Acquiring and using world knowledge using a restricted subset of english
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Acquiring and Using World Knowledge using a Restricted Subset of English. Peter Clark, Phil Harrison, Tom Jenkins, John Thompson, Rick Wojcik Boeing Phantom Works, Seattle. Introduction. Knowledge acquisition is still a major bottleneck automated methods are good but still very restricted

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Acquiring and using world knowledge using a restricted subset of english

Acquiring and Using World Knowledgeusing a Restricted Subset of English

Peter Clark, Phil Harrison, Tom Jenkins,

John Thompson, Rick Wojcik

Boeing Phantom Works, Seattle


Introduction

Introduction

  • Knowledge acquisition is still a major bottleneck

    • automated methods are good but still very restricted

  • Our approach:

    • Knowledge entry using Controlled Language

    • Hits “sweet spot” between logic and full NLP

    • language interpreter generates logic output

  • Outline:

    • Our Controlled Language Processing technology

    • Discussion on Natural Language as a basis for KR


The language spectrum

“A ball falls from a cliff”

“xy B(x)

R(x,y)C(y)”

“Consider the following possible situation in which a ball first…”

too hard for the user

too hard for the computer

to understand

TheLanguage Spectrum

Unrestricted

natural

language

Formal

language

Controlled English


Cpl computer processable language

Short sentences

No pronouns

Rewritten in CPL (computer can understand):

An object is thrown from a cliff.

The horizontal velocity of the object is 20 m/s.

The top of the cliff is 125 m above level ground.

The object falls 125 m to the ground.

What is the duration of the fall?

Simple sentence structures

CPL (Computer-Processable Language)

Original text (incomprehensible to computer):

An object is thrown with a horizontal velocity of 20 m/s from a cliff that is 125 m above level ground. If air resistance is negligible, how long does it take the object to fall to the ground?


Target interpretation

isa(_Object1, object_n1)

isa(_Cliff2, cliff_n1)

isa(_Throw3, throw_v1)

object(_Throw3, _Object1)

origin(_Throw3, _Cliff2)

Throw

object

origin

Object

Cliff

Target Interpretation

  • Sentences in first-order logic

  • Capable of supporting machine inference

“An object is thrown from a cliff”


Target interpretation1

isa(_Person1, person_n1)

isa(_Room2, room_n1)

isa(_Entity3, entity_n1)

isa(_Carry4, carry_v1)

object(_Carry4, _Entity3)

agent(_Carry4, _Person1)

is-inside(_Entity4, _Room2)

=====>

is-inside(_Person1, _Room2)

Carry

agent

object

Person

Object

is-inside

is-inside

Room

Target Interpretation

  • Sentences in first-order logic

  • Capable of supporting machine inference

IF

“a person is carrying an entity that is inside a room”

THEN

“the person is in the room.”


Overview of processing

Throw

object

origin

Object

Cliff

Overview of Processing

“An object is thrown from a cliff”

Parser & LF Generator

Word sense disambiguator

Linguistic

Knowledge

Relational disambiguator

Coreference identifier

World

Knowledge

Structural reorganizer

(_Object13320 instance_of object_n1)

(_Cliff13321 instance_of cliff_n1)

(_Throw13319 instance_of throw_v1)

(_Throw13319 object _Object13320)

(_Throw13319 origin _Cliff13321)


Entering quantified expressions rules

Entering Quantified Expressions (Rules)

  • Seven “rule templates” used:

IFsentence THENsentence

ABOUTobject: sentence

object ISnoun/verb phrase

BEFOREsentence, sentence

BEFOREsentence, it is not true thatsentence

AFTERsentence, sentence

AFTERsentence, it is not true thatsentence

Processing:

  • Each sentence processed as a ground assertion

  • Quantifiers are added (Prolog-style)

  • “Action” templates become situation calculus rules


Overall flow of processing

CPL (Controlled english)

An object is thrown from a cliff.

