O NTO PED IA
The Identity of Everything
Computing and Linguistics
A Cognitive Approach
or, Computing “As We May Think”
Steve Pepper
[email protected]
University of Oslo, 2009-04-21
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O NTO PE D IA
The Identity of Everything
Today’s “research questions”

How can linguistics – and in particular cognitive
linguistics – inform our work with Topic Maps?

Can Topic Maps contribute in any way to the
cognitive linguistics project?

Plan of action
–
I tell you about Topic Maps (conceptual model)
–
I draw some parallels with natural language
–
You correct me, elaborate and suggest new directions
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Relevance to you as linguists

As users of the technology
–

As consultants to users of the technology
–

e.g. universities, government agencies, private enterprise
As contributors to the standard
–

organizing data collected in your research
clarify some of the cognitive issues, establish best
practices, help extend the standard
As lobbyists to the University of Oslo
–
if you think the new UiO web site should be based on
Topic Maps, please make your views known to the project
group: http://www.admin.uio.no/prosjekter/nyuioweb/
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Relevance in general
1. We need to organize information in a new way
–
The summation of human experience is being
expanded at a prodigious rate, and the means we
use for threading through the consequent maze
to the momentarily important item is the same
as was used in the days of square-rigged ships.
(Vannevar Bush, As We May Think, 1945)
2. We need new ways of managing knowledge
–

In today’s global knowledge economy, knowledge is
the key asset in many organizations...
Topic Maps makes major contributions in both areas
–
See the use cases presented at recent Topic Maps conferences
http://www.topicmaps.com
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What is Topic Maps?

An ISO standard for computer-based information
and knowledge management
–


“Provides the ability to control infoglut and share knowledge
by connecting any kind of information from any kind of source
based on its meaning”
A “semantic technology”
–
Cf. Semantic Web (RDF, OWL)
–
A form of knowledge representation (primitive perhaps, but useful)
Widely used for web-based delivery of information
–
Plus: Information Integration, eLearning, Business Process
Modeling, Product Configuration, Business Rules Management,
Asset Management, Knowledge Management, …
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The problem with computing...

...is that it’s inside-out!

People used to think the sun
revolved around the earth
–

Copernicus’ heliocentric theory
turned this idea inside out and
revolutionized our understanding
of the universe
Today we face a similar
situation in computing
–
–
Our computing universe has
computers, applications and
documents at the centre
The concepts that our information is
about are somewhere in outer space
where they can’t be found
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A subject-centric revolution

This is wrong, because it does
not reflect how humans think
–
–
–

We think in terms of interrelated
concepts (or subjects)
Subjects are what interest us, not
documents or applications
And so subjects must be given
centre stage
We need a subject-centric
revolution
–
This has ramifications for every
aspect of human-computer
interaction, including user interfaces,
operating systems, file systems, etc.
–
Consider the typical user desktop...
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Today our
desktops are
applicationcentric and
documentcentric
Icons represent
applications and
documents
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Why can’t they be subject-centric, with icons that
represent the subjects we are interested in?
 With links between related icons?
 And with context menus that allow us to find
everything related to a particular subject?

The Identity of Everything
TM2008
Topic page
Emails
Documents
Web topic
pagesmaps
K185
opera
OOXML
tm2008
keynote
bayreuth
håkon
LING 2110
Ψ Copy PSI
rana
janacek
gambia
bantu
semantics
INF 2820
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Computing “As We May Think”

Bush’s solution to information overload:
–



Organize information “As We May Think”, i.e. associatively
His vision spawned the hypertext movement
–
Doug Engelbart, Ted Nelson, Bill Atkinson, Tim Berners-Lee, ...
–
The World Wide Web is its greatest triumph to date
But hypertext does not correspond to how we think
–
Our heads are not full of millions of interlinked documents
–
They are full of “interlinked” concepts (or subjects)
Topic Maps provides a close approximation to this
–
It is a technology that is based on cognitive principles
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Background to Topic Maps



Emerged from the SGML community in 1990’s
–
Use case: How to merge (digital) back-of-book indexes
–
Some input from library science
–
No input from linguists
–
Precious little input from computer scientists before 2001
–
Most of the SGML community came from the humanities
ISO 13250 first published in 2000 (recently revised)
–
A model for representing knowledge organization structures
(indexes, glossaries, thesauri, encyclopedias)
–
Plus interchange syntax, query language, constraint language, ...
Widely adopted in Norway (esp. public sector)
–
And gaining ground elsewhere
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The TAO of Topic Maps

