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builderanthologyAI and Robotics

Oct 19, 2013 (3 years and 7 months ago)

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Ontology
-
Driven

Information Retrieval

Nicola Guarino


Laboratory for Applied Ontology

Institute for Cognitive Sciences and Technology (ISTC
-
CNR)

Trento
-
Roma, Italy

ICDL 2004, New Dehli, India


Summary


The role of ontologies in (generalized) IR


Semantic Matching


Increasing precision


Increasing recall


Problems with WordNet


The role of Foundational Ontologies


The key problem

Semantic matching

Simple queries: need more knowledge about what
the user wants



Search for “Washington” (the person)


Google: 26,000,000 hits


45th entry is the first relevant


Noise: places



Search for “George Washington”


Google: 2,200,00 hits


3rd entry is relevant


Noise: institutions, other people, places

Solution: ontology+semantic markup


Ontology


Person


George Washington


George Washington Carver


Place


Washington, D.C.


Artifact


George Washington Bridge


Organization


George Washington University



Semantic disambiguation/markup of questions and corpora


What Washington are you talking about?

The role of taxonomy and lexical knowledge


Search for “Artificial Intelligence Research”



Misses subfields of the general field


Misses references to “AI” and “Machine Intelligence”
(synonyms)


Noise: non
-
research pages, other fields…

Solution:


Extra knowledge


Ontology: Sub
-
fields (of AI)


Knowledge Representation


Machine Vision etc.


Neural networks


Lexicon: Synonyms (for

AI)


Artificial Intelligence


Machine Intelligence


Techniques


Query Expansion


Add disjuncted sub
-
fields to search


Add disjuncted synonyms to search


Semantic Markup of question and corpora


Add “general terms” (categories)


Add “synonyms”

Ontology
-
driven search engines


Idealized view


Ontology
-
driven search engines act as
virtual librarians


Determine what you “really mean”


Discover relevant sources


Find what you “really want”



Requires common knowledge on all ends


Semantic linkage between questioning agent, answering agent
and knowledge sources



Hence the “Semantic Web”?

Two main roles of ontologies in IT


Semantic Interoperability


Generalized database integration


Virtual Enterprises, Concurrent Engineering


e
-
commerce


Web services



Information Retrieval


Documents


Facts (query answering)


Products


Services


[Not mentioning IS analysis and design…]

The role of linguistic ontologies

coupled with structured representation
formalisms


Why not just simple thesauri based on fixed keyword hierarchies?


Data’s intrinsic dynamics needs to keep track of new terms


Need of understanding a rigid set of terms


Heterogeneous descriptions need a broad
-
coverage vocabulary



Linguistic ontologies
with

structured representation formalisms


Decouple user vocabulary from data vocabulary


Increase recall (synonyms, generalizations)


Increase precision (disambiguation, ontology navigation)


Further increase precision (by capturing the structure of queries and
data)

Using WordNet as an ontology



Unclear semantic interpretation of hyperonimy



Instantiation vs. subsumption



Object
-
level vs. meta
-
level



Hyperonymy used to account for polysemy


(law both a document and a rule)



Unclear taxonomic structure



Glosses not consistent with taxonomic structure



Heterogeneous leves of generality



Formal constraints violations (especially concerning roles)



Polysemous use of antonymy (child/parent vs. daughter/son)



Poor ontology of adjectives and qualities



Shallow taxonomy of verbs

When subtle distinctions are
important


“Trying to engage with too many partners too
fast is one of the main reasons that so many
online market makers have foundered. The
transactions they had viewed as simple and
routine actually involved many
subtle
distinctions in terminology and meaning



Harvard Business Review, October 2001

Ontologies and

intended meaning

Models
M
D
(L)

Language L

Commitment:

K = < C,
I
>

Conceptualization


Ontology

Intended
models
I
K
(L)

Interpretation

Levels of Ontological Precision

Ontological precision


Axiomatized
theory

Glossary

Thesaurus

Taxonomy

DB/OO
scheme

tennis

football

game

field game

court game

athletic game

outdoor game

Catalog

game


athletic game


court game


tennis


outdoor game


field game


football

game

NT athletic game


NT court game


RT court


NT tennis


RT double fault

game(x)


activity(x)

athletic game(x)


game(x)

court game(x)


athletic game(x)



y. played_in(x,y)


court(y)

tennis(x)


court game(x)

double fault(x)


fault(x)



y. part_of(x,y)


tennis(y)

Ontology Quality:

Precision and Coverage

Low precision, max coverage

Less good

Good

High precision, max coverage


BAD

Max precision, low coverage

WORSE

Low precision and coverage

I
A
(L)

M
D
(L)

I
B
(L)

Why precision is important

False agreement!

DOLCE

a Descriptive Ontology for Linguistic and
Cognitive Engineering


Strong cognitive bias
:
descriptive

(as
opposite to
prescriptive
) attitude


Emphasis on
cognitive invariants


Categories as
conceptual containers
: no
“deep” metaphysical implications wrt
“true” reality


Clear
branching points

to allow easy
comparison with different ontological
options


Rich axiomatization

DOLCE’s basic taxonomy

Endurant


Physical



Amount of matter



Physical object



Feature


Non
-
Physical



Mental object



Social object




Perdurant


Static



State



Process


Dynamic



Achievement



Accomplishment


Quality


Physical



Spatial location






Temporal



Temporal location






Abstract

Abstract


Quality region



Time region



Space region



Color region









Foundational ontologies and ontological
analysis


Domain ontologies


Physical objects


Information and information processing


Social interaction


Ontology of legal and financial entities


Ontology, language, cognition


Ontology
-
driven information systems


Ontology
-
driven conceptual modeling


Ontology
-
driven information access


Ontology
-
driven information integration


Research priorities at LOA
-
CNR

www.loa
-
cnr.it