The horizontal velocity of the object is 20 m/s. The top of the cliff is 125 m above level ground.

Rewriting

advice

Logic

An object is thrown from a cliff.

The horizontal velocity of the object is 20 m/s. The top of the cliff is 125 m above level ground.

Paraphrase of

system’s understanding

KB

Overall Flow of Processing

Original

text


Part ii discussion controlled languages strengths and challenges

Part II: DiscussionControlled Languages:Strengths and challenges


Strengths

Strengths…

xy B(x)

R(x,y)C(y)???

“A man is driving a truck towards the factory”

  • CPL is easy to use, appears viable

    • built KB with over 1000 rules

    • KB is

      • inference-capable

      • easy to inspect and organize

  • Makes knowledge entry accessible to many users

    • major achievement


Challenges 1 reformulating in a controlled language is not trivial

Original text:

“attack: intense adverse criticism”

CPL:

“IF a person attacks a 2nd person

THEN the first person criticizes the 2nd person intensely.”

Challenges: 1. Reformulating in a Controlled Language is not trivial

  • Task is not just grammatical reformulation

  • Rather:

    • “natural” English leaves much knowledge implicit

    • CPL author must make that explicit


Challenges 1 reformulating in a controlled language is not trivial1

Original text:

“axis: the center around which something rotates”

CPL:

“IF an object is rotating

THEN the object is turning around the object’s axis.”

Challenges: 1. Reformulating in a Controlled Language is not trivial

  • Task is not just grammatical reformulation

  • Rather:

    • “natural” English leaves much knowledge implicit

    • CPL author must make that explicit


2 users may not be aware of system s mistakes

“The man ate the sandwich on the plate”

“The man ate on the plate. He ate the sandwich.”

??????

2. Users may not be aware of system’s mistakes

  • User must be able to spot misinterpretations easily

    • System’s paraphrase must be unambiguous

  • User must know how to correct them


2 users may not be aware of their mistakes

“The man ate the sandwich on the plate”

“The man ate on the plate. He ate the sandwich.”

“the man ate the sandwich that was on the plate”

2. Users may not be aware of their mistakes

  • User must be able to spot errors easily

    • System’s paraphrase must be unambiguous

  • User must know how to correct them


3 natural language based knowledge representations have limited expressivity

3. Natural-Language-based knowledge representations have limited expressivity

“Natural language is very expressive”

  • …not to the computer! (Avoid “wishful semantics”)

  • Expressiveness =

    • the amount the computer understands

    • the amount it is able to use to draw conclusions from

  • Everything else is meaningless to the computer

  • e.g., CPL can’t express:

    • constraints, defaults, some quantification patterns


4 sometimes linguistically motivated representations are poor

NL-based

KR

“Traditional”

KR

distance(_Walk1, _Mile1)

count(_Mile1, 10)

distance(_Walk1, _Distance1)

value(_Distance1, 10, mile)

4. Sometimes, linguistically motivated representations are poor

  • Language-based KR:

    • Most concepts correspond to words

    • Structure of KB will mirror structure of language

  • Is this bad? Sometimes…

“… walked for 10 miles”


5 lack of canonicalization

5. (Lack of) Canonicalization

“conducting a test of an entity”

“testing an entity”

  • Many ways to say the same thing

  • System needs to realize the equivalence

    BUT: often NL-based KRs will not 

    Solutions:

  • Add equivalence rules. (But there are lots!!)

    • e.g., “Conducting a X of Y ↔ Xing a Y”

  • Have the interpreter normalize the input.

  • Restrict the input language.


Summary

Summary

  • CPL = a restricted English language for knowledge

    • Hits “sweet spot” between logic and full NLP

    • Produces inference-capable representations

    • Is viable, used to build a large KB

  • But: No “free lunch”

    • requires skill to use it effectively

  • NL-based KRs are becoming more important!

    • Web: need semantically meaningful annotations

    • AI: need better knowledge acquisition tools

  • Some exciting possibilities ahead (esp. at Boeing!)


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