The core concepts are derived
from the back-of-book index

Extended and generalized for
use with digital information

Consider a two-layer model
consisting of

–
a set of information resources (below)
–
a “knowledge map” (above)
This is like the division of a
book into content and index
Callas, Maria …………………… 42
Cavalleria Rusticana … 71, 203-204
Mascagni, Pietro
Cavalleria Rusticana . 71, 203-204
Pavarotti, Luciano ……………… 45
Puccini, Giacomo ………. 23, 26-31
Tosca ………………. 65, 201-202
Rustic Chivalry, see Cavalleria
Rusticana
singers ………………………. 39-52
baritone ………………………. 46
bass ……………………….. 46-47
soprano ……………… 41-42, 337
tenor ………………………. 44-45
see also Callas, Pavarotti
Tosca ………………… 65, 201-202
(INDEX)
knowledge layer
information layer
(CONTENT)
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(1) The information layer


The lower layer contains the content
–
usually digital, but need not be
–
can be in any format or notation or location
–
can be text, graphics, video, audio – whatever
This is like the content of the book to which the
back-of-book index belongs
(CONTENT)
information layer
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(2) The knowledge layer

The upper layer consists of (typed) topics and associations
–
Topics represent the subjects that the information is about

–
Like the list of topics that forms a back-of-book index
Associations represent relationships between those subjects

Like “see also” relationships in a back-of-book index
composed by
Domain:
Italian opera
composed by
Tosca
Puccini
(INDEX)
born in
Lucca
Madame
Butterfly
knowledge layer
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Occurrences link the layers



Occurrences represent
relationships between
information resources and
the subjects that they are
“about”
The links (or locators) are
like page numbers in a
back-of-book index
Occurrences can
also be typed (e.g.
bio, map, synopsis)
composed by
composed by
Tosca
Puccini
born in
Lucca
Madame
Butterfly
knowledge layer
information layer
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Summary of core concepts
Let’s look at some TAOs
in the Omnigator…
A pool of information or data, and
a knowledge layer consisting of
Topics
composed by
– a set of topics representing the key
subjects of the domain in question
composed by
Associations
– representing relationships between
subjects
Tosca
Puccini
born in
Occurrences
– links to information that is somehow
relevant to a given subject
Lucca
Madame
Butterfly
knowledge
information
= The TAO of Topic Maps
Plus: topic types, association types,
occurrence types – each of which
are represented by topics...
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About the Omnigator




A free topic map browser from Ontopia
–
Download from http://www.ontopia.net (part of “OKS Samplers”)
–
Java-based, runs on any computer
Completely generic
–
Not optimized for any particular ontology
–
Display and navigate any conforming topic map
A teaching aid
–
Not designed for end-users (no attempt to hide technical jargon)
–
Also used for prototyping and debugging
Not to be used for most real world applications!
–
These require custom interfaces based on a specific ontology
–
(see http://www.topicmaps.com for a good example)
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a typical topic page
Omnigator interface
topic type(s)
current topic
identifier(s)
multiple (typed) names
typed
associations
Demo
typed
occurrences
(internal and
external)
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Typing topics revisited



Basic building blocks of the TAO model are
–
Topics: e.g. “Puccini”, “Lucca”, “Tosca”
–
Associations: e.g. “Puccini was born in Lucca”
–
Occurrences: e.g. “http://www.opera.net/puccini/bio.html
is a biography of Puccini”
Each of these constructs can be typed
–
Topic types: “composer”, “city”, “opera”
–
Association types: “born in”, “composed by”
–
Occurrence types: “biography”, “street map”, “synopsis”
All such types are also topics
–
The set of typing topics constitutes an ontology
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Capabilities of the TAO model (1)

Represent subjects explicitly
–


Topics represent the “things” users are interested in
Capture relationships between subjects
–
Associations provide user-friendly navigation paths to information
(navigation “as we may think”)
–
Associations also promote serendipitous knowledge discovery
through browsing
Make information findable
–
Topics provide a “one-stop-shop” for everything that is known
about a subject (collocation of information and knowledge)
–
Occurrences allow information about a common subject to be
aggregated across multiple systems, irrespective of location
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Capabilities of the TAO model (2)


Represent taxonomies and thesauri
–
Associations can (also) represent hierarchical relationships
–
With Topic Maps you can have multiple, interlinked hierarchies
and faceted classification
Transcend simple hierarchies
–

Rich associative structures capture the complexity of
knowledge and reflect the way people think
Manage knowledge
–
The topic map is the embodiment of “organizational memory”
–
Provides a structured way to capture people’s knowledge of
things, events, relationships, etc.
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Beyond the TAO

