populated American country?


Capture the “pattern” of information
requests i an i f
in information access schema
i h
<nl:InformationAccessSchema>
Natural language
<nl:ann>what country in $region has the largest
$attribute</nl:ann> annotations are
<nl:pattern>?x a :Country</nl:pattern> employed to
<nl:pattern>?x map($attribute) ?val</nl:pattern> describe a pattern of
RDF statements
<nl:pattern>?x :location $region</nl:pattern>
<nl:action>display(boundto(?x, max(?val)))
</nl:action>

<nl:mapping> Because annotations would be
<nl:hash variable="$attribute">
variable $attribute > p
processed by linguistically
y g y
<nl:map value="population"> sophisticated systems, different
:population adjectives such as “highest” and
</nl:map> “largest” could be uniformly mapped
<nl:map value="area"> onto the maximum operation.
:area
</nl:map>
... Schema answers questions that
</nl:hash> involve region specific superlative
</nl:mapping>
pp g comparison of countries.
</nl:InformationAccessSchema>


 The pattern binds to the value of the particular attribute for
countries within the queried geographic region, and the action
ti ithi th i d hi i d th ti
specifies an aggregate operation (maximum) over the values
bound within the pattern.
 The country corresponding to that maximum value is returned as the
answer.

 The mapping provides a translation from language attributes to
pp g p g g
RDF properties.
 Information access schemata are written with respect to a particular
pre-existing ontology;
 In thi
I this example, we assume th t an appropriate ontology h b
l that i t t l has been
established (i.e., :Country is defined as a class, and :location is
defined as a property).

 In this vision of the Semantic Web, information access schemata
grounded in natural language would co-exist alongside RDF
metadata.


Further extension: Query Plan

 Question: What is the distance from India to
Norway?
 Solution Plan: To compute the distance
between their respective capitals.

Could humans “teach” such plans to a computer directly
teach ?


Information Planning Schemata

 An extension of Information Access Schemata.
 Simplifies the task of knowledge engineering.

 Example:
 Instead of writing RDF patterns,
 which would require knowledge of domain-specific ontologies,
 Use natural language itself to describe the process of
answering a question.
 The answer plan (nl:plan) reflects the user’s thought process
expressed in natural language: first find the capitals of the
countries, and then find the distance between those cities


An information planning schema
<nl:InformationPlanningSchema>

<nl:ann>distance between $country1
and $country2</ann>
<nl:plan>
<rdf:Seq>
<rdf:li>what is the capital of $country1
:= ?capital1</rdf:li>
<rdf:li>what is the capital of $country2
:= ?capital2</rdf:li>
<rdf:li>what is the distance between
?capital1 and ?capital2
:= ?distance</rdf:li>
</rdf:Seq>
</nl:plan>
<nl:action>display(?distance)</nl:action>
</nl:InformationPlanningSchema>


Integrating the methods
 The three proposed methods for integrating natural language and
RDF can be used together to afford greater flexibility.
 Annotating RDF properties is a low-cost (from a knowledge
engineering perspective) way of providing natural language access to
RDF statements.
 Information access schemata while being more complex and
schemata,
requiring knowledge of domain-specific ontologies, give experienced
knowledge engineers fine-grained tools for manipulating RDF and
controlling the output.
 Information planning schemata allow users to describe in natural
describe,
language itself, how they would go about answering a particular class
of questions.

 These three methods can combine to provide the foundation for
question answering on the Semantic Web.


Thank You !


Relationship between the Semantic Web and NLP

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