Formal data model
–
–

–
–
Topic maps can be merged
Potential to federate knowledge
Scope
–

Topic maps can be interchanged
Increased reuse = added value
Robust identity model
–

Topic maps can be queried, e.g.
Give me all composers that composed operas that were
based on plays that were written by Shakespeare
Interchange syntax
–

For more details,
see Pepper 2009
Topic maps can capture context
Reification
–
–
Topic maps can express different levels of detail
Similar to scaling in cartography
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Break – any questions so far?
After the break:
Topic Maps and natural
language – towards a
linguistic perspective
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Parallels with natural language












Basic grammatical classes
Nouns and verbs
Nominals and nouns
Clauses and verbs
Valency
Semantic roles
Categories and schemas
Hyponymy
Synonymy and homonymy
Nominalization
Grounding / co-reference
Information structure












TAO model
Topics and associations
Topics and their types
Associations and their types
Arity
Association roles
Typing topics
Type hierarchies
Naming
Reification
Subject identity / collocation
Navigation
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Basic principles, basic classes
 In elementary school, I was taught that a noun is the name of a person,
place, or thing. In college, I was taught the basic linguistic doctrine that a
noun can only be defined in terms of grammatical behavior, conceptual
definitions of grammatical classes being impossible. Here, several
decades later, I demonstrate the inexorable progress of grammatical
theory by claiming that a noun is the name of a thing.
(Langacker 2008)

The basic grammatical classes are nouns and verbs
–

They prototypically profile things and relationships
They correspond to topics and associations
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Grounding
Langacker 2008: 259ff (esp. 264)
 “Grounding is characteristic of the structure referred to in CG
as nominals and finite clauses. More specifically, a nominal
or a finite clause profiles a grounded instance of a thing or
process type.”
 “A noun designates a type of thing, and a verb a type of
process.”
 “A nominal or a finite clause profiles a grounded instance of
a thing or process type.”
 Nominal grounding (determiners and quantifiers)
– the, this, that, some, a, each, every, no, any
 Clausal grounding (mood and tense)
– -s, -ed, may, will, should
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Nouns and nominals

Topic types represent classes of topics
–
Conceptual “groupings of things”, e.g. composer, opera, city, ...
–
They correspond to Langacker’s nouns (“types of thing”)
However, topics can have multiple names
–
–

(This is how we handle synonymy and multilingualism)
In one sense it is topic names that correspond to nouns
Topic instances represent individual subjects
–
They correspond to Langacker’s nominals (“instances of types”)
–
Their names are typically proper nouns, e.g. Puccini, Tosca, Lucca
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Verbs and clauses

Association types represent classes of relationships
–
–

They correspond to Langacker’s verbs (“types of process”)
(Often named accordingly, e.g. born in, composed by, killed by, ...)
Individual associations represent specific relationships
–
–
They correspond to Langacker’s clauses (“instances of processes”)
e.g. Puccini was born in Lucca; Tosca was composed by Puccini
Langacker distinguishes processes (temporal) and non-processual
relationships (non-temporal). The latter are (prototypically) profiled
by adjectives, adverbs, prepositions, and participles. This distinction
is not made explicitly in Topic Maps.

Note: There are two predefined association types
–
–
type-instance (the relationship between a topic and its type)
supertype-subtype (a relationship between types, see Hyponymy)
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Valency


Associations can involve one, two or more topics
–
Binary associations, e.g. Puccini composed Tosca, are most
common and correspond to transitive verbs
–
Ternary associations, e.g. Tosca killed Scarpia with a knife, can
correspond to ditransitive verbs
–
Unary associations, e.g. Turandot was unfinished, correspond
(sort of) to intransitive verbs (or binary properties)
The arity of an association
–
Corresponds to the valency of a verb
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Semantic roles

An association does not have “directionality”
Puccini
composed
composed by
composer


Tosca
Topic
RDF Maps
work
Instead of direction, Topic Maps uses roles
–
Roles are classified by type
–
Role types specify the nature of each topic’s involvement
in the relationship. They correspond to semantic roles.
–
(Role types are also topics)
Role types are different from topic types...
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Roles and types
composer
opera
T
T
T
composer
composed
work
T
T
T
R
A
R
Puccini
T
Tosca
The role type can be
–
–
–
–
the same as the role playing topic’s topic type (composer = composer)
a supertype of the topic type (work > opera)
a subtype of the topic type (teacher < person)
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Association roles


Italian Opera Topic Map
Semantic roles

(Frawley 1992)
–
composed: composer, work
–
born in: person, place
–
appears in: character, work
–
based on: source, result
patient, experiencer,
–
revision of: source, result
benefactive
–
part of: part, whole
–
exponent of: person, style
–
located in: container, containee
–
pupil of: teacher, pupil
–
(logical actors)
agent, author, instrument
–
–
(logical recipients)
(spatial roles)
theme, source, goal
–
(non-participant roles)
locative, reason, purpose
Association roles tend to be much more specific
–
Variable practice – as yet no established conventions
–
Might (cognitive) linguists have something to offer here?
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Naming of associations

Intuitive naming requires
flexibility
–

–

Puccini was born in Lucca
Lucca was the birthplace of Puccini
Active / passive forms of the verb

i.e. multiple AT names that change
depending on the “direction” of the
association

Voice-based
–

Works well in SVO languages.
Less satisfactory with SOV.
Role-based


Alternative CG view
–
Naming should be based on whether
the agent or the theme is in focus

–


–
Point of focus = Current topic
Some strategies...

teacherN of/pupilN of
Nominalization

The focus becomes the trajector
composedVa / composedVp by
composition
Tends to be used by Japanese,
Koreans (and Germans??)
Combinations


bornV in / birthplaceN of
partN of/consistsV of
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Categories and prototypes

Topic types define categories of things
–


But are they Aristotelian or prototypical categories?
Aristotelian
–
Category membership is binary
–
All instances are equally representative. No standard notion of “similarity”.
Prototypical
–
Not defined by “necessary and sufficient conditions” (cf. OWL)

–
A topic can have more than one type

–
Boïto is a composer and a librettist
The same topic can be a topic type and a role type

–
The decision is up to the conceptualizer (a.k.a. topic map author)
e.g. Puccini is a composer; Puccini plays the role of composer in …
Should we establish conventions for goodness of example?

Could be useful in automated classification
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Schemas and constraints

Other types can also be said to define categories
–

But these are more schematic (in the CG sense)
–

association types, (occurrence types, name types, role types)
Schemas are “abstract templates obtained by reinforcing the
commonality inherent in a set of instances”
(Langacker 2008, p.23, in the context of grammatical rules)
Rules can be defined as templates and constraints:
opera
composer
T
T
AGENT
Puccini
composer
composed
work
T
T
T
R
A
R
elaboration sites
T
T
THEME
Tosca
“Puccini composed Tosca”
The composer Puccini plays
the role of composer in the
“composition” relationship in
which the role of work is
played by the opera Tosca.
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Hyponymy

Topic Maps has two predefined association types:
–
type-instance (relationship between a topic and its type)
–
supertype-subtype (relationship between the denotations of
a hyponym and its hyperonym)
LEGEND
Mammal
supertype-subtype
Primate
Canine
types
Chimp
Dog
Human
Wolf
type-instance
instances
Steve
Ron
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Synonymy and homonymy


Synonyms

Homonyms
–
One subject, multiple names
–
One name, multiple subjects
–
In thesauri: USE and USED FOR
–
In thesauri: problematic
TMs are subject-centric
–
A topic can have multiple names
–
Names can be typed
–
–

Typical name types:
–
nickname, synonym, alternate name
Context can be expressed using scope

TMs are based on identifiers
–
Same name can be used by more than
one topic
–
Disambiguation in UI is left to the
application
–
Two main disambiguation strategies

Typically names in different natural languages

Default: qualify by type, e.g.
–
composer, komponist, 작곡가, ...
–
Tosca (opera) vs. Tosca (character)
Names can also have “variants”

Fallback: qualify by some other
relationship, e.g.
–
Paris (France) vs. Paris (Texas)
–
La Bohème (Puccini) vs. La Bohème
(Leoncavallo)

Often used to capture orthographic variation:
–
Tchaikovsky, Чайко́вский, Tsjajkovskij,
Tschaikowski

Also useful for sort names, pronunciation, etc.
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Nominalization



Derivation of nouns from other words,
including verbs, adjectives etc.
e.g. meetV  meetingN
(A topic map consists of assertions about subjects)
Assertions are made using statements:
–
names, e.g. a certain subject has the name “Tosca”
–
associations, e.g. “Tosca is set in Rome”
–
occurrences, e.g. “http://en.wikipedia.org/wiki/Rome is a web
page about Rome”
Any statement can be reified
–
Reification results in a topic that has the same referent as the
reified statement
–
e.g. Tosca is set in RomeA  The setting of Tosca in RomeT
–
The (new) reifying topic can have names and occurrences,
and it can play roles in associations
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Subjects and topics


Topics represent subjects
–
the topic is the representation
–
the subject is the referent
A subject in
the real world
Or, in Saussure’s terms
–
signifiant and signifié

A subject can be anything:
A subject is any “thing” whatsoever,
whether or not it exists or has any other
specific characteristics, about which
anything whatsoever may be asserted
by any means whatsoever.
T

Is the topic/subject pairing a
symbolic assembly?
A topic in the
computer domain
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Co-reference and collocation



Grounding singles out referents and enables co-reference
–
between speaker and listener
–
across a sequence of utterances
In Topic Maps the central objective is collocation
–
By definition, each topic represents a single subject (one subject per topic)
–
A topic is intended to be a point of collocation for everything that is known
about a particular subject
–
Therefore the goal is to have only one topic per subject
To achieve that we need to know which subject a topic
represents
–
(This is sometimes referred to as the “intentionality” of the relation
between a symbol and its referent.
–
We call it subject identity.
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Subject identity
SUBJECTS
TOPICS
Tosca
Madame
Butterfly
Puccini
Lucca

The identity of a subject is expressed using globally
unique identifiers called subject identifiers
–
If two topics share a subject identifier, they are deemed to
represent the same subject and must be merged
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The subject is identified by a URL
Subject identifiers
topic
• The URL is called a
subject identifier
subject identifier
subject
http://psi.ontopedia.net/Giacomo_Puccini
Giacomo
Puccini
The URL is the address
of a web page
• The web page describes
the subject such that a
human can know what
subject is referred to
• This web page is called a
subject descriptor
http://psi.ontopedia.net/Giacomo_Puccini
Is the subject identifier/
subject descriptor pairing
a symbolic assembly?
Machines use the identifier
The link is not resolved.
Instead simple lexical
comparison is used. If the
strings are identical, the
subject is deemed to be the
same and the topics are
merged.
Giacomo Puccini
Italian composer, b.
Lucca 22nd Dec 1858,
d. Brussels, 29th Nov
1924. Best known for
his operas, of which
Tosca is one of the
most popular
and well-known.
subject descriptor
Humans use the descriptor
By inspecting the web page the person
responsible for assigning the identifier can
be sure that it does not refer to, say,
Giacomo’s grandfather Domenico (who
was also a composer of operas)
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Information structure

Intuitive navigation is a key feature of Topic Maps

But what is its cognitive basis?


–
I claim that it corresponds to the way we think (i.e., associatively)
–
Can linguistics back up this claim?
topic vs. comment in linguistics (Bussmann, 487)
–
“Analysis of sentences according to communicative criteria into the
topic (what is being talked about) and the comment (what is being
said about the topic)”
–
“Analysis of utterances according to the communicative criteria of
given/known information vs. new information”
–
Cf. theme vs. rheme in Halliday’s functional grammar
Consider our earlier tour of Italian opera...
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Navigation as narrative
Giacomo Puccini was a composer. He was born in Lucca in 1858.
Lucca is a city, located in Italy. It was the birthplace of
Puccini and […] Catalani.
Catalani was a composer who composed 5 operas. He died in Milan.
Milan is the home of La Scala, which was the venue for many
premiére performances, including that of Madam Butterfly.
Madam Butterfly is set in Nagasaki, which is located in Japan.
Japan is (also) the setting for Iris, [which is] an opera [which
was] composed by Mascagni, who was a pupil of Ponchielli who was
(also) the teacher of Puccini...
THEME:
RHEME:
new theme
continuing theme
predicate with potential new theme
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Discussion

Questions, comments, corrections?
–


What have I missed? Where else should I look?
What might linguists contribute?
–
A better understanding of the nature of roles?
–
Approaches to representing temporal knowledge?
–
...
Can Topic Maps inform linguistics?
–
After all, it is a technology that captures (some
degree of) (some form of) knowledge
–
It seems to have a reasonable cognitive basis
–
It emerged through usage (librarians, indexers, etc.)
–
And last but not least, it works!
“Now! …. That should
clear up a few things
around here!”
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References

Bussman, H. Routledge Dictionary of Language and Linguistics
(London 1996)

Frawley, W. Linguistic Semantics (Hillsdale 1992)

Langacker, R. Cognitive Grammar (Oxford 2008)

Pepper, S. Italian Opera Topic Map
–

http://www.ontopedia.net/ItalianOpera
Pepper, S. “Topic Maps” in Bates, M.J. and Maack, M.N. (eds)
Encyclopedia of Library and Information Sciences (CRC Press,
forthcoming 2009)
–
http://www.ontopedia.net/pepper/papers/ELIS-TopicMaps.pdf
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Questions
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Notes
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Understanding Topic Maps